CN119379250A - Intelligent winter maintenance method for tower foundation integrating environmental perception algorithm - Google Patents

Abstract

The present invention provides a winter intelligent maintenance method for a tower foundation integrating an environmental perception algorithm, which relates to the field of data processing technology. The image captured by a visual camera is identified by computer vision technology to obtain scene data of a target tower. The target tower is perceived by an environmental sensor array to obtain basic environmental data of the target tower. The scene data and basic environmental data are integrated, and a tower foundation status database is input into a simulation prediction space to predict the tower status. According to the tower prediction results combined with historical maintenance records, a tower maintenance plan is generated to perform intelligent tower maintenance. The technical problem that the tower environment is changeable in winter and preventive maintenance cannot be performed in time, resulting in low efficiency of maintenance work, is solved in the prior art. The intelligent preventive maintenance of the tower in winter is achieved, the maintenance efficiency is improved, and the technical effect of reducing the safety risks caused by the changeable winter environment is achieved.

Description

Iron tower foundation intelligent maintenance method integrating environment sensing algorithm in winter

Technical Field

The invention relates to the technical field of data processing, in particular to an intelligent maintenance method for an iron tower foundation in winter, which is integrated with an environment sensing algorithm.

Background

With the increasing demand of energy and the increasing complexity of power transmission networks in modern society, iron towers are important support structures for power transmission and communication networks, and operational safety and stability of iron towers are of great concern. Particularly in winter, severe environmental conditions such as severe cold, wind and snow and the like have obvious influence on the structure of the iron tower. Traditional iron tower maintenance mainly relies on periodic inspection and manual maintenance, and this mode not only consumes a large amount of manpower and material resources, but also is difficult to realize real-time monitoring and preventive maintenance due to the variability of environmental conditions.

In the prior art, the iron tower has changeable environments in winter, maintenance mainly depends on regular inspection and manual maintenance, and preventive maintenance cannot be performed in time, so that the technical problem of low efficiency of maintenance work is caused.

Disclosure of Invention

The application provides an intelligent maintenance method for an iron tower foundation in winter, which is used for solving the technical problems that in the prior art, the iron tower foundation in winter has changeable environment, maintenance mainly depends on periodic inspection and manual maintenance, preventive maintenance cannot be performed in time, and the maintenance work efficiency is low.

In view of the above problems, the application provides an intelligent maintenance method for a foundation of an iron tower, which is integrated with an environment sensing algorithm, and the intelligent maintenance method comprises the steps of identifying images captured by a visual camera through a computer visual technology to obtain scene data of a target iron tower, wherein the scene data comprises an iron tower structure and an iron tower appearance state, sensing the environment data of the target iron tower through an environment sensor array to obtain foundation environment data of the target iron tower, integrating the scene data with the foundation environment data to form a foundation state database of the iron tower, inputting the foundation state database of the iron tower into a simulation prediction space to predict the state of the iron tower to obtain a prediction result of the iron tower, and generating a maintenance scheme of the iron tower according to the prediction result of the iron tower and a historical maintenance record.

One or more technical schemes provided by the application have at least the following technical effects or advantages:

The method comprises the steps of identifying images captured by a visual camera through a computer visual technology to obtain scene data of a target iron tower, wherein the scene data comprise structural characteristics of the iron tower and appearance states of the iron tower, sensing the environment data of the target iron tower through an environment sensor array to obtain basic environment data of the target iron tower, fusing the scene data and the basic environment data to form an iron tower basic state database, inputting the iron tower basic state database into a simulation prediction space to perform iron tower state prediction to obtain an iron tower prediction result, combining historical maintenance records according to the iron tower prediction result to generate an iron tower maintenance scheme, and performing intelligent maintenance of the iron tower. The intelligent preventive maintenance of the iron tower in winter is achieved, the maintenance efficiency is improved, and the technical effect that safety risks exist due to changeable winter environments is reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the drawings needed in the description of the embodiments, which are merely examples of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of an intelligent maintenance method for iron tower foundations in winter, which is provided by the application and is fused with an environment perception algorithm;

Fig. 2 is a schematic flow chart of forming a database of the basic state of the iron tower in the intelligent maintenance method of the iron tower foundation in winter, which is provided by the application and is fused with the environment perception algorithm.

Detailed Description

The application provides an intelligent maintenance method for an iron tower foundation in winter, which is used for solving the technical problems that in the prior art, the iron tower foundation in winter has changeable environment, maintenance mainly depends on periodic inspection and manual maintenance, preventive maintenance cannot be performed in time, and the maintenance work efficiency is low. The intelligent preventive maintenance of the iron tower in winter is achieved, the maintenance efficiency is improved, and the technical effect that safety risks exist due to changeable winter environments is reduced.

In the following, the technical solutions of the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention, and that the present invention is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.

As shown in fig. 1, the application provides an intelligent maintenance method for an iron tower foundation in winter, which is integrated with an environment sensing algorithm, and comprises the following steps:

And identifying the image captured by the visual camera through a computer vision technology to obtain scene data of the target iron tower, wherein the scene data comprises an iron tower structure and an iron tower appearance state.

Specifically, the related information of the target iron tower is automatically captured through high-definition visual cameras arranged around the iron tower. For example, the vision camera is an infrared camera to enhance visibility during night or during severe winter weather. Then, preprocessing the collected original image, removing image noise, adjusting brightness contrast and the like, so that the structural and appearance details of the iron tower are more obvious. And identifying the preprocessed image by utilizing a target detection algorithm in a computer vision technology, and extracting and analyzing key characteristics in the image, wherein the geometric characteristics of the tower structure part in the image, including angles, lengths, surface curvatures and the like, are extracted to form the structural characteristics of the tower, and the structural characteristic data of the tower can help judge whether deformation, fracture or other structural problems exist in the tower. And analyzing the surface texture, color change, corrosion condition and the like of the target iron tower for the appearance state of the target iron tower, extracting the appearance state characteristics of the iron tower, and reflecting whether the iron tower has the problems of surface damage, corrosion expansion and the like through the appearance state of the iron tower. And integrating all the identified characteristic data to generate complete scene data of the target iron tower, wherein the scene data comprises the structural characteristics of the iron tower and the appearance state of the iron tower. Based on scene data captured by the camera images, the structure and the appearance of the iron tower can be accurately identified and analyzed, comprehensive basic information can be provided for subsequent intelligent maintenance decision, and safe operation of the iron tower is ensured.

And sensing the environmental data of the target iron tower through the environmental sensor array to obtain the basic environmental data of the target iron tower.

The method comprises the steps of obtaining basic environment data of a target iron tower through environment data sensing of the target iron tower through an environment sensor array, determining monitoring data, determining sensor types according to the monitoring data, wherein the sensor types comprise a temperature sensor, a humidity sensor, an anemoscope and a snow gauge, planning layout of a plurality of sensors according to structural characteristics of the iron tower and the monitoring data to generate the environment sensor array, and performing real-time sensing acquisition on the environment data of the target iron tower based on the environment sensor array to form the basic environment data.

Specifically, firstly, key environment monitoring data of a target iron tower are determined, in winter, the brittleness of iron tower base materials (such as concrete and steel) is increased, frost cracking or deformation is easy to occur at low temperature, the low-temperature environment can be timely found and early warned by monitoring the temperature, the humidity represents the moisture content in the air and can influence the corrosion condition of the iron tower, and the wind speed and the snowfall have direct influence on the structural stability of the iron tower, such as wind load and snow pressure monitoring data. Thus, the monitoring data is determined to include temperature, humidity, wind speed, and snowfall. Then, according to the determined monitoring data type, a suitable sensor type is selected, wherein the sensor type refers to equipment for monitoring specific environmental parameters, for example, a temperature sensor is needed for temperature monitoring, a humidity sensor is used for humidity monitoring, an anemometer is used for measuring wind speed and wind direction, and a snowmeter is used for measuring snowfall. And then, according to the structural characteristics of the target iron tower and the monitoring data, specific installation positions and arrangement modes of the sensors are formulated, and the comprehensiveness and the accuracy of the data are ensured. After the layout of the sensors is completed, an environment sensor array is generated, wherein the sensor array refers to a monitoring network which is formed by the sensors according to a specific layout and a specific connection mode and works cooperatively, and environmental data around the iron tower can be acquired in real time. After the sensor array is deployed, the environment where the target iron tower is located is subjected to real-time sensing monitoring and data acquisition, wherein real-time sensing means that the sensor can continuously and uninterruptedly acquire environment data, and the sensor can immediately sense and record when any change occurs. The data acquisition comprises environmental parameters of multiple dimensions such as temperature, humidity, wind speed, wind direction, snowfall and the like, and based on the environmental sensor array, real-time monitoring and data acquisition of the surrounding environment of the target iron tower are realized, so that the basic environmental data are formed. By obtaining basic environment data, winter climate conditions and change trends of the environment where the target iron tower is located can be reflected, the influence of the environment on the iron tower state can be understood, and environmental factors which possibly cause defects can be identified.

And fusing the scene data and the basic environment data to form an iron tower basic state database.

Specifically, visual information and environmental information are comprehensively considered, namely the obtained scene data and the basic environmental data are fused to form an iron tower basic state database which comprehensively reflects the state of a target iron tower, and data support is provided for intelligent maintenance of the follow-up iron tower.

In one embodiment, as shown in fig. 2, the scene data and the basic environment data are fused to form an iron tower basic state database, the method comprises the steps of synchronously sequencing the scene data and the basic environment data according to acquisition timestamps, analyzing the scene data to obtain iron tower scene features, wherein the iron tower scene features comprise iron tower states, defect types and defect positions, analyzing the basic environment data to obtain basic environment features, the basic environment features comprise environment parameter values and change trends, performing association analysis on the iron tower scene features and the basic environment features according to the timestamp information after synchronous sequencing to generate association analysis results under a plurality of time frames, and storing the iron tower scene features, the basic environment features and the association analysis results under the plurality of time frames into the iron tower basic state database.

Specifically, according to the time stamp information of the acquired data, namely the specific time point when the data is acquired, the scene data and the basic environment data are subjected to time stamp synchronous sequencing, so that the time alignment of the scene data and the basic environment data is ensured, and the data analysis error caused by inconsistent time is avoided. After synchronous sequencing, the scene data are subjected to deep analysis, scene characteristic information comprising the iron tower state, defect types and defect positions is extracted from the scene data, wherein the iron tower state is like the overall health condition of the iron tower, the defect types are rust, cracks, deformation and the like, the defect positions are specific to the iron tower, problems occur at the positions, and basic data are provided for subsequent iron tower state evaluation by obtaining the iron tower scene characteristics. And then analyzing the basic environment data, extracting basic environment characteristics from the basic environment data, wherein the basic environment characteristics comprise environment parameter values such as specific values of current temperature, humidity, wind speed and the like, and change trends such as whether the temperature is gradually increased, whether the humidity is periodically changed and the like, and providing a data basis for the iron tower running environment and the influence of the iron tower running environment on the iron tower state by obtaining the basic environment characteristics.

Then, by analyzing the data of the scene features and the basic environment features under the same time stamp, the influence of environmental changes on the state of the iron tower, for example, the influence of environmental changes such as temperature, humidity, snowfall and the like on the state of the iron tower such as corrosion aggravation, crack expansion and the like is identified. In the analysis process, correlation analysis results under a plurality of time frames are generated. A time frame refers to a data segment divided in units of time. In each time frame, the correlation analysis will show how the pylon status and environmental features interact, change over time, and identify key influencing factors and trends. And finally, storing the iron tower scene characteristics, the basic environment characteristics and the association analysis results under a plurality of time frames into a basic state database of the iron tower. And carrying out association analysis on the iron tower scene characteristics and the environment characteristics through the time stamp, so that the relation between the iron tower state change and the environment conditions can be revealed, and storing the iron tower scene characteristics, the basic environment characteristics and the association analysis results to form an iron tower basic state database, thereby providing a basis for the state prediction of the subsequent iron tower and improving the accuracy and the instantaneity of risk prediction.

And inputting the iron tower basic state database into a simulation prediction space to perform iron tower state prediction, and obtaining an iron tower prediction result.

Further, inputting the fused iron tower basic state database into a simulation prediction space to perform iron tower state prediction to obtain an iron tower prediction result, wherein the simulation prediction space comprises a time sequence analysis technology and comprises the steps of extracting historical iron tower scene characteristics, historical basic environment characteristics and historical association analysis results from the iron tower basic state database; according to a predefined time window, the historical iron tower scene characteristics and the historical basic environment characteristics are segmented into a plurality of time sequence data sets according to a time sequence, a prediction model is trained by using the plurality of time sequence data sets to obtain a time sequence prediction model, and the trained time sequence prediction model is deployed in a simulation prediction space.

Specifically, the iron tower basic state database is a comprehensive database, and stores state information of the iron tower in different time periods, including scene characteristics and environment characteristics of the iron tower and correlation analysis results between the scene characteristics and the environment characteristics. And extracting relevant historical data from the iron tower basic state database, wherein the historical data comprise historical iron tower scene characteristics, historical basic environment characteristics and historical association analysis results. The continuous history data is divided into separate data sets, i.e. time series data sets, over a plurality of time periods in time sequence according to a predefined time window. The time window is a time period for dividing the data, and the length of the time window depends on the frequency of the change of the state of the iron tower and the predicted requirement, such as a week, a half month and the like. The time sequence data sets are data sets segmented according to a time window, and each data set comprises iron tower scene characteristics, environment characteristics and associated analysis results in a time period.

The segmented plurality of time series data sets are utilized to train a prediction model, which is a data model dedicated to predicting time-based variations. The state of the pylon in the future can be predicted by learning a time series pattern in the historical data, optionally the prediction model comprises ARIMA, LSTM, etc. The prediction model training is a process of data input and model parameter adjustment, and the parameters of the prediction model are adjusted by repeatedly inputting a time sequence data set, so that the time dependency relationship and the change trend in the prediction data can be accurately captured and predicted, the prediction error is continuously minimized, the model can be ensured to effectively predict the future iron tower state until the model output reaches convergence, and the trained time sequence prediction model is obtained. And deploying the trained time sequence prediction model in a simulation prediction space, so that the time sequence prediction model can execute a prediction task in practical application. The deployed model can receive new data in real time, conduct state prediction and generate a prediction result of the future iron tower state. Time dependency in the data is captured through time sequence analysis, so that the prediction model can be ensured to effectively predict the future iron tower state.

And inputting the iron tower basic state database into a simulation prediction space, and predicting the iron tower state by using the deployed time sequence prediction model to finally obtain an iron tower prediction result. The iron tower prediction result is output generated by a time sequence prediction model according to current iron tower scene data and environment basic data, and comprises specific description of future iron tower states (such as possible defect types and defect positions) and prediction of influence of environment conditions on the iron tower. By training the time prediction model and deploying the simulation prediction space, the future iron tower state can be effectively predicted, the prediction accuracy and reliability are improved, and timely decision support is provided for winter maintenance of the iron tower, so that the safe operation of the iron tower is ensured.

And generating an iron tower maintenance scheme according to the iron tower prediction result and the historical maintenance record, and performing intelligent maintenance on the iron tower.

Further, according to the iron tower prediction result, a historical maintenance record is combined to generate an iron tower maintenance scheme, the iron tower maintenance scheme comprises the steps of collecting and arranging the historical maintenance record of an iron tower, wherein the historical maintenance record comprises historical maintenance, historical repair and inspection reports, a multi-objective optimization algorithm is utilized to generate the iron tower maintenance scheme by combining the iron tower prediction result and the historical maintenance record, the iron tower maintenance scheme is divided into a plurality of stages, each stage comprises a specific maintenance task and a specific schedule, and resources and priorities are distributed to the maintenance scheme of each stage according to the iron tower prediction result, the scene data and the basic environment data.

Specifically, a database query tool is used for extracting and sorting historical maintenance records of the iron tower from electronic databases such as an electric company database, an iron tower maintenance management database and the like, the historical maintenance records are mainly divided into three types of historical maintenance, historical repair and inspection reports, the historical maintenance is maintenance work in a designated period, such as daily maintenance operations of cleaning, lubrication, fastening and the like, and the records help to know the conventional maintenance condition of the iron tower. Historical repair involves repair operations performed on iron towers in the past, including repairing cracks, replacing damaged parts, etc., to reflect specific problems and repair situations of the iron tower that occur at different points in time. Inspection reports are detailed reports for evaluating the status of the iron tower, and are usually generated after periodic inspection by professionals, and include information such as structural conditions of the iron tower, potential risks and the like.

After the historical maintenance record of the iron tower is obtained, combining the iron tower prediction result obtained through the simulation prediction space, utilizing a multi-objective optimization algorithm, comprehensively considering a plurality of objectives such as risk, time, resources, cost and the like, and generating an iron tower maintenance scheme under the condition of meeting all the objectives. For example, a multi-objective optimization algorithm balances the relationship between repair costs and maintenance effects, ensuring that optimal maintenance effects are achieved within limited resources. The iron tower maintenance scheme is a plan generated by an optimization algorithm and comprises all maintenance work arrangements of the iron tower for a period of time in the future. The iron tower maintenance scheme not only considers the current state and future prediction of the iron tower, but also synthesizes the experience training of the history maintenance record, thereby ensuring the comprehensiveness and rationality of the scheme.

In order to facilitate the orderly execution of the maintenance scheme, the iron tower maintenance scheme is divided into a plurality of stages, and each stage has a specific maintenance task and schedule. For example, the first phase may focus on emergency repair and preventative maintenance, and the second phase may focus on routine inspection and small scale repair. The maintenance tasks of each stage are formulated according to the specific conditions and the prediction results of the iron tower, including the activities of checking, repairing, monitoring and the like, and detailed time schedule is formulated for each stage, so that when and what maintenance activities are performed are clear, each task is ensured to be propelled according to the plan, and resource conflict and time delay are avoided.

And finally, distributing resources for the maintenance scheme of each stage and setting priority according to the iron tower prediction result, the scene data and the basic environment data. The maintenance work needs to put in various resources such as manpower, materials, equipment and the like. According to the task demands of each stage, the resources are reasonably distributed, and the key tasks are ensured to be supported by sufficient resources. In the curing process, the importance and the emergency degree of different tasks are different. And setting priority for the tasks in each stage according to the prediction result and the environmental data, and ensuring that the most critical and urgent tasks are preferentially processed. For example, if the prediction indicates that a component may be about to fail, then repair work for that component should be prioritized. The history maintenance record is combined with the future prediction result of the iron tower, a staged maintenance scheme is generated by utilizing a multi-objective optimization algorithm, resources are reasonably distributed and priorities are set for each stage, preventive maintenance can be timely carried out, maintenance work is more orderly, priority treatment of key tasks is ensured, resources are optimally configured, timeliness and comprehensiveness of the maintenance scheme are improved, and effectiveness of execution of the maintenance scheme is ensured.

Further, combining the iron tower prediction result and the historical maintenance record, generating an iron tower maintenance scheme by utilizing a multi-objective optimization algorithm, wherein the iron tower maintenance scheme comprises the steps of obtaining iron tower maintenance requirement information according to the iron tower prediction result, traversing the historical maintenance record based on the iron tower maintenance requirement information to generate N maintenance schemes, wherein N is a positive integer greater than 1, and determining an optimal maintenance scheme by utilizing the multi-objective optimization algorithm based on the N maintenance schemes to obtain the iron tower maintenance scheme.

Specifically, the prediction information of the future state of the iron tower obtained through the simulation prediction model comprises possible defects, structural damage, environmental influence and the like. A specific maintenance requirement is extracted from the prediction, the maintenance requirement indicating which parts need maintenance, repair or further inspection. For example, the prediction may indicate that a critical node is at risk of fatigue damage, and the corresponding maintenance requirement may include structural reinforcement or periodic monitoring of the node. After the maintenance requirement information is obtained, all relevant historical maintenance records are retrieved and analyzed one by one, including repair, maintenance, inspection and the like, so as to extract useful information therefrom. For example, in the past some form of repair would have performed well in a similar situation, and this information could then be used to generate a new maintenance regimen. And generating a plurality of different maintenance schemes (N is a positive integer greater than 1) according to the traversed result and the current maintenance requirement information, wherein each scheme may be based on different strategies, resource allocation modes or technical methods. For example, some schemes focus on preventative maintenance, while others may focus more on emergency repair. After N curing schemes are generated, an optimal curing scheme is determined by utilizing a multi-objective optimization algorithm. For example, trade-offs are made between multiple objectives of cost, time, risk, resource utilization, etc., analyzing the performance of each maintenance solution under these objectives, and finding the solution that best balances all objectives. And finally, the optimal scheme, namely the iron tower maintenance scheme, is determined through screening of an optimization algorithm. The iron tower maintenance scheme can complete tasks with minimum cost, shortest time and optimal resource allocation while meeting all maintenance requirements. The optimal maintenance scheme is determined by utilizing a multi-objective optimization algorithm based on the N maintenance schemes, so that the efficiency and pertinence of intelligent maintenance of the iron tower foundation in winter can be remarkably improved, the resource allocation is optimized, and the intelligent level is improved.

Further, according to the structural characteristics of the iron tower and the monitoring data, planning the layout of a plurality of sensors to generate the environment sensor array, wherein the environment sensor array is generated by obtaining the structural characteristics of a target iron tower according to the scene data, dividing the priority of monitoring points of the iron tower according to the influence degree of the structural characteristics and the monitoring data, and arranging the plurality of sensors according to the division result.

Specifically, according to the identified scene data, the structural characteristics of the target iron tower are extracted and obtained, wherein the structural characteristics refer to physical properties and geometric characteristics of the iron tower, including height, node positions, supporting points, distribution of key parts (such as connecting nodes, tower tops and tower bases) and the like of the iron tower, and the structural characteristics can directly influence the stability and safety of the iron tower. After the structural characteristics of the iron tower are obtained, the influence degree of the monitoring data (such as wind speed, temperature, humidity and the like) on the structural characteristics (different parts) of the iron tower is achieved. For example, strong winds may have a greater impact on the top of the pylon, while temperature variations may have a greater impact on the stress distribution of the connection nodes. By analyzing the monitoring data, it can be determined which environmental factors have the most significant effect on which structural parts. And determining which monitoring points are more critical according to the structural characteristics and the influence degree of the monitoring data, and dividing the priority of the monitoring points of the iron tower according to the influence degree. Monitoring points of high priority are typically those locations in the pylon structure where the load bearing pressure is greater or more susceptible to environmental factors, such as the top of the tower, the main load bearing structure, critical connection points, etc. And (5) distributing the sensors according to the priority classification result of the monitoring points, and finally generating an environment sensor array. The environment sensor array is a complete monitoring network and is composed of a plurality of sensors distributed at each key position of the iron tower, the sensors can collect environment data in real time, the comprehensive monitoring of the iron tower structure is ensured, the comprehensiveness and accuracy of the monitoring data are further improved, and reliable data support is provided for the follow-up winter maintenance of the iron tower.

Further, the priority of the iron tower monitoring points is divided according to the structural characteristics and the influence degree of the monitoring data, the priority of the iron tower monitoring points is divided according to the stress concentration area of the iron tower structure, the part which is easily influenced by environmental factors and the historical fault multiple points, the priority comprises three stages, wherein the primary monitoring points are positioned at key parts of the iron tower and comprise tower feet, connecting nodes and tower tips, the secondary monitoring points are positioned at environment-variable parts and comprise windward surfaces and snow cover surfaces, and the tertiary monitoring points are positioned at the historical fault multiple points of the iron tower.

Specifically, based on the design and structural analysis of the iron tower, the stress concentration area of the iron tower structure is identified, wherein the stress concentration area of the iron tower structure is a part bearing the maximum stress or strain in the iron tower structure and comprises key parts such as a tower foot, a connecting node, a tower tip and the like. These areas are the most likely sites to be fatigued and damaged due to the large load carrying capacity and concentrated load. Thus, these critical locations are identified as primary priority points for pylon monitoring. Next, according to the environmental data analysis and the historical environmental conditions, the part of the iron tower, which is easily affected by the environmental factors, is determined, and the part, which is easily affected by the environmental factors, is positioned on the windward side and the snow surface of the iron tower. The windward side refers to the part of the iron tower which directly faces the wind direction, and the iron tower is exposed to strong wind for years, so that wind erosion or structural fatigue can be caused. Snow cover refers to the area covered by snow in winter, and the weight of snow may create additional load on the structure. Thus, these environmental variant sites are categorized as secondary monitoring points. And identifying historical fault multiple points on the iron tower by analyzing the historical fault data, wherein the historical fault multiple points refer to parts of the iron tower where problems frequently occur in the past use. These faults may be associated with specific environmental conditions, structural defects, or long-term fatigue accumulation. Although these points may not be in areas of greatest structural stress or environmental impact, they are classified as tertiary monitoring points because historical data indicates a high probability of failure for these locations. Monitoring of these points helps to prevent the same problem from reoccurring. After the priority division is finished, sensors are arranged and monitoring is started, and meanwhile, the priority and the monitoring strength of the monitoring points are dynamically adjusted according to real-time monitoring data and the running condition of the iron tower. By dividing the priorities of the iron tower monitoring points, the sensor allocation monitoring resources can be optimized, and the most effective utilization of the resources is ensured. And the method can realize comprehensive and real-time mastering of the state of the iron tower, acquire more accurate and comprehensive monitoring data, provide powerful data support for winter maintenance and management of the iron tower, and improve the accuracy and timeliness of management.

The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.

The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.

Claims (8)

1. A winter intelligent maintenance method for a tower foundation integrating an environmental perception algorithm, characterized in that the method comprises: The image captured by the visual camera is recognized by computer vision technology to obtain scene data of the target tower, wherein the scene data includes the structural features of the tower and the appearance status of the tower; Through the environmental sensor array, the environmental data of the target tower is sensed to obtain the basic environmental data of the target tower; The scene data and the basic environment data are integrated to form a tower basic status database; Inputting the tower foundation status database into the simulation prediction space to perform tower status prediction and obtain tower prediction results; Based on the tower prediction results and historical maintenance records, a tower maintenance plan is generated to carry out intelligent tower maintenance. 2. The winter intelligent maintenance method for the iron tower foundation integrating the environmental perception algorithm as claimed in claim 1 is characterized in that the environmental data of the target iron tower is perceived by the environmental sensor array to obtain the basic environmental data of the target iron tower, including: Determining monitoring data, wherein the monitoring data includes temperature, humidity, wind speed and snowfall; Determine the sensor type according to the monitoring data, the sensor type including a temperature sensor, a humidity sensor, an anemometer and a snow gauge; According to the structural characteristics of the tower and the monitoring data, planning the layout of multiple sensors and generating the environmental sensor array; Based on the environmental sensor array, the environmental data of the target tower is sensed and collected in real time to form the basic environmental data. 3. The winter intelligent maintenance method for a tower foundation integrating an environmental perception algorithm as claimed in claim 2 is characterized in that, according to the structural characteristics of the tower and the monitoring data, the layout of multiple sensors is planned to generate the environmental sensor array, including: According to the scene data, the structural characteristics of the target tower are obtained; Prioritizing tower monitoring points according to the structural characteristics and the degree of influence of the monitoring data; A plurality of sensors are arranged according to the division result to generate the environmental sensor array. 4. The winter intelligent maintenance method for the iron tower foundation integrating the environmental perception algorithm according to claim 3 is characterized in that the priorities of the iron tower monitoring points are divided according to the structural characteristics and the influence degree of the monitoring data, including: Prioritize tower monitoring points based on stress concentration areas of the tower structure, parts that are susceptible to environmental factors, and historical fault-prone points; The priority level includes three levels, among which the first-level monitoring points are located at the key parts of the tower, including the tower base, connection nodes, and tower top; the second-level monitoring points are located at parts with variable environment, including the windward side and snow-covered side; the third-level monitoring points are located at points where the tower has a history of frequent failures. 5. The winter intelligent maintenance method for the iron tower foundation integrating the environment perception algorithm according to claim 1 is characterized in that the scene data and the basic environment data are integrated to form an iron tower foundation status database, including: Synchronously sorting the scene data and the basic environment data according to the acquisition timestamp; Analyze the scene data to obtain tower scene features, where the tower scene features include tower status, defect type, and defect location; Analyze the basic environmental data to obtain basic environmental characteristics, wherein the basic environmental characteristics include environmental parameter values and change trends; According to the synchronously sorted timestamp information, correlation analysis is performed on the tower scene features and the basic environment features to generate correlation analysis results under multiple time frames; The tower scene characteristics, the basic environment characteristics and the correlation analysis results in multiple time frames are stored in the tower basic status database. 6. The winter intelligent maintenance method for the iron tower foundation integrating the environment perception algorithm according to claim 5 is characterized in that the integrated iron tower foundation state database is input into the simulation prediction space to perform the iron tower state prediction and obtain the iron tower prediction result, which comprises: Extracting historical tower scene features, historical basic environment features and historical correlation analysis results from the tower foundation status database; According to a predefined time window, the historical tower scene features and the historical basic environment features are divided into multiple time series data sets in chronological order; Using the multiple time series data sets, training a prediction model to obtain a time series prediction model; The trained time series prediction model is deployed in the simulation prediction space. 7. The winter intelligent maintenance method for iron tower foundation integrating environmental perception algorithm as claimed in claim 1 is characterized in that, according to the iron tower prediction result combined with historical maintenance records, an iron tower maintenance plan is generated, including: Collect and organize the historical maintenance records of the tower, including historical maintenance, historical repair and inspection reports; Combining the tower prediction results with the historical maintenance records, generating a tower maintenance plan using a multi-objective optimization algorithm; Dividing the tower maintenance program into multiple stages, each stage including specific maintenance tasks and schedules; Resources and priorities are allocated to the maintenance plan for each stage based on the tower prediction results, the scenario data and the basic environment data. 8. The method for winter intelligent maintenance of iron tower foundations integrating environmental perception algorithm according to claim 7 is characterized in that, combining the iron tower prediction results and the historical maintenance records, a multi-objective optimization algorithm is used to generate an iron tower maintenance plan, including: Obtaining tower maintenance demand information based on the tower prediction result; Based on the tower maintenance demand information, traverse the historical maintenance records and generate N maintenance plans, where N is a positive integer greater than 1; Based on the N maintenance plans, a multi-objective optimization algorithm is used to determine the optimal maintenance plan to obtain the tower maintenance plan.