CN117455242B - A water conservancy management system based on digital twin - Google Patents
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Abstract
本发明公开了一种基于数字孪生的水利管理系统，具体涉及水利管理技术领域，一种基于数字孪生的水利管理系统，包括数据采集模块、数据处理模块、数据分析模块，通过获取传感器的自身性能信息以及工作环境信息，建立数据处理模型，生成传感器工作状态异常指数，对不同工作状态下传感器采集的数据进行风险标记，并进一步通过采集风险数据的质量信息以及数字孪生模型运行状态的性能信息，生成数字孪生模型预测风险指数在数字孪生模型预测前对其进行风险评估，并确定是否生成预警信号，避免风险数据的质量过差，进一步增加数字孪生模型在运行状态较差时的数据处理负担，导致预测结果的不稳定性减少不必要的预测任务使数字孪生模型的运行更高效。
The present invention discloses a water conservancy management system based on digital twins, and specifically relates to the technical field of water conservancy management. A water conservancy management system based on digital twins comprises a data acquisition module, a data processing module and a data analysis module. By acquiring the performance information and working environment information of the sensor itself, a data processing model is established, an abnormal index of the working state of the sensor is generated, risk marking is performed on the data collected by the sensor under different working states, and further by collecting the quality information of the risk data and the performance information of the running state of the digital twin model, a prediction risk index of the digital twin model is generated. A risk assessment is performed on the digital twin model before prediction, and it is determined whether to generate an early warning signal to avoid the poor quality of the risk data, further increase the data processing burden of the digital twin model when the running state is poor, resulting in the instability of the prediction result, reducing unnecessary prediction tasks, and making the operation of the digital twin model more efficient.
Description
Technical Field
The invention relates to the technical field of water conservancy management, in particular to a water conservancy management system based on digital twinning.
Background
The digital twin technology is a simulation process integrating a plurality of disciplines, a plurality of physical variables, multidimensional measurement and event probability statistics, and reflects the full life cycle of corresponding entity equipment by establishing mapping in a virtual space. The digital twin-based water conservancy management system is an advanced water resource management tool, various water conservancy object indexes are obtained by constructing a sensor network by means of a large number of sensors, and various water conservancy object index data are input into a digital twin model to be used for predicting future water resource conditions, flood risks, drought conditions and the like.
At present, the water conservancy management system based on digital twinning is not perfect enough to monitor the state of the sensor, and in general, only when the sensor has obvious fault early warning, processing measures can be taken. The state of the sensor may have been slipped down before that, which results in difficulty in controlling the quality of the data acquired by the sensor, and although the digital twin model may perform preprocessing on the data, if the performance of the digital twin model is poor and the quality of the data is also worse, the data cannot be ensured to be effectively processed, and risk analysis is not performed on the data before model prediction, which results in errors in the prediction result.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides a digital twin-based water conservancy management system to solve the above-mentioned problems.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a water conservancy management system based on digital twinning comprises a data acquisition module, a data processing module and a data analysis module, wherein the modules are connected through signals;
the data acquisition module acquires own performance information and working environment information of the sensor, and transmits the own performance information and the working environment information to the data processing module after acquisition;
the data processing module is used for carrying out normalization processing on the acquired self-performance information and working environment information of the sensor, establishing a data processing model and generating an abnormal index of the working state of the sensor;
the data analysis module compares the abnormal index of the working state of the sensor with a reference threshold value of the abnormal index of the working state of the sensor and marks the data acquired by the sensor under different working states;
if the abnormal index of the working state of the sensor is larger than or equal to the reference threshold value of the abnormal index of the working state of the sensor, a risk assessment signal is generated, and data acquired by the sensor in the time T are marked as risk data.
In a preferred embodiment, the data acquisition module acquires self-performance information of the sensor and working environment information, wherein the self-performance information of the sensor comprises a response time fluctuation coefficient and an abnormal calibration frequency value, the working environment information comprises a water pressure abnormal deviation value, and the response time fluctuation coefficient, the abnormal calibration frequency value and the water pressure abnormal deviation value are respectively marked as BD, YX and SY after acquisition.
In a preferred embodiment, the response time fluctuation coefficient acquisition logic is as follows:
acquiring response time of a sensor in a time period T, and setting a reference threshold value of the response time;
when the response time of the sensor is greater than a reference threshold of the response time, marking the sensor as t i, wherein i represents the number of times the abnormal state occurs in the response time of the sensor, i= {1, 2,..;
The response time fluctuation coefficient is calculated by the following expression, bd=bc/PJ, wherein BZ is the standard deviation of response time in abnormal state, and the expression is that PJ is the average value of response time in abnormal state, and the expression is
In a preferred embodiment, the acquisition logic for the outlier calibration frequency values is as follows:
the calibration times of the sensor in the T time period are obtained, and the average calibration frequency is calculated, wherein the expression is as follows: ZC is the total number of times of calibration of the sensor in a time period T, and T is the time period for obtaining the number of times of calibration of the sensor;
setting an average calibration frequency reference threshold, and if the average calibration frequency is greater than or equal to the average calibration frequency reference threshold, marking the average calibration frequency as JZ ', JZ' representing the average calibration frequency in an abnormal state;
The acquisition expression of the abnormal calibration frequency value is yx=jz'.
In a preferred embodiment, the acquisition logic of the hydraulic anomaly deviation value is as follows:
setting a reference threshold range of water pressure under the working state of the sensor, and marking the reference threshold range as SY z min～SYz max;
acquiring working water pressure of a sensor in a T time period, marking the working water pressure as SY, and calculating a water pressure abnormality deviation value, wherein the expression is as follows Wherein h represents the number of times the sensor operating water pressure exceeds a reference threshold range, h= {1, 2,..and v }, h is a positive integer, And q x,qy is the starting time and the ending time of each sensor operating water pressure exceeding the reference threshold range.
In a preferred embodiment, the data acquisition module is further configured to acquire quality information of risk data and performance information of an operation state of the digital twin model, and transmit self performance information and working environment information to the data processing module after the acquisition;
the quality information of the risk data comprises data quality anomaly coefficients, and the performance information of the digital twin model operation state comprises operation performance coefficients.
In a preferred embodiment, the data processing module is further configured to normalize the quality information of the acquired risk data and the performance information of the running state of the digital twin model, and build a data processing model to generate a predicted risk index of the digital twin model.
In a preferred embodiment, the data analysis module is further configured to compare the digital twin model predicted risk index with a digital twin model predicted risk index reference threshold, and perform risk assessment on the digital twin model prior to prediction;
If the predicted risk index of the digital twin model is greater than or equal to the reference threshold value of the predicted risk index of the digital twin model, generating an early warning signal, and determining whether to predict the future water resource supply and demand condition by a manager.
The invention has the technical effects and advantages that:
1. According to the invention, the data processing model is established by acquiring the self performance information and the working environment information of the sensor, the abnormal index of the working state of the sensor is generated, the abnormal index of the working state of the sensor is compared with the reference threshold value of the abnormal index of the working state of the sensor, the risk marking is carried out on the data collected by the sensor under different working states, whether a risk assessment signal is generated or not is judged, when the working state of the sensor slides down, the risk marking is carried out on the collected data, the pressure of the digital twin model on the data processing is reduced, the prediction risk caused by the problem of the sensor is reduced, and the data credibility is improved.
2. According to the method, the quality information of the risk data and the performance information of the running state of the digital twin model are acquired, the digital twin model prediction risk index is generated, the digital twin model prediction risk index is compared with the digital twin model prediction risk index reference threshold value, risk assessment is carried out on the digital twin model prediction risk index before the digital twin model prediction, whether an early warning signal is generated or not is determined, the situation that the quality of the risk data is too bad is avoided, the data processing load of the digital twin model in the poor running state is further increased, the instability of a prediction result is caused, unnecessary prediction tasks are reduced, the running of the digital twin model is more efficient, and the prediction accuracy is improved.
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For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
fig. 1 is a schematic structural diagram of embodiment 1 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
Example 1
The invention provides a water conservancy management system based on digital twinning as shown in figure 1, which comprises a data acquisition module, a data processing module and a data analysis module, wherein the modules are connected through signals;
the data acquisition module acquires own performance information and working environment information of the sensor, and transmits the own performance information and the working environment information to the data processing module after acquisition;
the data processing module is used for carrying out normalization processing on the acquired self-performance information and working environment information of the sensor, establishing a data processing model and generating an abnormal index of the working state of the sensor;
And the data analysis module compares the abnormal index of the working state of the sensor with a reference threshold value of the abnormal index of the working state of the sensor and marks the data acquired by the sensor under different working states.
Digital twinning is a concept that combines the actual operating conditions of a physical system with a digital model, which can play an important role in water conservancy management systems. The sensor plays a key role in digital twinning, and the following are the roles of the sensor in the digital twinning water conservancy management system:
The sensor is responsible for monitoring various parameters in the water conservancy system in real time, such as water level, water flow speed, water quality, temperature, humidity, rainfall and the like. These data are used to build the basis of digital twinning, ensuring that the digital model accurately reflects the actual situation.
The sensor can detect abnormal conditions such as too fast rise of water level, abnormal water quality and the like, and immediately give an alarm to help water conservancy management personnel take urgent measures to prevent disasters or damages.
Prediction and optimization, namely, based on sensor data and a digital twin model, a water conservancy manager can make prediction and optimization decisions. They can predict future water supply and demand conditions, make proper scheduling plans, and optimize water resource allocation and utilization.
The sensor is difficult to control due to the fact that the sensor is lowered in performance and unstable working environment in the long-time use process, and the quality of data acquired by the sensor is difficult to control due to the fact that the sensor slides down, the working state of the sensor is evaluated through collecting the self performance information and the working environment information of the sensor, and the data acquired by the sensor are marked according to different working states.
It should be noted that, in this embodiment, the evaluated sensor types are all in direct contact with the water body, such as a water level sensor, a water quality sensor, and a flow sensor, and if the sensor types are changed, the collection of the working environment information needs to be changed according to the actual situation, which is not described herein.
The data acquisition module acquires own performance information and working environment information of the sensor, and transmits the own performance information and the working environment information to the data processing module after acquisition;
The self performance information comprises a response time fluctuation coefficient and an abnormal calibration frequency value, and after the self performance information is acquired, the response time fluctuation coefficient and the abnormal calibration frequency value are respectively marked as BD and YX by the data acquisition module;
The response time of a sensor, which is the time required for the sensor to detect a change in an environment or measured parameter to produce a measurement, is typically expressed in units of time (e.g., seconds or milliseconds), and is an important performance parameter, particularly in applications requiring real-time monitoring and control. A shorter response time means that the sensor can detect environmental changes faster and provide corresponding measurements, while a longer response time may result in delayed measurements.
The response time fluctuation coefficient acquisition logic is as follows:
acquiring response time of a sensor in a time period T, and setting a reference threshold value of the response time;
It should be noted that, the response time of the sensor may be obtained by timing the process from detecting the change of the environment or the measured parameter to generating the measured result, the reference threshold value of the response time refers to the maximum upper limit value of the response time, and reference may be made to a specific sensor data manual or specification table, and these information are generally provided by a specific sensor manufacturer, and include details about the response time of the sensor, which are not described herein in detail;
When the response time of the sensor is greater than the reference threshold of the response time, the response time of the sensor is indicated to be in an abnormal state, and the response time of the sensor is marked as t i, i represents the number of times the abnormal state occurs in the response time of the sensor, i= {1, 2..and j }, j is a positive integer;
The response time fluctuation coefficient is calculated by the following expression, bd=bc/PJ, wherein BZ is the standard deviation of response time in abnormal state, and the expression is that PJ is the average value of response time in abnormal state, and the expression is
The higher the response time fluctuation coefficient is, the worse the working state of the sensor in the T time is, the higher the risk that the data collected in the T time possibly exists, otherwise, the better the working state of the sensor in the T time is, and the higher the quality of the data collected in the T time is.
Calibration of the sensor is an important procedure for ensuring the accuracy and reliability of the sensor measurements. Calibration aims to adjust the output of the sensor as close as possible to the true or standard value. The performance of the sensor may vary over time, and therefore requires periodic maintenance and calibration to ensure accuracy of the measurement results.
The acquisition logic for the outlier calibration frequency values is as follows:
the calibration times of the sensor in the T time period are obtained, and the average calibration frequency is calculated, wherein the expression is as follows: ZC is the total number of times of calibration of the sensor in a time period T, and T is the time period for obtaining the number of times of calibration of the sensor;
Setting an average calibration frequency reference threshold value, wherein the average calibration frequency reference threshold value is used for evaluating whether the calibration frequency of the sensor is abnormal or not, the setting of the average calibration frequency reference threshold value can be determined according to the past calibration frequency or application requirements, if the average calibration frequency is smaller than the average calibration frequency reference threshold value, the calibration frequency of the sensor is indicated to be in a normal level, and if the average calibration frequency is greater than or equal to the average calibration frequency reference threshold value, the calibration frequency of the sensor is indicated to be in an abnormal state, and the calibration frequency is marked as JZ ', JZ' to represent the average calibration frequency in the abnormal state;
the acquisition expression of the abnormal calibration frequency value is that YX=JZ';
The higher the abnormal calibration frequency value is, the more the performance of the sensor is reduced in the T time, the more unstable the working state of the sensor is, the higher the risk that the data acquired in the T time possibly exists, otherwise, the better the working state of the sensor in the T time is, and the higher the quality of the data acquired in the T time is.
The working environment information comprises a water pressure abnormal deviation value, and after the water pressure abnormal deviation value is acquired, the data acquisition module marks the water pressure abnormal deviation value as SY;
abnormal changes in water pressure can have a variety of effects on sensors operating under water, with the specific effects varying depending on the type, design and use of the sensor. The following are some possible effects:
The sensor performance is limited in that abrupt changes in water pressure can cause the sensor to be subjected to mechanical stress, thereby affecting its performance and accuracy. The sensor may produce errors or inaccurate measurements;
tightness problems sensors are often required to have good tightness to prevent water from entering the interior of the sensor. Sudden changes in water pressure can cause the sensor to be compromised in its sealability, exposing it to an underwater environment, thereby affecting its long-term stability;
damage to the electronics inside the sensor may be affected by changes in water pressure, especially at extreme depths. This may lead to damage or failure of the electronic components;
Material resistance the housing and component materials of the sensor need to have high pressure resistant properties to ensure stability in an underwater environment. Abnormal water pressure changes may cause material fatigue or damage;
data transmission problems underwater sensors typically require data to be transmitted to the surface or other location. Abnormal water pressure changes may affect the stability and quality of the data transmission signal;
The acquisition logic of the water pressure abnormal deviation value is as follows:
setting a reference threshold range of water pressure under the working state of the sensor, and marking the reference threshold range as SY z min～SYz max;
It should be noted that, the reference threshold range of the water pressure in the working state of the sensor refers to the optimal water pressure range that can be adapted to the working state of the sensor, and can be comprehensively considered from a specific sensor data manual or specification table and a specific depth of the underwater work of the sensor, which is not described herein in detail.
Acquiring working water pressure of a sensor in a T time period, marking the working water pressure as SY, and calculating a water pressure abnormality deviation value, wherein the expression is as followsWherein h represents the number of times the sensor operating water pressure exceeds a reference threshold range, h= {1, 2,..and v }, h is a positive integer, And q x,qy is the starting time and the ending time of each sensor operating water pressure exceeding the reference threshold range.
It should be noted that, some sensors working under water integrate the water pressure sensor, can obtain the working water pressure at the same time while measuring the water quality data, can install the professional water pressure sensor to measure in addition if not integrate, the professional water pressure sensor has more excellent compression resistance than the sensor used for gathering the water quality data;
the higher the water pressure abnormal deviation value is, the more unstable the working state of the sensor in the T time is, the higher the risk that the data acquired in the T time possibly exists, otherwise, the better the working state of the sensor in the T time is, and the higher the quality of the data acquired in the T time is.
The data processing module normalizes the obtained response time fluctuation coefficient BD, the abnormal calibration frequency value YX and the water pressure deviation value SY, establishes a data processing model, and obtains the abnormal index of the working state of the sensor through weighted summation calculation;
For example, the sensor operating condition abnormality index may be obtained by the following formula wd=a 1*BD+a2*YX+a3 ×sy, where WD is the sensor operating condition abnormality index, and a 1、a2、a3 is the response time fluctuation coefficient, the abnormality calibration frequency value, and the preset scaling factor of the water pressure deviation value, respectively, where a 1、a2、a3 is greater than 0.
The calculation shows that the larger the response time fluctuation coefficient, the abnormal calibration frequency value and the water pressure deviation value, namely the larger the abnormal index of the working state of the sensor, the worse the stability of the working state of the sensor in the T time is, the higher the risk of the data acquired in the T time is possibly, and the smaller the response time fluctuation coefficient, the abnormal calibration frequency value and the water pressure deviation value, namely the smaller the abnormal index of the working state of the sensor is, the higher the stability of the working state of the sensor in the T time is, and the lower the risk of the data acquired in the T time is.
The data analysis module is used for setting a sensor working state abnormality index reference threshold value, comparing the sensor working state abnormality index with the sensor working state abnormality index reference threshold value and marking data acquired by the sensor in different working states;
If the abnormal index of the working state of the sensor is smaller than the reference threshold value of the abnormal index of the working state of the sensor, the stability of the working state of the sensor in the time T is higher, the risk of the data acquired in the time T is lower, and the risk analysis of the data before the prediction of the digital twin model is not needed;
If the abnormal index of the working state of the sensor is larger than or equal to the abnormal index reference threshold of the working state of the sensor, the stability of the working state of the sensor in the T time is poor, the risk of data collected in the T time is high, risk analysis is needed to be carried out on the data collected in the T time before the digital twin model is predicted, a risk assessment signal is sent out, and the data collected in the T time of the sensor are marked as risk data.
According to the invention, the data processing model is established by acquiring the self performance information and the working environment information of the sensor, the abnormal index of the working state of the sensor is generated, the abnormal index of the working state of the sensor is compared with the reference threshold value of the abnormal index of the working state of the sensor, the risk marking is carried out on the data collected by the sensor under different working states, whether a risk assessment signal is generated or not is judged, when the working state of the sensor slides down, the risk marking is carried out on the collected data, the pressure of the digital twin model on the data processing is reduced, the prediction risk caused by the problem of the sensor is reduced, and the data credibility is improved.
Example 2
In the above embodiment, by acquiring the performance information and the working environment information of the sensor, establishing a data processing model, generating an abnormal index of the working state of the sensor, comparing the abnormal index of the working state of the sensor with a reference threshold value of the abnormal index of the working state of the sensor, performing risk marking on the data collected by the sensor in different working states, and after determining that the data has risk, performing risk analysis on the data before predicting the digital twin model to ensure the accuracy of the predicting result of the digital twin model, the specific steps are as follows:
the data acquisition module is used for acquiring quality information of the risk data and performance information of the running state of the digital twin model, and transmitting the quality information of the risk data and the performance information of the running state of the digital twin model to the data processing module after acquisition;
the quality information of the risk data comprises a data quality anomaly coefficient, and after acquisition, the data acquisition module marks the data quality anomaly coefficient as ZL;
The data quality is obtained through comprehensive consideration of the data deletion rate and the outlier of the data, wherein the data deletion rate is the proportion of the deletion value (namely, the data points which are not collected or recorded) in the data set, and the calculation of the deletion rate is very important for data quality assessment and data analysis. An outlier of data refers to a value that is significantly different from most data points in a dataset, typically due to measurement errors, data entry errors, abnormal events, or other causes of anomalies. Outliers can have adverse effects on data analysis and model construction.
The expression of the data quality anomaly coefficient is as follows: Wherein, QS represents the normalized value of the missing rate of the data, which can be obtained by making the ratio of the number of missing values to the total quantity of data points, LQ represents the normalized value of the outlier of the data, which can be obtained by calculating the standard deviation multiple between the data points and the mean value by the Z-Score method, alpha represents the weight coefficient of the missing rate of the data, and beta represents the weight coefficient of the outlier of the data;
The weight coefficient alpha of the missing rate of the data and the weight coefficient beta of the outlier of the data are obtained through historical data, suggestions of experts in the field and digital twin model operation requirements, and a large amount of historical data are collected to conduct software and related algorithm simulation to further determine the distribution value of the weight.
The higher the data quality anomaly coefficient is, the worse the quality of risk data is, the data processing load of the digital twin model is further increased when the running state is poorer, the instability of a prediction result is caused, otherwise, the digital twin model is still in a controllable range for the processing of the risk data, and the prediction result is not influenced.
The performance information of the digital twin model running state comprises a running performance coefficient, and after the data acquisition module marks the running performance coefficient as XN;
The running state performance information of the digital twin model can be comprehensively considered through calculation time, memory utilization rate and CPU utilization rate, wherein the calculation time refers to the time required for recording the running of the digital twin model and comprises training and deducing stages. Shorter computation times generally represent higher performance and efficiency. The memory usage rate is the memory usage condition during the operation of the digital twin model, and the digital twin model is ensured to operate within the available memory range. CPU utilization is the degree of hardware resources that are occupied by the runtime of the index twin model.
The expression of the running performance coefficient is as follows, xn=ln (γ×js+δ×nc+θ×ly+1), where JS represents the calculation time, NC represents the memory usage, LY represents the CPU usage, γ represents the weight coefficient of the calculation time, δ represents the weight coefficient of the memory usage, θ represents the weight coefficient of the CPU usage, and the calculation time, the memory usage, and the CPU usage may be obtained in real time during the running of the digital twin model by using a specialized performance monitoring tool such as top, prometheus, grafana.
The weight coefficient gamma of the calculated time, the weight coefficient delta of the memory utilization rate and the weight coefficient theta of the CPU utilization rate are obtained through historical data, suggestions of experts in the field and digital twin model operation requirements in a comprehensive mode, and a large amount of historical data are collected to conduct software and related algorithm simulation to further determine the distribution value of the weight.
The data processing module performs normalization processing on the acquired data quality abnormal coefficient ZL and the operation performance coefficient XN, establishes a data processing model, and obtains a digital twin model prediction risk index through weighted summation calculation;
for example, the digital twin model predicted risk index may be obtained by the following formula fx=b 1*ZL+b2 ×xn, where FX is the digital twin model predicted risk index, b 1、b2 is the preset scaling factor of the data quality anomaly coefficient and the running performance coefficient, respectively, and b 1、b2 is greater than 0.
The calculation shows that the larger the data quality anomaly coefficient and the running performance coefficient are, namely the larger the digital twin model prediction risk index is, the higher the risk degree of the digital twin model prediction is, the error of the prediction result is caused by the extremely high probability, and the smaller the data quality anomaly coefficient and the running performance coefficient are, namely the smaller the digital twin model prediction risk index is, the risk degree of the digital twin model prediction is in a controllable range.
The data analysis module is used for setting a digital twin model prediction risk index reference threshold value, comparing the digital twin model prediction risk index with the digital twin model prediction risk index reference threshold value, and carrying out risk assessment on the digital twin model before prediction;
If the digital twin model prediction risk index is smaller than the digital twin model prediction risk index reference threshold, the digital twin model prediction risk degree is in a controllable range, and the future water resource supply and demand conditions can be normally predicted;
If the digital twin model prediction risk index is greater than or equal to the digital twin model prediction risk index reference threshold, the higher the risk degree of the digital twin model prediction is, the error of the prediction result is caused by the extremely high probability, an early warning signal is generated, and a manager determines whether the future water resource supply and demand condition needs to be predicted continuously.
According to the method, the quality information of the risk data and the performance information of the running state of the digital twin model are acquired, the digital twin model prediction risk index is generated, the digital twin model prediction risk index is compared with the digital twin model prediction risk index reference threshold value, risk assessment is carried out on the digital twin model prediction risk index before the digital twin model prediction, whether an early warning signal is generated or not is determined, the situation that the quality of the risk data is too bad is avoided, the data processing load of the digital twin model in the poor running state is further increased, the instability of a prediction result is caused, unnecessary prediction tasks are reduced, the running of the digital twin model is more efficient, and the prediction accuracy is improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may 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 may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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 the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (5)
1. The water conservancy management system based on digital twinning is characterized by comprising a data acquisition module, a data processing module and a data analysis module, wherein the modules are connected through signals;
the data acquisition module acquires own performance information and working environment information of the sensor, and transmits the own performance information and the working environment information to the data processing module after acquisition;
the data processing module is used for carrying out normalization processing on the acquired self-performance information and working environment information of the sensor, establishing a data processing model and generating an abnormal index of the working state of the sensor;
the data analysis module compares the abnormal index of the working state of the sensor with a reference threshold value of the abnormal index of the working state of the sensor and marks the data acquired by the sensor under different working states;
if the abnormal index of the working state of the sensor is larger than or equal to the reference threshold value of the abnormal index of the working state of the sensor, a risk assessment signal is generated, and data acquired by the sensor in the time T are marked as risk data;
The system comprises a data acquisition module, a sensor and a control module, wherein the data acquisition module acquires self-performance information and working environment information of the sensor, the self-performance information of the sensor comprises a response time fluctuation coefficient and an abnormal calibration frequency value, the working environment information comprises a water pressure abnormal deviation value, and the response time fluctuation coefficient, the abnormal calibration frequency value and the water pressure abnormal deviation value are respectively marked as BD, YX and SY after acquisition;
The acquisition logic of the water pressure abnormal deviation value is as follows:
setting a reference threshold range of water pressure under the working state of the sensor, and marking the reference threshold range as SY z min～SYz max;
acquiring working water pressure of a sensor in a T time period, marking the working water pressure as SY, and calculating a water pressure abnormality deviation value, wherein the expression is as follows Wherein h represents the number of times the sensor operating water pressure exceeds a reference threshold range, h= {1, 2,..and v }, h is a positive integer,[ Q x,qy ] is the starting time and the ending time of each sensor working water pressure exceeding the reference threshold range;
The data acquisition module is also used for acquiring quality information of the risk data and performance information of the running state of the digital twin model, and transmitting the performance information and the working environment information of the data acquisition module to the data processing module after the acquisition;
the quality information of the risk data comprises data quality anomaly coefficients, and the performance information of the digital twin model operation state comprises operation performance coefficients.
2. The water conservancy management system based on digital twinning as set forth in claim 1, wherein the response time fluctuation coefficient acquisition logic is as follows:
acquiring response time of a sensor in a time period T, and setting a reference threshold value of the response time;
when the response time of the sensor is greater than a reference threshold of the response time, marking the sensor as t i, wherein i represents the number of times the abnormal state occurs in the response time of the sensor, i= {1, 2,..;
the response time fluctuation coefficient is calculated by the following expression, namely BD=BC/PJ, wherein BC is the standard deviation of response time in abnormal state, and the expression is that PJ is the average value of response time in abnormal state, and the expression is
3. The system of claim 1, wherein the logic for obtaining the abnormal calibration frequency values is as follows:
the calibration times of the sensor in the T time period are obtained, and the average calibration frequency is calculated, wherein the expression is as follows: ZC is the total number of times of calibration of the sensor in a time period T, and T is the time period for obtaining the number of times of calibration of the sensor;
setting an average calibration frequency reference threshold, and if the average calibration frequency is greater than or equal to the average calibration frequency reference threshold, marking the average calibration frequency as JZ ', JZ' representing the average calibration frequency in an abnormal state;
The acquisition expression of the abnormal calibration frequency value is yx=jz'.
4. The system of claim 1, wherein the data processing module is further configured to normalize quality information of the obtained risk data and performance information of an operating state of the digital twin model, and establish a data processing model to generate a predicted risk index of the digital twin model.
5. The system of claim 4, wherein the data analysis module is further configured to compare the digital twin model predicted risk index with a digital twin model predicted risk index reference threshold, and perform risk assessment on the digital twin model prior to prediction;
If the predicted risk index of the digital twin model is greater than or equal to the reference threshold value of the predicted risk index of the digital twin model, generating an early warning signal, and determining whether to predict the future water resource supply and demand condition by a manager.
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