CN118863477A - Resource utilization analysis system and method using three-dimensional modeling visualization technology - Google Patents

Abstract

The invention discloses a resource movement analysis system and a method using a three-dimensional modeling visualization technology, which relate to the technical field of mineral resource movement analysis and specifically comprise the following steps: analyzing the obtained ore utilization information of each layer of the ore region to be mined, predicting whether the resource utilization conditions among all layers of the ore region to be mined are balanced, and dividing all layers of the ore region to be mined into a high optimization region, a medium optimization region and a low optimization region according to the prediction result; constructing a resource movement amount adjustment mechanism according to the division result of each layer of the ore region to be mined; in the process of adjusting each layer by the resource action amount adjusting mechanism, whether the adjusting effect of the adjusting mechanism on each layer can reach the expected or not is evaluated, and the adjusting mechanism is optimized according to the evaluation result. The invention solves the problem of unbalanced utilization of resources in the multi-level open-air mining area and realizes real-time optimization and balanced adjustment of the mining process.

Description

Resource utilization analysis system and method using three-dimensional modeling visualization technology

Technical Field

The present invention relates to the technical field of mineral resource utilization analysis, and in particular to a resource utilization analysis system and method using three-dimensional modeling visualization technology.

Background Art

In the process of mineral resource development, resource mobilization refers to the total amount of resources actually used and extracted in the process of mineral resource development. It is an important indicator for evaluating the benefits of mining and resource utilization efficiency. In mineral resource development activities, accurate analysis of resource mobilization helps to timely discover illegal mining activities, optimize resource mining plans, and improve the rational utilization rate of mineral resources. With the development of science and technology, the use of three-dimensional modeling and visualization technology for resource mobilization analysis has significant advantages. First of all, three-dimensional modeling technology can accurately construct a three-dimensional model of the mine through unmanned aerial vehicle oblique photography, radar data processing and other means, and truly reflect the changes in surface objects in the mining area. Compared with traditional two-dimensional monitoring methods, three-dimensional visualization models are more intuitive and accurate, and can clearly present information such as the scope of mine development, mining volume, and geological structure. In addition, the use of this technology can break through the limitations of traditional remote sensing images, make up for the shortcomings of orthophotos in detail presentation, and quickly obtain high-precision terrain data and mining information, thereby improving the scientificity and rationality of mineral resource development.

The existing resource utilization analysis technology using 3D modeling visualization technology is mainly realized through unmanned aerial vehicle oblique photography, radar data processing and optical remote sensing. First, unmanned aerial vehicle oblique photography technology can collect image data of the mining area from different angles (vertical and oblique) to generate high-precision 3D models. These models can truly reflect the changes in the mine surface, such as the mining range and mining depth of the mine. Next, through the import, clipping and splicing of radar data, combined with precise orbital data, the temporal change analysis of the mining area is carried out to further understand the impact of underground mining activities on the surface. In terms of data processing, the system pre-processes the collected images and radar data, such as orthorectification, radiation correction, image fusion, etc., to ensure the accuracy and authenticity of the image data. Then, based on these processed high-resolution images, combined with DEM (digital elevation model) data, a 3D topographic map and surface model of the mining area are constructed, and then the resource utilization is accurately calculated through the point cloud data in the model. This analysis process can intuitively display the changes in mining volume, mining location and geological structure, and evaluate the development progress of mineral resources and illegal mining through multi-period data comparison. In addition, the dynamic characteristics of 3D modeling allow law enforcement officers to monitor changes in mines in real time through virtual means, especially in areas that are difficult to observe directly, providing strong technical support for the rational development and supervision of mineral resources.

The prior art has the following deficiencies:

In the process of multi-level open-pit mining, due to the different terrain and geological conditions of each layer, the ore is mined in layers, resulting in differences in the mining progress, resource mobilization and mining stability of each layer. In this case, the complexity of the mining environment makes it difficult for traditional 3D modeling technology to synchronously analyze the resource mobilization of each layer. Due to the lack of a hierarchical real-time evaluation mechanism, existing technologies are unable to capture and process the mining progress of each layer in a timely manner, especially when there are significant differences in geological structures and mining conditions at different levels. This will cause a lag in the analysis of resource mobilization, and it will be impossible to accurately distinguish the changes in the mobilization of each layer, which will lead to insufficient optimization of resource mobilization, which may cause resource waste or over-mining. As the deviation of mining progress accumulates, the stability of the ore layer is difficult to accurately assess, which in turn increases safety hazards such as mine collapse and equipment misoperation, affecting the mining efficiency and safety of the mining area.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art.

Summary of the invention

The purpose of the present invention is to provide a resource utilization analysis system and method using three-dimensional modeling visualization technology to solve the problems in the above-mentioned background technology.

In order to achieve the above object, the present invention provides the following technical solution: a resource utilization analysis method using three-dimensional modeling visualization technology, specifically comprising the following steps:

In the mining process of multi-level open-pit mines, the ore area to be mined is evenly divided into several levels, and a sensor network is deployed around each level of the ore area to be mined to obtain the ore utilization information of each layer in the ore area to be mined in real time;

Analyze the ore utilization information of each layer of the ore area to be mined, predict whether the resource utilization of each layer in the ore area to be mined is balanced, and divide each layer of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the prediction results;

According to the division results of various levels of the ore area to be mined, a resource utilization adjustment mechanism is established to carry out different mining equipment scheduling, mining sequence adjustment and resource allocation optimization for the high optimization area, medium optimization area and low optimization area respectively;

In the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time, and the information is analyzed to evaluate whether the adjustment effect of the adjustment mechanism at each level can meet the expectations, and the adjustment mechanism is optimized according to the evaluation results;

Conduct real-time monitoring and comprehensive analysis of resource mobilization data, adjustment records and mining results throughout the entire mining process, continuously improve mining strategies and resource allocation plans, and optimize adjustment mechanisms in real time.

Preferably, the ore utilization information of each layer of the ore area to be mined is analyzed to predict whether the resource utilization between each layer of the ore area to be mined is balanced, and each layer of the ore area to be mined is divided into a high optimization area, a medium optimization area and a low optimization area according to the prediction result, which specifically includes the following steps:

Preprocessing the ore utilization information of each layer in the ore area to be mined;

Extracting resource utilization information and mining progress information of each layer from the pre-processed ore utilization information of each layer in the ore area to be mined, and analyzing them after extraction to generate resource utilization coefficients and mining progress indexes of each layer respectively;

The resource utilization coefficients and mining progress indexes of each level are used to construct a resource utilization equilibrium model, and the equilibrium coefficients of each level are generated. The equilibrium coefficients of all levels are comprehensively analyzed to generate the equilibrium standard deviation. The generated equilibrium standard deviation is compared with the preset equilibrium standard deviation threshold, and the resource utilization conditions of each level in the ore area to be mined are predicted based on the comparison results.

When the prediction result shows that the resource mobilization situation among various levels of the ore area to be mined is unbalanced, a pre-set threshold interval of the balance coefficient is determined, the threshold interval is compared with the balance coefficient of each level, and according to the comparison result, each level of the ore area to be mined is divided into high optimization area, medium optimization area and low optimization area.

Preferably, the logic for obtaining the resource utilization coefficient and mining progress index at each level is as follows:

The resource utilization information of each layer in the pre-processed ore utilization information of each layer in the ore area to be mined is extracted, including the total amount of mineable ore in each layer of the ore area to be mined, the average distribution density of the ore, and the amount of mined ore at different times within a period of time, and they are marked as , and , Indicates the area of ore to be mined. The total amount of mineable ore at each level, Indicates the area of ore to be mined. The average distribution density of ore in each layer, Indicates the area of ore to be mined. level within a period of time The amount of ore mined at a given time, , , and All are positive integers;

Calculate the resource utilization coefficient at each level. The specific calculation formula is as follows:

In the formula, For the The resource mobilization coefficient of each level;

Extract the mining progress information of each layer from the pre-processed ore utilization information of each layer in the ore area to be mined, including the expected mining volume of each layer in the ore area to be mined, as well as the equipment operation time and equipment efficiency at different times within a period of time, and mark them as , and , Indicates the area of ore to be mined. The expected mining volume at each level, Indicates the area of ore to be mined. level within a period of time The device running time at the moment, Indicates the area of ore to be mined. level within a period of time Equipment efficiency at all times;

Calculate the mining progress index at each level. The specific calculation formula is as follows:

In the formula, For the The mining progress index at each level.

Preferably, the resource utilization coefficients of each level generated and mining progress index Construct a resource mobilization balance model and generate balance coefficients at each level through weighted summation , equalize the coefficients of all levels Perform comprehensive analysis to generate balanced standard deviation , according to the formula: , and the resulting equilibrium standard deviation The pre-set equilibrium standard deviation threshold Compare and predict whether the resource utilization between various layers in the ore area to be mined is balanced based on the comparison results. The specific comparison analysis is as follows:

like , the resource mobilization between the layers of the ore area to be mined is balanced;

like , the resource utilization between the layers in the area of ore to be mined is uneven.

Preferably, when the prediction result shows that the resource utilization between the various layers of the ore area to be mined is unbalanced, a preset balance coefficient threshold interval is determined. , the threshold interval Balance coefficients at all levels Compare and divide the various levels of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the comparison results. The specific comparison division is as follows:

like , classifying this layer of the ore area to be mined as a low-optimization zone;

like , the layer of the ore area to be mined is divided into a medium optimization area;

like , dividing this layer of the ore area to be mined into a high optimization zone.

Preferably, a resource utilization adjustment mechanism is constructed according to the division results of each level of the ore area to be mined, specifically: according to the division results of the high optimization area, the medium optimization area and the low optimization area, different mining equipment scheduling, mining sequence adjustment and resource allocation parameters are set respectively to form a resource utilization adjustment mechanism; the adjustment mechanism is based on the balance coefficient and equipment efficiency of each area, and automatically determines the adjustment method and range of equipment scheduling and resource allocation through pre-set rules;

Different mining strategy adjustments are made to the high optimization area, medium optimization area and low optimization area respectively, specifically including: in the high optimization area, the resource restriction scheduling parameters in the adjustment mechanism are used to reduce the equipment scheduling frequency and reduce resource allocation; in the medium optimization area, the standard scheduling parameters in the adjustment mechanism are maintained, and the current equipment scheduling and resource allocation are not changed; in the low optimization area, the resource increase scheduling parameters in the adjustment mechanism are used to increase the equipment scheduling frequency and improve resource allocation to optimize resource mobilization.

Preferably, in the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time, and the information is analyzed to evaluate whether the adjustment effect of the adjustment mechanism at each level can meet expectations, and the adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps:

In the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time and pre-processed after acquisition;

Extract mining effect information and scheduling efficiency information from the dynamic feedback information of mining areas at various levels of the pre-processed ore area to be mined, and analyze them after extraction to generate resource mining compliance coefficients and equipment scheduling efficiency coefficients at various levels respectively;

The generated resource exploitation compliance coefficients and equipment scheduling efficiency coefficients at each level are used to construct an adjustment effect evaluation model to generate adjustment coefficients at each level. The generated adjustment coefficients at each level are compared with the pre-set adjustment coefficient thresholds at each level. Based on the comparison results, it is evaluated whether the adjustment effect of the adjustment mechanism at each level can meet expectations, and the adjustment mechanism is optimized based on the evaluation results.

Preferably, the logic for obtaining the resource mining compliance coefficient and equipment scheduling efficiency coefficient at each level is as follows:

Extract the mining effect information from the dynamic feedback information of each layer of the pre-processed ore area to be mined, including the actual mined ore volume, expected mined ore volume and corresponding time points at different times during the adjustment process of each layer of the ore area to be mined, and use functions according to the time series and To express, For time point, Indicates the area of ore to be mined. During the adjustment process, The actual amount of ore mined at the time, Indicates the area of ore to be mined. During the adjustment process, The expected amount of ore to be mined at the time, , is a positive integer, defining the time period as ;

Calculate the resource exploitation compliance coefficient at each level. The specific calculation formula is as follows:

In the formula, For the Resource extraction compliance coefficients at each level;

Extract the scheduling efficiency information from the dynamic feedback information of the mining area at each level of the pre-processed ore area to be mined, including the equipment scheduling frequency, actual utilization rate of the equipment, expected utilization rate of the equipment and the corresponding time points at different times during the adjustment process of each level of the ore area to be mined, and use the function to calculate the scheduling efficiency of the equipment according to the time series. , and To express, For time point, Indicates the area of ore to be mined. During the adjustment process, The device scheduling frequency at the moment, Indicates the area of ore to be mined. During the adjustment process, The actual utilization rate of the equipment at the moment, Indicates the area of ore to be mined. During the adjustment process, The expected utilization of the equipment at that time;

Calculate the equipment scheduling efficiency coefficient at each level. The specific calculation formula is as follows:

In the formula, For the Equipment scheduling efficiency coefficient at each level.

Preferably, the resource mining compliance coefficients at each level generated and equipment scheduling efficiency coefficient Construct an adjustment effect evaluation model and generate adjustment coefficients at each level through weighted summation , and the adjustment coefficients of each level generated The adjustment coefficient thresholds of each level are set in advance Compare and evaluate whether the adjustment effect of the adjustment mechanism at each layer can meet expectations based on the comparison results, and optimize the adjustment mechanism based on the evaluation results. The specific comparison and analysis is as follows:

like , the adjustment effect of the adjustment mechanism in this layer cannot meet the expectations, and the adjustment mechanism needs to be optimized, including: increasing the scheduling frequency and utilization rate of the mining equipment in this layer, adjusting the resource allocation ratio, and resetting the mining order; combining the real-time feedback data of the mining area to dynamically optimize the scheduling parameters and equipment operation plans;

like , the adjustment effect of the adjustment mechanism at this layer can achieve the expected effect, and there is no need to optimize the adjustment mechanism.

Preferably, the resource utilization analysis system using three-dimensional modeling visualization technology includes an ore layer division and sensor deployment module, a resource utilization balance prediction and analysis module, a resource utilization adjustment mechanism construction module, a dynamic feedback and adjustment effect evaluation module, and a mining process monitoring and optimization module;

Ore layer division and sensor deployment module: In the mining process of multi-level open-pit mines, the ore area to be mined is evenly divided into several layers, and a sensor network is deployed around each layer of the ore area to be mined to obtain real-time ore utilization information of each layer of the ore area to be mined;

The resource utilization balance prediction and analysis module analyzes the ore utilization information of each layer of the ore area to be mined, predicts whether the resource utilization of each layer of the ore area to be mined is balanced, and divides each layer of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the prediction results;

The resource utilization adjustment mechanism construction module builds a resource utilization adjustment mechanism based on the division results of various levels of the ore area to be mined, and performs different mining equipment scheduling, mining sequence adjustment and resource allocation optimization for the high optimization area, medium optimization area and low optimization area respectively;

Dynamic feedback and adjustment effect evaluation module, in the process of adjusting each layer by the resource mobilization adjustment mechanism, obtains the dynamic feedback information of the mining area at each level of the ore area to be mined in real time, analyzes this information, evaluates whether the adjustment effect of the adjustment mechanism at each level can meet the expectations, and optimizes the adjustment mechanism according to the evaluation results;

The mining process monitoring and optimization module conducts real-time monitoring and comprehensive analysis of resource mobilization data, adjustment records and mining effects throughout the entire mining process, continuously improves mining strategies and resource allocation plans, and optimizes adjustment mechanisms in real time.

In the above technical solution, the technical effects and advantages provided by the present invention are:

1. The present invention divides the ore area to be mined into several levels and deploys a sensor network. The system can obtain resource mobilization information and equipment operation status of each level in real time and accurately. Compared with the traditional two-dimensional analysis method, three-dimensional modeling combined with sensor networks provides more accurate data, allowing the mining progress and resource mobilization amount of different levels to be monitored synchronously. This layered real-time monitoring method solves the problem of delayed resource mobilization and mining progress caused by differences in terrain and geological conditions, and improves the management efficiency and mining transparency of the mining area.

2. Based on the resource mobilization information and mining progress index of each layer, the system of the present invention can predict and analyze the mining situation of each layer by constructing a resource mobilization balance model. By dividing each layer into high optimization area, medium optimization area and low optimization area, the system can flexibly adjust the mining equipment scheduling, mining sequence and resource allocation according to the optimization needs of different levels. This adjustment mechanism ensures the balance and rationality of resource mobilization, avoids resource waste, over-exploitation and safety hazards caused by differences in mining progress, and improves the safety and economy of the overall mining operation.

3. The present invention can continuously obtain dynamic data during the mining process through a real-time feedback mechanism, evaluate and optimize the adjustment effect. Combined with the actual dynamic feedback data of the mining area, the system can timely adjust the equipment scheduling and resource allocation strategy to ensure that the mining operations at each level are consistent with the expected goals. This continuous optimization capability greatly improves the overall mining efficiency and resource utilization of the mining area, ensures the stability and balance of the entire mining process, and reduces the risks of mine collapse, equipment misoperation, etc., thereby improving the safety level and economic benefits of the mining area.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

FIG1 is a flow chart of a resource utilization analysis system and method using three-dimensional modeling visualization technology according to the present invention.

FIG. 2 is a module diagram of a resource utilization analysis system and method using three-dimensional modeling visualization technology according to the present invention.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

The present invention provides a resource utilization analysis method using three-dimensional modeling visualization technology as shown in FIG1 , which specifically includes the following steps:

In the mining process of multi-level open-pit mines, the ore area to be mined is evenly divided into several levels, and a sensor network is deployed around each level of the ore area to be mined to obtain the ore utilization information of each layer in the ore area to be mined in real time;

In the mining process of multi-level open-pit mines, 3D modeling software can be used to evenly divide the ore area to be mined based on terrain and geological data. First, the 3D terrain and geological data of the mining area are obtained through drone oblique photography, laser radar (LiDAR), geological surveys, etc. Then, these data are input into the software through 3D modeling tools, and the ore area is divided into several uniform layers using an automatic stratification algorithm based on parameters such as geological structure, ore distribution, and layer height. The division criteria for each layer can be flexibly adjusted according to the requirements of ore reserves, geological stability, and mining planning to ensure that the mining difficulty and resource mobilization of each layer are relatively balanced. This process is implemented through software, which ensures the accurate division of ore areas and provides data support for subsequent mining.

After the division is completed, the deployment of the sensor network is achieved through a remote monitoring system and a sensor distribution algorithm. First, the software is used to analyze the geological conditions, mining paths, and equipment distribution at each level to determine the optimal layout of the sensor network. Sensor types include geological sensors, vibration sensors, temperature sensors, laser scanners, and GPS positioning sensors. During deployment, sensor nodes are set up around the ore area, and these nodes are interconnected through a wireless sensor network to form a real-time monitoring system that can cover the entire mining area. The sensors continuously collect ore mobilization information (such as ore distribution, geological changes, and mining equipment status), and upload the data to the central control system through a remote data transmission module for real-time monitoring and feedback analysis. The software is responsible for managing sensor data collection, synchronization, and analysis.

The core purpose of this process is to solve the problem of uneven resource utilization and mining progress in each layer of a multi-level open-pit mine. By dividing the ore area into multiple layers through software, the geological structure, ore distribution and mining conditions of each layer can be clearly visible, which is convenient for subsequent precise mining planning. At the same time, the deployment of the sensor network enables the system to obtain ore utilization information of each layer in real time, which is particularly important for mining optimization under complex geological conditions. The data fed back by the sensor can help the system dynamically adjust the scheduling, resource allocation and mining sequence of mining equipment to ensure balanced resource utilization at each layer and avoid waste of resources or over-mining due to differences in geological structures. This solution combines data collection, real-time monitoring and optimized scheduling to achieve a dual improvement in mining efficiency and safety, meeting the needs of intelligent management of modern mining areas.

Analyze the ore utilization information of each layer of the ore area to be mined, predict whether the resource utilization of each layer in the ore area to be mined is balanced, and divide each layer of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the prediction results;

In this embodiment, the ore utilization information of each layer of the ore area to be mined is analyzed to predict whether the resource utilization of each layer of the ore area to be mined is balanced, and each layer of the ore area to be mined is divided into a high optimization area, a medium optimization area and a low optimization area according to the prediction results, which specifically includes the following steps:

Preprocessing the ore utilization information of each layer in the ore area to be mined;

The main reason for preprocessing is to improve the accuracy and efficiency of subsequent analysis and model building. The acquired ore use information may contain noise, redundant data or inconsistent formats. Direct use of this data may lead to inaccurate analysis results. Therefore, preprocessing can clean, standardize and optimize the data to make it suitable for subsequent processing and analysis. Preprocessing includes the following specific tasks: data cleaning, format unification, missing value processing and data normalization. First, data cleaning is performed, including removing erroneous or invalid data points, such as abnormal data caused by sensor failure or duplicate data during acquisition. Next, format unification is performed to integrate data from different sources into a unified format for subsequent software processing. Then, missing value processing is used to fill or interpolate the missing ore use information to ensure data integrity. Finally, data normalization is performed to scale data of different dimensions to the same range to ensure that the data can be processed in a consistent manner during analysis and model building. The entire preprocessing process is automated by software, for example, using algorithms to clean the data, convert the format, handle missing values and normalize to ensure that the data quality meets the needs of subsequent analysis.

Extracting resource utilization information and mining progress information of each layer from the pre-processed ore utilization information of each layer in the ore area to be mined, and analyzing them after extraction to generate resource utilization coefficients and mining progress indexes of each layer respectively;

The resource utilization coefficients and mining progress indexes of each level are used to construct a resource utilization equilibrium model, and the equilibrium coefficients of each level are generated. The equilibrium coefficients of all levels are comprehensively analyzed to generate the equilibrium standard deviation. The generated equilibrium standard deviation is compared with the preset equilibrium standard deviation threshold, and the resource utilization conditions of each level in the ore area to be mined are predicted based on the comparison results.

Determining the "pre-set equilibrium standard deviation threshold" can be achieved through a comprehensive analysis of historical data and current environmental variables. First, using the historical resource mobilization data of the mining area, analyze the resource mobilization and mining progress of each level, and calculate the equilibrium coefficient and standard deviation under different circumstances. These historical data can provide a benchmark reference for the model to determine the equilibrium range of ore mobilization under normal conditions. Secondly, by collecting parameters such as the geological conditions, ore distribution density, and mining equipment efficiency of the current mining area, combined with the analysis results of the historical standard deviation, the standard deviation threshold is dynamically adjusted using machine learning or statistical analysis algorithms. The specific implementation method is that the software will train the model based on the existing data set, and continuously adjust the standard deviation threshold through the model so that it can reflect the actual resource mobilization in different mining areas and under different geological conditions. Combining historical data with real-time monitoring data, the software can set the most suitable equilibrium standard deviation threshold for the actual situation of the current mining area, ensuring that the threshold has sufficient adaptability and flexibility.

When the prediction result shows that the resource mobilization situation among various levels of the ore area to be mined is unbalanced, a pre-set threshold interval of the balance coefficient is determined, the threshold interval is compared with the balance coefficient of each level, and according to the comparison result, each level of the ore area to be mined is divided into high optimization area, medium optimization area and low optimization area.

The "pre-set balance coefficient threshold interval" can be determined by a comprehensive analysis of the historical mining data, resource distribution patterns, geological characteristics and current mining conditions of the mining area. First, the software analyzes the resource utilization coefficients of different mining levels in the historical data and identifies the typical range of resource utilization coefficients under the conditions of relatively balanced and unbalanced resource utilization. Then, different balance coefficient intervals in the historical data are divided through statistical analysis techniques, such as quantile analysis or cluster analysis. These intervals will serve as preliminary reference thresholds. Secondly, combined with the specific mining conditions of the current mining area, including variables such as ore density, geological structure complexity, and equipment efficiency, the software uses an algorithm to weight these factors to generate the optimal balance coefficient threshold interval under current conditions. In this way, the threshold interval is not only based on historical data, but also dynamically considers changes in the current environment. In this way, the software can generate adaptive balance coefficient threshold intervals for dividing each level, thereby ensuring the optimal allocation of resources.

In this embodiment, the logic for obtaining the resource utilization coefficient and mining progress index at each level is as follows:

The resource utilization information of each layer in the pre-processed ore utilization information of each layer in the ore area to be mined is extracted, including the total amount of mineable ore in each layer of the ore area to be mined, the average distribution density of the ore, and the amount of mined ore at different times within a period of time, and they are marked as , and , Indicates the area of ore to be mined. The total amount of mineable ore at each level, Indicates the area of ore to be mined. The average distribution density of ore in each layer, Indicates the area of ore to be mined. level within a period of time The amount of ore mined at a given time, , , and All are positive integers;

Extracting the ore mobilization information of each layer in the pre-processed ore area to be mined is mainly achieved through software automation. The total amount of mineable ore is obtained through the previous geological exploration data and geological modeling tools. The software retrieves these data from the geological database and integrates them; the average distribution density of the ore is monitored in real time by sensors inside the mining area (such as gravity sensors). Combined with historical geological data, the software automatically calculates and generates an average value; the amount of mined ore is recorded in real time at each moment by the sensors of the automated mining equipment. The software extracts these data from the equipment monitoring system and accumulates and analyzes them to achieve accurate recording of the amount mined at different times. All of this is automatically completed through the integration of software data interfaces and sensor networks.

Calculate the resource utilization coefficient at each level. The specific calculation formula is as follows:

In the formula, For the The resource mobilization coefficient of each level;

This formula is used to calculate the resource utilization coefficient at each level , by comprehensively considering the amount of mined ore at each moment, the total amount of mineable ore, and the average distribution density of ore to evaluate resource utilization. Amount of ore mined The total amount of ore that can be mined in this layer during the entire period of time is calculated by summing up. Average distribution density of ore As the denominator, it is used to standardize the amount of ore mined. The purpose of this is to ensure that the resource mobilization coefficient reflects the efficiency of resource utilization at this level by considering the total amount of resources and distribution density at this level, rather than relying solely on the amount of mining. Finally, the formula takes a weighted average of the mining volume at all times to measure the resource mobilization over the entire time period. In this way, the formula can reflect the amount of resource mobilization while taking into account the geological characteristics of the ore and the total amount of resources, ensuring the comprehensiveness and accuracy of the assessment.

No. The size of the resource mobilization coefficient of each level directly reflects the resource utilization of that level, and is an important indicator for predicting whether the resource mobilization of each level is balanced. In our technical solution, the resource mobilization coefficient is calculated by combining the mining volume at different times, the total amount of mineable ore, and the average distribution density. The larger the value, the more fully the resources at that level are utilized; the smaller the value, the less effectively the resources are utilized. After we obtain the resource mobilization coefficients of all levels, we conduct a comprehensive analysis. By comparing the differences in resource mobilization coefficients of each level, we can predict whether the resource mobilization of each level is balanced. If the resource mobilization coefficient of some levels is significantly low, it indicates that the resources at that level are underutilized and may need to be optimized. Based on these analysis results, we can divide each level into high optimization area, medium optimization area, and low optimization area, so as to adopt targeted mining strategies to ensure the balance of resource mobilization and overall mining efficiency.

Extract the mining progress information of each layer from the pre-processed ore utilization information of each layer in the ore area to be mined, including the expected mining volume of each layer in the ore area to be mined, as well as the equipment operation time and equipment efficiency at different times within a period of time, and mark them as , and , Indicates the area of ore to be mined. The expected mining volume at each level, Indicates the area of ore to be mined. level within a period of time The device running time at the moment, Indicates the area of ore to be mined. level within a period of time Equipment efficiency at all times;

Extract the mining progress information of each layer in the ore area to be mined, mainly through the software system combined with mining equipment and real-time monitoring system to obtain data. First, the expected mining volume is generated by the mine planning software according to the mining plan and ore reserve estimation tool. The software retrieves the target mining volume of each layer from the mining plan database of the mine area and allocates it to each layer according to the mine area plan. Next, the equipment running time is automatically obtained through the time recording sensor on the mining equipment. The equipment running time can be recorded in real time through the work log and sensor data in the equipment management system. The software will extract this data from the equipment monitoring database at regular intervals. Equipment efficiency depends on equipment sensors (such as power monitoring sensors and load sensors) to monitor the working efficiency of the equipment. Combined with the load, speed and other parameters of the equipment, the software will automatically calculate the real-time efficiency of the equipment at each moment. By integrating these data from different sources, the software can update the equipment running time and efficiency at different times to ensure the accurate acquisition of mining progress information.

Calculate the mining progress index at each level. The specific calculation formula is as follows:

In the formula, For the The mining progress index at each level.

This formula is used to calculate the mining progress index at each level , which comprehensively evaluates the mining progress of each level by combining the equipment's operating time, equipment efficiency, and mined volume. The specific calculation steps are as follows: First, is the expected mining volume of this level, which represents the total amount expected to be mined in the mining area planning. The purpose of using it as the denominator is to compare the actual mining progress with the expected mining target and measure whether the current mining progress is consistent with the expectation. Then, the formula is used for each moment The actual amount mined This part reflects how the production at each moment is accumulated over a period of time. In order to relate these production volumes to the actual working conditions of the equipment, the formula takes into account the equipment's operating time. and equipment efficiency . Running time It reflects the actual working time of the equipment at each moment, and the equipment efficiency It reflects the performance of the equipment at each moment. These two as denominators mean that the formula not only considers the absolute amount of mining, but also the actual operation of the equipment during the mining process to evaluate the effective utilization of the equipment. By combining these two factors, the formula can more accurately reflect the relationship between the actual mining progress and the working efficiency of the equipment. Finally, by taking the weighted sum of the mining volume and equipment performance at all times and comparing it with the expected mining volume, the formula derives the mining progress index. , reflecting the actual mining progress relative to the expected progress. This calculation method ensures that the mining progress not only reflects the total amount, but also takes into account the actual working efficiency of the equipment and the time utilization, thus providing a more comprehensive mining progress assessment.

No. The size of the mining progress index of each level directly reflects the degree of consistency between the actual mining progress of the level and the expected plan, and is one of the key indicators for judging whether the resource mobilization of each level is balanced. In our technical solution, the mining progress index is calculated by integrating the operating time, equipment efficiency and mined volume of the equipment. The larger the value, the closer the mining progress of the level is to the expected target. Conversely, it indicates that the mining progress is lagging behind. After the mining progress index of all levels is calculated, we can predict whether the mining progress and resource mobilization between the levels are balanced by comparing these indexes. If the mining progress index of some levels is low, it means that the resources of the level are insufficient and the mining is unbalanced. Based on these prediction results, we divide each level into high optimization area, medium optimization area and low optimization area, so as to formulate corresponding optimization strategies to ensure that the mining progress and resource utilization of the entire mining area are balanced.

In this embodiment, the resource utilization coefficients of each level are generated. and mining progress index Construct a resource mobilization balance model and generate balance coefficients at each level through weighted summation , equalize the coefficients of all levels Perform comprehensive analysis to generate balanced standard deviation , according to the formula: , and the resulting equilibrium standard deviation The pre-set equilibrium standard deviation threshold Compare and predict whether the resource utilization between various layers in the ore area to be mined is balanced based on the comparison results. The specific comparison analysis is as follows:

like , the resource mobilization between the layers of the ore area to be mined is balanced, which means that the resource mobilization between the various layers is relatively balanced and the resource utilization rate is relatively uniform. This shows that the current mining strategy can well coordinate the resource mobilization and mining progress of each layer, and there is no obvious imbalance between the layers. Therefore, in this case, there is no need to make large-scale adjustments to the current mining strategy, and it can continue to proceed according to the established plan. This result means that the overall resource mobilization efficiency of the mining area is high, and the mining process can proceed steadily, thereby reducing resource waste and unnecessary equipment downtime, and improving the economic benefits and safety of the mining area;

like The resource mobilization between the layers in the ore area to be mined is unbalanced, indicating that the resource mobilization between the various layers in the ore area to be mined is unbalanced, and some layers may have excessive or insufficient resource mobilization. This unbalanced resource mobilization will lead to a decrease in the overall mining efficiency, and some layers may be over-mined, increasing the risk of resource waste, or the mining progress of some layers may lag behind, affecting the mining progress of the entire mining area. In this case, it is necessary to divide each layer into high optimization area, medium optimization area and low optimization area according to the specific balance coefficient value, and take targeted optimization measures to achieve balanced resource mobilization in the subsequent mining process, improve the mining efficiency of the mining area, and reduce potential safety hazards and economic losses.

"The resource utilization coefficients at each level generated and mining progress index The resource mobilization equilibrium model is constructed by combining these two key parameters to evaluate the mining situation at each level. Specifically, the model first assigns weights to the resource mobilization coefficient and mining progress index at each level. The weights are assumed to be and The two weights are allocated according to their respective impacts on mining balance and efficiency. For example, if resource mobilization has a greater impact on overall balance, then will be High, and vice versa. Then the equalization coefficient of each layer is calculated by weighted summation. , the formula is: In this way, the resource mobilization and progress balance of each layer can be comprehensively evaluated, and the overall mining plan of the mine area can be further optimized and adjusted accordingly.

In this embodiment, when the prediction result shows that the resource utilization between the various layers of the ore area to be mined is unbalanced, a preset balance coefficient threshold interval is determined. , the threshold interval Balance coefficients at all levels Compare and divide the various levels of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the comparison results. The specific comparison division is as follows:

like , classifying this layer of the ore area to be mined as a low-optimization area means that the resource mobilization of this layer is obviously insufficient, resulting in low resource utilization, delayed mining progress, and may cause the overall mining plan of the mining area to fail to be completed on time. At this time, the mobilization amount can be increased by increasing the equipment investment and resource allocation of this layer. When dividing, the software can monitor the resource mobilization coefficient of each layer in real time and compare it with the pre-set Compare and automatically classify the qualified layers into the low-optimization area, prompting the adjustment of optimization strategy;

like , the layer of the ore area to be mined is divided into the medium optimization area, which means that the resource utilization of this layer is within a reasonable range, the resource utilization and mining progress are in line with expectations, and no further adjustment is required. At this time, the software will conduct regular monitoring of the utilization coefficient of this layer, and compare the utilization coefficient with the and to ensure that it remains in the mid-optimization zone and maintains the existing mining plan;

like , classifying the layer of the ore area to be mined as a high optimization area means that the resources in this layer are too much, which may lead to over-mining or over-use of equipment, and then cause risks such as waste of resources or equipment damage. At this time, it is necessary to reduce the mining intensity of this layer or deploy some equipment to other layers. The division process is realized through software, which automatically analyzes the resource utilization of each layer and makes dynamic adjustments and divisions in real time based on the pre-set threshold interval.

According to the division results of various levels of the ore area to be mined, a resource utilization adjustment mechanism is established to carry out different mining equipment scheduling, mining sequence adjustment and resource allocation optimization for the high optimization area, medium optimization area and low optimization area respectively;

In this embodiment, according to the division results of each level of the ore area to be mined, a resource utilization adjustment mechanism is constructed, specifically: according to the division results of the high optimization area, the medium optimization area and the low optimization area, different mining equipment scheduling, mining sequence adjustment and resource allocation parameters are set respectively to form a resource utilization adjustment mechanism; the adjustment mechanism is based on the balance coefficient and equipment efficiency of each area, and automatically determines the adjustment method and range of equipment scheduling and resource allocation through pre-set rules;

The various aspects of building a resource utilization adjustment mechanism can be achieved in the following ways: First, divide the high optimization area, medium optimization area and low optimization area. The software can monitor the resource utilization of each level in real time, and compare the calculated balance coefficient with the preset threshold interval in combination with the balance coefficient and equipment efficiency of each level. Through this comparison, the software can automatically identify the resource utilization status of each level and determine which optimization zone it belongs to. The core of this step is to use historical data, real-time mining data and automated analysis tools to divide resource utilization into different priorities for subsequent optimization and scheduling.

Then, different mining equipment scheduling, mining sequence adjustment and resource allocation parameters are set for each optimization zone. The software can automatically allocate resources based on the optimization zone division results at each level. For example, for high optimization zones, the system will reduce the frequency of equipment scheduling and reduce resource input to avoid over-mining; while for low optimization zones, the software will increase the frequency of equipment scheduling and improve resource allocation. This adjustment mechanism can be achieved through the equipment monitoring system, the scheduling management software of the mining equipment, and the resource allocation module. In specific operations, the software can monitor the equipment operation in real time through equipment sensors and the mining management system, and dynamically adjust the equipment scheduling sequence and mining task allocation according to actual needs.

Finally, the adjustment method and range of equipment scheduling and resource allocation are automatically determined through pre-set rules. The setting of rules is based on multi-dimensional data input, such as the mining efficiency of the equipment, the resource mobilization situation at the current level, and historical mining data. Based on these input data, the software dynamically generates decision rules, such as analyzing the historical scheduling data at each level through machine learning algorithms to automatically generate the optimal scheduling strategy. Each equipment scheduling adjustment and resource configuration change will be determined based on these rules to ensure the automation and accuracy of scheduling.

Summary: This mechanism uses intelligent software to achieve automatic adjustments at all levels during the mining process, which can optimize resource allocation and equipment scheduling in real time, ensuring balanced resource mobilization and maximum mining efficiency in the mining area. This approach can not only improve resource utilization, but also prevent excessive use of equipment or waste of resources, ensuring the overall safety and economic benefits of the mining area.

Different mining strategy adjustments are made to the high optimization area, medium optimization area and low optimization area respectively, specifically including: in the high optimization area, the resource restriction scheduling parameters in the adjustment mechanism are used to reduce the equipment scheduling frequency and reduce resource allocation; in the medium optimization area, the standard scheduling parameters in the adjustment mechanism are maintained, and the current equipment scheduling and resource allocation are not changed; in the low optimization area, the resource increase scheduling parameters in the adjustment mechanism are used to increase the equipment scheduling frequency and improve resource allocation to optimize resource mobilization.

The adjustment of different mining strategies for high, medium and low optimization areas can be achieved through software system automation and equipment scheduling management tools. In the high optimization area, due to excessive resource mobilization, mining may be too aggressive, and the software will automatically implement resource restriction strategies. Through the scheduling management system, the software can reduce the operating frequency of the equipment, reduce the time period of equipment use, and reduce the fuel, energy and other resources used by the mining equipment through the resource allocation system. This is to prevent over-mining and equipment overload, avoid waste of resources and excessive equipment loss. In this process, the equipment scheduling frequency and resource allocation will be dynamically adjusted according to the real-time feedback of the optimization area to ensure the rational use of resources.

In the middle optimization area, the current mining strategy is already in a balanced state, and resource mobilization and equipment scheduling are in line with expectations. Therefore, the software will maintain the standard scheduling parameters without making additional adjustments. This can maintain the existing mining equipment frequency and resource input through the equipment scheduling system, and maintain the original mining efficiency. The purpose of this step is to ensure that when resources are reasonably mobilized, no excessive adjustments are made, avoid unnecessary interventions that interfere with the mining process, and maintain mining stability.

In the low-optimization area, due to insufficient resource mobilization, the mining progress may lag behind, and the software will make adjustments by increasing the scheduling frequency and improving resource allocation. The specific implementation methods include real-time monitoring of the equipment's operating status through the equipment monitoring system, dynamically increasing the equipment's working time or increasing the number of equipment inputs. At the same time, the resource allocation module will automatically allocate more mining resources (such as fuel, energy, and personnel) to ensure that the mining speed in this area can be accelerated to make up for the insufficient resource mobilization. The role of this adjustment mechanism is to improve the mining efficiency of the low-optimization area by strengthening equipment use and resource investment, so that it can be balanced with other levels, and avoid the overall mining efficiency from declining due to insufficient resource mobilization.

In summary, these strategies, through the combination of software systems and automated scheduling management, can adjust equipment scheduling and resource allocation in real time and dynamically according to the conditions of each optimization area, thereby ensuring the balance of resource mobilization and mining progress in the entire mining area, and ultimately achieving efficient resource utilization and optimization of the mining process.

In the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time, and the information is analyzed to evaluate whether the adjustment effect of the adjustment mechanism at each level can meet the expectations, and the adjustment mechanism is optimized according to the evaluation results;

In this embodiment, in the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time, and the information is analyzed to evaluate whether the adjustment effect of the adjustment mechanism at each level can meet the expectations, and the adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps:

In the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time and pre-processed after acquisition;

In the process of adjusting each layer by the resource mobilization adjustment mechanism, dynamic feedback information of the mining area can be obtained in real time by deploying sensor networks and equipment monitoring systems. This information can include mining progress, equipment utilization, equipment scheduling frequency, etc. of each layer. Sensors can monitor data such as ore mining volume, geological changes, equipment working conditions, and transmit these data to the central control system through wireless communication technology. Combined with technical means such as drone remote sensing, geological radar, and built-in sensors in the equipment, it is possible to fully capture the dynamic situation of each layer of the mining area. The software system will generate feedback information based on the real-time data received by the sensor for subsequent analysis.

The purpose of preprocessing is to ensure the accuracy, consistency and integrity of the data for subsequent analysis. First, eliminate noise: Since sensor data may be interfered with, the software can use filtering algorithms to clean the data and exclude outliers and erroneous data. Second, time synchronization: The data acquisition frequency of different sensors may be different. The software will align the data timestamps to ensure that the data at different times have consistent timing. Finally, format standardization: Different types of sensor data formats may be different. The software will convert the format and unify the data structure for subsequent analysis and processing. After the preprocessing is completed, the dynamic feedback information can ensure high quality and consistency, laying the foundation for subsequent analysis and parameter calculation.

Extract mining effect information and scheduling efficiency information from the dynamic feedback information of mining areas at various levels of the pre-processed ore area to be mined, and analyze them after extraction to generate resource mining compliance coefficients and equipment scheduling efficiency coefficients at various levels respectively;

The generated resource exploitation compliance coefficients and equipment scheduling efficiency coefficients at each level are used to construct an adjustment effect evaluation model to generate adjustment coefficients at each level. The generated adjustment coefficients at each level are compared with the pre-set adjustment coefficient thresholds at each level. Based on the comparison results, it is evaluated whether the adjustment effect of the adjustment mechanism at each level can meet expectations, and the adjustment mechanism is optimized based on the evaluation results.

The preset adjustment coefficient thresholds for each layer can be determined by software analysis of historical data, simulation calculations, and machine learning models. First, the software can perform statistical analysis based on historical mining data at each layer to determine the optimal state of resource mining at each layer and the ideal value of equipment scheduling efficiency. Through regression analysis of past mining efficiency, resource mobilization balance, equipment utilization and other data, the mining rules and ideal range of adjustment coefficients for each layer can be extracted. Secondly, the software can use simulation calculation models to simulate the mining process under different adjustment mechanisms based on different mining environments, geological conditions, and equipment configurations to obtain the optimal adjustment coefficient for each layer. Finally, combined with machine learning algorithms, the software can continuously learn and update the mining mode and adjustment effect of each layer, and dynamically optimize the threshold setting based on actual feedback, thereby ensuring that the adjustment coefficient thresholds for each layer are flexible and accurate.

In this embodiment, the logic for obtaining resource mining compliance coefficients and equipment scheduling efficiency coefficients at each level is as follows:

Extract the mining effect information from the dynamic feedback information of each layer of the pre-processed ore area to be mined, including the actual mined ore volume, expected mined ore volume and corresponding time points at different times during the adjustment process of each layer of the ore area to be mined, and use functions according to the time series and To express, For time point, Indicates the area of ore to be mined. During the adjustment process, The actual amount of ore mined at the time, Indicates the area of ore to be mined. During the adjustment process, The expected amount of ore to be mined at the time, , is a positive integer, defining the time period as ;

When extracting mining effect information from the dynamic feedback information of the mining area at each level of the ore area to be mined, data can be obtained in real time through the sensor network and monitoring system deployed in the mining area. Specifically, the actual amount of mined ore can be monitored by load sensors installed on the mining equipment. These sensors record the weight of ore for each mining operation and transmit the data to the central control system. The expected amount of mined ore can be automatically calculated and generated by the software system based on the equipment capacity, mining area structure, and preset mining progress by combining the mining plan and the geological model of the mining area. The corresponding time point is recorded by the sensor The timestamp of the mining operation is synchronized by the software to ensure that the mining data at different times can accurately correspond. The entire extraction process is automatically integrated by the software from the sensor The data stream and converted into mining effect information provides a basis for subsequent analysis and evaluation.

Calculate the resource exploitation compliance coefficient at each level. The specific calculation formula is as follows:

In the formula, For the Resource extraction compliance coefficients at each level;

For this formula, the resource extraction compliance coefficient By comparing the actual amount of ore mined in each layer over a period of time Expected mining volume The formula uses the integral form because it takes into account the resource mining situation at different times and accumulates the changes in mining volume during the entire adjustment process in order to evaluate the average level of mining effect over the entire time period. By using integral calculation, we can avoid the deviation caused by relying only on data at a certain moment, and comprehensively measure the resource mobilization situation over the entire time period. At the same time, using the time interval Standardization is performed to ensure that the coefficient reflects the overall trend over time rather than accidental fluctuations at a certain moment. This calculation method helps to comprehensively and dynamically assess whether resource extraction meets the standards.

No. Resource extraction compliance coefficient at each level The size of directly reflects the gap between the actual mining effect of the layer and the expected target. If the value is close to 1, it means that the actual amount of ore mined is close to the expected amount of ore mined, indicating that the resource mining situation at this level has basically reached the expected target; if If it is less than 1, it means that the actual mining volume has not reached the expected level, and there may be problems such as insufficient equipment scheduling or insufficient resource utilization; If it is greater than 1, it means that the actual mining volume exceeds expectations and there may be a problem of over-mining, which may affect the long-term utilization of resources and the stability of the mining area. The size of is an important parameter for evaluating whether the adjustment mechanism has achieved the expected mining effect at each layer.

Extract the scheduling efficiency information from the dynamic feedback information of the mining area at each level of the pre-processed ore area to be mined, including the equipment scheduling frequency, actual utilization rate of the equipment, expected utilization rate of the equipment and the corresponding time points at different times during the adjustment process of each level of the ore area to be mined, and use the function to calculate the scheduling efficiency of the equipment according to the time series. , and To express, For time point, Indicates the area of ore to be mined. During the adjustment process, The device scheduling frequency at the moment, Indicates the area of ore to be mined. During the adjustment process, The actual utilization rate of the equipment at the moment, Indicates the area of ore to be mined. During the adjustment process, The expected utilization of the equipment at that time;

When extracting the dispatch efficiency information at each level of the area to be mined, the required data can be obtained through the equipment monitoring system and sensor network. The frequency of equipment dispatch can be determined by recording the time points of the equipment start and stop operation, using the dispatch management system to monitor each dispatch behavior of the equipment, and the software automatically counts the frequency of equipment dispatch over a period of time. The actual utilization of the equipment can be calculated by combining the equipment's working time and the equipment's rated running time. The working status sensor on the equipment records when the equipment is in working state, and the software calculates the actual utilization based on the ratio of total running time to working time. The expected utilization of the equipment is generated by the software based on the mining plan, equipment capacity and efficiency model, which are derived from the design parameters of the mining task. For the time point, all data are obtained with precise timestamps, and the synchronization function of the sensor system ensures that the data is related in time sequence. The software integrates and analyzes all this data and automatically generates dispatch efficiency information for further adjustment mechanism evaluation.

Calculate the equipment scheduling efficiency coefficient at each level. The specific calculation formula is as follows:

In the formula, For the Equipment scheduling efficiency coefficient at each level.

The equipment scheduling efficiency coefficient in this formula It is the frequency of device scheduling at different times , Actual Utilization Rate Expected utilization First, the device scheduling frequency Indicates the operating frequency of the equipment at a certain moment, reflecting the actual use of the equipment; actual utilization rate Indicates the actual working efficiency of the equipment at a certain point in time; while the expected utilization rate It is a reference value based on the ideal performance of the equipment and the task planning setting. The formula combines the actual utilization rate with the expected utilization rate, compares the actual dispatch frequency of the equipment, and calculates whether the dispatch effect of the equipment meets the standard. The use of the integral form is to conduct a cumulative analysis of the equipment operation status at different times, so as to obtain the comprehensive dispatch efficiency during the entire adjustment period. Finally, by dividing the calculation result by the time interval , the formula standardizes the results so that the output scheduling efficiency coefficient can reflect the average effect of the entire adjustment process rather than the fluctuation at a single moment. This ensures the comprehensiveness and time consistency of scheduling efficiency.

No. The size of the equipment scheduling efficiency coefficient at each level is used to measure the matching degree between the actual execution of equipment scheduling and the expected efficiency. If the value is close to 1, it means that the actual scheduling frequency and utilization rate of the equipment are in line with expectations, indicating that the scheduling arrangement of the equipment has achieved the expected effect; if If it is less than 1, it means that the utilization rate or scheduling frequency of the equipment is low, and the equipment may not be fully utilized, resulting in reduced resource mining efficiency; If it is greater than 1, it means that the equipment is over-scheduled and there may be over-use of the equipment, which increases the wear and maintenance cost of the equipment. The size of can directly reflect the rationality of equipment scheduling and utilization, thereby evaluating whether the execution effect of the adjustment mechanism in equipment scheduling is up to standard.

In this embodiment, the resource exploitation compliance coefficients at each level are generated. and equipment scheduling efficiency coefficient Construct an adjustment effect evaluation model and generate adjustment coefficients at each level through weighted summation , and the adjustment coefficients of each level generated The adjustment coefficient thresholds of each level are set in advance Compare and evaluate whether the adjustment effect of the adjustment mechanism at each layer can meet expectations based on the comparison results, and optimize the adjustment mechanism based on the evaluation results. The specific comparison and analysis is as follows:

like , the adjustment effect of the adjustment mechanism in this layer cannot meet the expectations, and the adjustment mechanism needs to be optimized, including: increasing the scheduling frequency and utilization rate of the mining equipment in this layer, adjusting the resource allocation ratio, and resetting the mining order; combining the real-time feedback data of the mining area to dynamically optimize the scheduling parameters and equipment operation plans;

This means that The adjustment mechanism of the layer fails to achieve the expected effect, and the efficiency of equipment scheduling and resource utilization is low, resulting in uneven resource mining or delayed progress. The impact may include: the actual mining volume of the equipment fails to match the expected mining volume, resulting in reduced mining efficiency, affecting the mining plan, and even causing problems such as waste of resources or over-mining. In addition, inefficient scheduling increases the idle time of equipment, wastes resources and affects the overall operating efficiency of the mine.

In view of the fact that "the adjustment effect of the adjustment mechanism at this layer cannot meet expectations, the adjustment mechanism needs to be optimized, including: increasing the scheduling frequency and utilization rate of the mining equipment at this layer, adjusting the resource allocation ratio, and resetting the mining order", it can be achieved in the following ways: Equipment scheduling optimization: The software system monitors the operating status and scheduling frequency of the equipment in real time, and analyzes the idle time of the equipment and the completion of tasks. The software can automatically adjust the scheduling frequency of the equipment, increase the working time of the equipment, and reduce the idle time. For example, a scheduling frequency threshold is set. When the scheduling frequency is lower than the threshold, the scheduling frequency is automatically increased to ensure that the equipment is used more efficiently. Resource allocation adjustment: The software system can dynamically analyze the resource allocation efficiency of each layer based on real-time feedback data and automatically adjust the resource allocation ratio of each layer. For example, the resource allocation weight can be dynamically adjusted through the algorithm, and more resources can be allocated to the layer that needs to be accelerated, thereby optimizing the utilization of resources and reducing unnecessary waiting and excessive resource waste. Resetting the mining sequence: Combined with the real-time feedback data from the mining area, the software can automatically reset the optimal mining sequence by analyzing the resource mobilization, equipment scheduling efficiency, geological conditions, etc. of each layer, giving priority to layers with higher resource mobilization or lower equipment efficiency, ensuring a more balanced mining progress across the mining area. This is achieved by optimizing the scheduling algorithm to ensure coordination and efficiency across the mining area.

like , the adjustment effect of the adjustment mechanism at this layer can achieve the expected effect, and there is no need to optimize the adjustment mechanism.

This means that The adjustment mechanism of the layer has achieved the expected effect, the equipment scheduling frequency and resource utilization rate are within a reasonable range, and the mining progress and resource mobilization volume meet the predetermined targets. Its impact includes the rational use of equipment, balanced distribution of mining resources, effective guarantee of mining progress and quality, reduction of resource waste or over-mining risks, and at the same time improving the overall operating efficiency of the mining area and ensuring the rational use of equipment and resources.

"Using the generated resource mining compliance coefficients and equipment scheduling efficiency coefficients at each level to construct an adjustment effect evaluation model" is to combine these two coefficients to conduct a comprehensive evaluation of resource utilization and equipment scheduling at each level. The specific method is: first, the resource mining compliance coefficients and equipment scheduling efficiency coefficient Assign weights proportionally, usually based on their importance to the overall extraction process, such as when resource extraction has a greater impact on the final outcome. May have a higher weight, and when equipment scheduling has a greater impact on efficiency, Assume that the weights are and , then through the weighted summation formula Adjustment coefficients for each level can be generated . Weight coefficient and The determination can be based on historical data, adjustment experience or dynamic adjustment according to the actual operational needs of different mining areas, ensuring that the evaluation results can accurately reflect the comprehensive impact of resource mobilization and equipment scheduling on the adjustment effect.

Conduct real-time monitoring and comprehensive analysis of resource mobilization data, adjustment records and mining results throughout the entire mining process, continuously improve mining strategies and resource allocation plans, and optimize adjustment mechanisms in real time.

Real-time monitoring of resource mobilization data: To achieve real-time monitoring of resource mobilization data, it is first necessary to deploy a sensor network at each mining layer, and use these sensors to collect real-time information on the mining progress of the ore, resource mobilization, and the operating status of the equipment. The software system aggregates these real-time collected data to the central server through the Internet of Things technology, and combines data processing algorithms to continuously track the mining data of each layer. These data include the total amount of mining, equipment utilization, equipment scheduling frequency, etc. The system will process these data in real time and form a monitoring report. The significance of real-time monitoring is to ensure that operators can quickly discover abnormal situations in resource mining and take timely countermeasures to avoid mining delays or waste of resources.

Comprehensive analysis of adjustment records: Comprehensive analysis of adjustment records can be achieved through the storage and analysis of historical data. Each time the equipment scheduling frequency or resource configuration is adjusted, the system automatically records the relevant parameters and operation results and generates an adjustment record table. Through the data analysis module, the system can compare the effects of different adjustment plans and identify which adjustment method is most effective in resource mobilization and equipment scheduling at different levels. This analysis can be achieved through statistical analysis, trend analysis, and machine learning algorithms to summarize effective mining strategies. The purpose of this is to continuously optimize future adjustment strategies and improve mining efficiency in the mine area through the analysis of historical data.

Real-time optimization of mining results: The way to optimize mining results in real time is mainly based on the comprehensive analysis of the aforementioned monitoring data and adjustment records. Through the feedback mechanism of the software system, the system will automatically calculate the deviation of the mining progress based on the real-time monitoring data, and dynamically adjust the mining strategy based on key indicators such as the resource mobilization of each layer and equipment utilization. This optimization can be achieved through an intelligent scheduling system, which automatically optimizes equipment scheduling and resource allocation to ensure that the mining process always remains efficient. If necessary, the system can also reset the scheduling frequency of the equipment or reallocate resources based on real-time feedback data to better adapt to the actual situation in the mining area.

The purpose of continuously improving mining strategies and resource allocation plans is to maximize the efficiency of resource mining in the mining area, avoid resource waste and excessive or improper use of mining equipment. Through real-time monitoring and comprehensive analysis, the system can promptly detect potential problems and make adjustments, thereby improving the production efficiency of the entire mining area. In addition, the real-time optimization and adjustment mechanism can ensure that resource mobilization and equipment scheduling at all levels meet the expected goals, reduce equipment idle time, reduce operating costs, and ultimately improve the overall production efficiency of the mining area. This automated optimization and adjustment mechanism is particularly important in the management of large-scale mining areas, and can effectively cope with complex mining environments and ensure the efficiency and safety of mining operations.

As shown in FIG2 , the resource utilization analysis system using three-dimensional modeling visualization technology includes an ore layer division and sensor deployment module, a resource utilization balance prediction and analysis module, a resource utilization adjustment mechanism construction module, a dynamic feedback and adjustment effect evaluation module, and a mining process monitoring and optimization module;

Ore layer division and sensor deployment module: In the mining process of multi-level open-pit mines, the ore area to be mined is evenly divided into several layers, and a sensor network is deployed around each layer of the ore area to be mined to obtain real-time ore utilization information of each layer of the ore area to be mined;

The resource utilization balance prediction and analysis module analyzes the ore utilization information of each layer of the ore area to be mined, predicts whether the resource utilization of each layer of the ore area to be mined is balanced, and divides each layer of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the prediction results;

The resource utilization adjustment mechanism construction module builds a resource utilization adjustment mechanism based on the division results of various levels of the ore area to be mined, and performs different mining equipment scheduling, mining sequence adjustment and resource allocation optimization for the high optimization area, medium optimization area and low optimization area respectively;

Dynamic feedback and adjustment effect evaluation module, in the process of adjusting each layer by the resource mobilization adjustment mechanism, obtains the dynamic feedback information of the mining area at each level of the ore area to be mined in real time, analyzes this information, evaluates whether the adjustment effect of the adjustment mechanism at each level can meet the expectations, and optimizes the adjustment mechanism according to the evaluation results;

The mining process monitoring and optimization module conducts real-time monitoring and comprehensive analysis of resource mobilization data, adjustment records and mining effects throughout the entire mining process, continuously improves mining strategies and resource allocation plans, and optimizes adjustment mechanisms in real time.

The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (10)

1. A resource utilization analysis method using three-dimensional modeling visualization technology, characterized in that it specifically includes the following steps: In the mining process of multi-level open-pit mines, the ore area to be mined is evenly divided into several levels, and a sensor network is deployed around each level of the ore area to be mined to obtain the ore utilization information of each layer in the ore area to be mined in real time; Analyze the ore utilization information of each layer of the ore area to be mined, predict whether the resource utilization of each layer in the ore area to be mined is balanced, and divide each layer of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the prediction results; According to the division results of various levels of the ore area to be mined, a resource utilization adjustment mechanism is established to carry out different mining equipment scheduling, mining sequence adjustment and resource allocation optimization for the high optimization area, medium optimization area and low optimization area respectively; In the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time, and the information is analyzed to evaluate whether the adjustment effect of the adjustment mechanism at each level can meet the expectations, and the adjustment mechanism is optimized according to the evaluation results; Conduct real-time monitoring and comprehensive analysis of resource mobilization data, adjustment records and mining results throughout the entire mining process, continuously improve mining strategies and resource allocation plans, and optimize adjustment mechanisms in real time. 2. The resource utilization analysis method using three-dimensional modeling visualization technology according to claim 1 is characterized in that the ore utilization information of each layer of the ore area to be mined is analyzed to predict whether the resource utilization between each layer of the ore area to be mined is balanced, and the various layers of the ore area to be mined are divided into high optimization area, medium optimization area and low optimization area according to the prediction results, specifically comprising the following steps: Preprocessing the ore utilization information of each layer in the ore area to be mined; Extracting resource utilization information and mining progress information of each layer from the pre-processed ore utilization information of each layer in the ore area to be mined, and analyzing them after extraction to generate resource utilization coefficients and mining progress indexes of each layer respectively; The resource utilization coefficients and mining progress indexes of each level are used to construct a resource utilization equilibrium model, and the equilibrium coefficients of each level are generated. The equilibrium coefficients of all levels are comprehensively analyzed to generate the equilibrium standard deviation. The generated equilibrium standard deviation is compared with the preset equilibrium standard deviation threshold, and the resource utilization conditions of each level in the ore area to be mined are predicted based on the comparison results. When the prediction result shows that the resource mobilization situation among various levels of the ore area to be mined is unbalanced, a pre-set threshold interval of the balance coefficient is determined, the threshold interval is compared with the balance coefficient of each level, and according to the comparison result, each level of the ore area to be mined is divided into high optimization area, medium optimization area and low optimization area. 3. The resource utilization analysis method using three-dimensional modeling visualization technology according to claim 2 is characterized in that the logic for obtaining the resource utilization coefficient and mining progress index at each level is as follows: The resource utilization information of each layer in the pre-processed ore utilization information of each layer in the ore area to be mined is extracted, including the total amount of mineable ore in each layer of the ore area to be mined, the average distribution density of the ore, and the amount of mined ore at different times within a period of time, and they are marked as , and , Indicates the area of ore to be mined. The total amount of mineable ore at each level, Indicates the area of ore to be mined. The average distribution density of ore in each layer, Indicates the area of ore to be mined. level within a period of time The amount of ore mined at a given time, , , and are all positive integers; Calculate the resource utilization coefficient at each level. The specific calculation formula is as follows: In the formula, For the The resource mobilization coefficient of each level; Extract the mining progress information of each layer from the pre-processed ore utilization information of each layer in the ore area to be mined, including the expected mining volume of each layer in the ore area to be mined, as well as the equipment operation time and equipment efficiency at different times within a period of time, and mark them as , and , Indicates the area of ore to be mined. The expected mining volume at each level, Indicates the area of ore to be mined. level within a period of time The device running time at the moment, Indicates the area of ore to be mined. level within a period of time Equipment efficiency at all times; Calculate the mining progress index at each level. The specific calculation formula is as follows: In the formula, For the The mining progress index at each level. 4. The resource utilization analysis method using three-dimensional modeling visualization technology according to claim 3 is characterized in that the resource utilization coefficients of each level generated are and mining progress index Construct a resource mobilization balance model and generate balance coefficients at each level through weighted summation , equalize the coefficients of all levels Perform comprehensive analysis to generate balanced standard deviation , according to the formula: , and the resulting equilibrium standard deviation The pre-set equilibrium standard deviation threshold Compare and predict whether the resource utilization between various layers in the ore area to be mined is balanced based on the comparison results. The specific comparison analysis is as follows: like , the resource mobilization between the layers of the ore area to be mined is balanced; like , the resource utilization between the layers in the area of ore to be mined is uneven. 5. The resource utilization analysis method using three-dimensional modeling visualization technology according to claim 4 is characterized in that, when the prediction result shows that the resource utilization between the various layers of the ore area to be mined is unbalanced, a preset balance coefficient threshold interval is determined. , the threshold interval Balance coefficients at all levels Compare and divide the various levels of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the comparison results. The specific comparison division is as follows: like , classifying this layer of the ore area to be mined as a low-optimization zone; like , the layer of the ore area to be mined is divided into a medium optimization area; like , dividing this layer of the ore area to be mined into a high optimization zone. 6. The resource mobilization analysis method using three-dimensional modeling visualization technology according to claim 5 is characterized in that a resource mobilization adjustment mechanism is constructed according to the division results of each level of the ore area to be mined, specifically: according to the division results of the high optimization area, the medium optimization area and the low optimization area, different mining equipment scheduling, mining sequence adjustment and resource allocation parameters are set respectively to form a resource mobilization adjustment mechanism; the adjustment mechanism is based on the balance coefficient and equipment efficiency of each area, and automatically determines the adjustment method and range of equipment scheduling and resource allocation through pre-set rules; Different mining strategy adjustments are made to the high optimization area, medium optimization area and low optimization area respectively, specifically including: in the high optimization area, the resource restriction scheduling parameters in the adjustment mechanism are used to reduce the equipment scheduling frequency and reduce resource allocation; in the medium optimization area, the standard scheduling parameters in the adjustment mechanism are maintained, and the current equipment scheduling and resource allocation are not changed; in the low optimization area, the resource increase scheduling parameters in the adjustment mechanism are used to increase the equipment scheduling frequency and improve resource allocation to optimize resource mobilization. 7. The resource mobilization analysis method using three-dimensional modeling visualization technology according to claim 6 is characterized in that, in the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time, and the information is analyzed to evaluate whether the adjustment effect of the adjustment mechanism at each level can meet expectations, and the adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps: In the process of adjusting each layer by the resource mobilization adjustment mechanism, the dynamic feedback information of the mining area at each level of the ore area to be mined is obtained in real time and pre-processed after acquisition; Extract the mining effect information and scheduling efficiency information from the dynamic feedback information of the mining area at each level of the pre-processed ore area to be mined, and analyze it after extraction to generate resource mining compliance coefficients and equipment scheduling efficiency coefficients at each level respectively; The generated resource exploitation compliance coefficients and equipment scheduling efficiency coefficients at each level are used to construct an adjustment effect evaluation model to generate adjustment coefficients at each level. The generated adjustment coefficients at each level are compared with the pre-set adjustment coefficient thresholds at each level. Based on the comparison results, it is evaluated whether the adjustment effect of the adjustment mechanism at each level can meet expectations, and the adjustment mechanism is optimized based on the evaluation results. 8. The resource utilization analysis method using three-dimensional modeling visualization technology according to claim 7 is characterized in that the acquisition logic of resource mining compliance coefficients and equipment scheduling efficiency coefficients at each level is as follows: Extract the mining effect information from the dynamic feedback information of each layer of the pre-processed ore area to be mined, including the actual mined ore volume, expected mined ore volume and corresponding time points at different times during the adjustment process of each layer of the ore area to be mined, and use functions according to the time series and To express, For time point, Indicates the area of ore to be mined. During the adjustment process, The actual amount of ore mined at the time, Indicates the area of ore to be mined. During the adjustment process, The expected amount of ore to be mined at the time, , is a positive integer, defining the time period as ; Calculate the resource exploitation compliance coefficient at each level. The specific calculation formula is as follows: In the formula, For the Resource extraction compliance coefficients at each level; Extract the scheduling efficiency information from the dynamic feedback information of the mining area at each level of the pre-processed ore area to be mined, including the equipment scheduling frequency, actual utilization rate of the equipment, expected utilization rate of the equipment and the corresponding time points at different times during the adjustment process of each level of the ore area to be mined, and use the function to calculate the scheduling efficiency of the equipment according to the time series. , and To express, For time point, Indicates the area of ore to be mined. During the adjustment process, The device scheduling frequency at the moment, Indicates the area of ore to be mined. During the adjustment process, The actual utilization rate of the equipment at the moment, Indicates the area of ore to be mined. During the adjustment process, The expected utilization of the equipment at that time; Calculate the equipment scheduling efficiency coefficient at each level. The specific calculation formula is as follows: In the formula, For the Equipment scheduling efficiency coefficient at each level. 9. The resource utilization analysis method using three-dimensional modeling visualization technology according to claim 8 is characterized in that the resource exploitation compliance coefficients of each level generated are and equipment scheduling efficiency coefficient Construct an adjustment effect evaluation model and generate adjustment coefficients at each level through weighted summation , and the adjustment coefficients of each level generated The adjustment coefficient thresholds of each level are set in advance Compare and evaluate whether the adjustment effect of the adjustment mechanism at each layer can meet expectations based on the comparison results, and optimize the adjustment mechanism based on the evaluation results. The specific comparison and analysis is as follows: like , the adjustment effect of the adjustment mechanism in this layer cannot meet the expectations, and the adjustment mechanism needs to be optimized, including: increasing the scheduling frequency and utilization rate of the mining equipment in this layer, adjusting the resource allocation ratio, and resetting the mining order; combining the real-time feedback data of the mining area to dynamically optimize the scheduling parameters and equipment operation plans; like , the adjustment effect of the adjustment mechanism at this layer can achieve the expected effect, and there is no need to optimize the adjustment mechanism. 10. A resource utilization analysis system using three-dimensional modeling visualization technology, used to implement the resource utilization analysis method using three-dimensional modeling visualization technology described in any one of claims 1 to 9, characterized in that it includes an ore layer division and sensor deployment module, a resource utilization balance prediction and analysis module, a resource utilization adjustment mechanism construction module, a dynamic feedback and adjustment effect evaluation module, and a mining process monitoring and optimization module; Ore layer division and sensor deployment module: In the mining process of multi-level open-pit mines, the ore area to be mined is evenly divided into several layers, and a sensor network is deployed around each layer of the ore area to be mined to obtain real-time ore utilization information of each layer of the ore area to be mined; The resource utilization balance prediction and analysis module analyzes the ore utilization information of each layer of the ore area to be mined, predicts whether the resource utilization of each layer of the ore area to be mined is balanced, and divides each layer of the ore area to be mined into high optimization area, medium optimization area and low optimization area according to the prediction results; The resource utilization adjustment mechanism construction module builds a resource utilization adjustment mechanism based on the division results of various levels of the ore area to be mined, and performs different mining equipment scheduling, mining sequence adjustment and resource allocation optimization for the high optimization area, medium optimization area and low optimization area respectively; Dynamic feedback and adjustment effect evaluation module, in the process of adjusting each layer by the resource mobilization adjustment mechanism, obtains the dynamic feedback information of the mining area at each level of the ore area to be mined in real time, analyzes this information, evaluates whether the adjustment effect of the adjustment mechanism at each level can meet the expectations, and optimizes the adjustment mechanism according to the evaluation results; The mining process monitoring and optimization module conducts real-time monitoring and comprehensive analysis of resource mobilization data, adjustment records and mining effects throughout the entire mining process, continuously improves mining strategies and resource allocation plans, and optimizes adjustment mechanisms in real time.