Abnormality positioning method and system based on meter reading data

Abstract

The invention discloses an anomaly positioning method and system based on meter reading data, and relates to the technical field of power system data processing and analysis, and the method comprises the steps: collecting multi-source data, carrying out the fusion processing of the collected multi-source data, carrying out the standardization processing of the fused data, and obtaining a power system state matrix; judging whether meter reading data is abnormal or not based on an abnormal detection value model; constructing an intelligent decision model according to historical data and a current abnormal detection result; evaluating a comprehensive decision score of the node; judging the priority of the node based on a score result; and a maintenance task is matched, the difficulty in approaching of the node is judged, a processing instruction is generated, and the meter reading data is encrypted and stored. The capability of anomaly detection and fault early warning is enhanced, and the reliability, safety and maintenance efficiency of operation of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system data processing and analysis, and in particular to an abnormality locating method and system based on meter reading data. Background Art

[0002] With the rapid development of smart grid technology, the power meter reading system has gradually transitioned from traditional manual meter reading to automated and intelligent meter reading. The traditional manual meter reading method is inefficient, inaccurate, and prone to human errors. These devices can automatically collect users' electricity consumption data and transmit the data to the power station through wireless networks. However, with the increase in data volume and the complexity of the network environment, data loss, delay, and data security issues that occur during data transmission are becoming increasingly prominent. At the same time, due to the real-time and high-frequency nature of meter reading data, how to efficiently process and store this data, especially how to ensure the security and integrity of the data, has become an urgent problem to be solved in the current power system.

[0003] The existing meter reading data processing system has some deficiencies in data collection, storage and analysis. First, although smart meters and sensors can provide high-precision electricity consumption data, there are still problems of data loss and difficulty in timely identification of abnormal data during data collection and transmission. Secondly, the existing technology mostly uses centralized databases for data storage. Although this method is convenient for management and query, it is easy to become a target of network attacks, and the security and reliability of data are difficult to guarantee. In addition, when processing large amounts of data, the existing system has low processing efficiency and real-time performance. Especially when locating abnormal data and supporting decision-making, the complexity of the algorithm and the huge amount of data often lead to slow system response speed, affecting decision-making efficiency. Finally, the existing technology also has deficiencies in the traceability and non-tamperability of abnormal data, and it is difficult to ensure the integrity and authenticity of data during storage and transmission. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing power meter reading system may have problems of data loss and inaccurate data collection during the data collection process, and the security and reliability of the data are difficult to guarantee. Faced with massive meter reading data, the existing system has deficiencies in processing efficiency and real-time performance, especially in abnormal data positioning and decision support. The complexity of the algorithm and the huge amount of data often lead to the problem of slow system response speed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an abnormality locating method based on meter reading data, comprising: Collect multi-source data, use linear regression model and normalization function to fuse the collected multi-source data, and standardize the fused data to obtain the power system state matrix; identify abnormal conditions in the power system by analyzing the power system state matrix, quantify the degree of abnormality of each node, and judge whether the meter reading data is abnormal based on the abnormal detection value model; build an intelligent decision-making model based on historical data and current abnormality detection results using linear regression model and normalization function, evaluate the comprehensive decision score of the node, judge the priority of the node based on the scoring result, and match the maintenance task; analyze the topology of the power grid in combination with graph neural network, locate the abnormality based on GPS and environmental data, judge the inaccessibility of the node, generate processing instructions, and encrypt and store the meter reading data.

[0007] As a preferred solution of the anomaly positioning method based on meter reading data described in the present invention, the multi-source data collection includes collecting meter reading data, historical data, weather data, geographic location data, time data, user type data, equipment status data, power load data and user behavior data through electricity meters and sensors; the sensors include GPS, system clock and load monitoring device.

[0008] As a preferred solution of the anomaly location method based on meter reading data described in the present invention, wherein: the power system state matrix includes fusing all data sources collected to the same node through electric meters and sensors into one result, and converting the fused result into the power system state matrix through standardized processing;

[0009] The fusion processing includes fusing the collected multi-source data through a linear regression model and a normalization function to reflect the operation status of the power system and the specific conditions of each node. The formula is expressed as follows: ; in, It represents the result after multi-source data fusion. Indicates the total number of data sources. Indicates Data source data, Indicates The mean of the data from each data source, Indicates The standard deviation of the data source data, represents the integral variable, Indicates The shape parameter of the gamma distribution of the data source, Represents the total number of normalized data. Indicates The normalized data are i and j, respectively.

[0010] As a preferred solution of the abnormality location method based on meter reading data described in the present invention, the power system state matrix also includes the result after multi-source data fusion. , use the Z-score formula to standardize the data and construct the power system state matrix, the formula is expressed as: ; in, represents the power system state matrix, represents the weight matrix, represents the input data matrix, represents the mean matrix, represents the identity matrix, represents the normalized data matrix, represents the parameter matrix; The analysis of the power system state matrix includes: identifying abnormal conditions in the power system by analyzing the comprehensive parameters of the power system nodes reflected by the power system state matrix, quantifying the abnormal degree of each node, and establishing an abnormal detection value model, which is expressed as follows: ; in, represents the anomaly detection value model, Indicates the total number of data sources. Indicates The fusion result of the data sources is for The state matrix of a data source, i is the variable index; The value range is ,when When , the meter reading data is judged to be normal; when When , it is judged that there is a slight abnormality in the meter reading data and a monitoring instruction is issued; when When the meter reading data is seriously abnormal, an alarm message is issued for immediate processing.

[0011] As a preferred solution of the abnormality positioning method based on meter reading data described in the present invention, the construction of the intelligent decision model includes constructing the intelligent decision model according to the linear regression model and the normalization function, and the formula is expressed as follows: ; ; in, Representation Node The overall decision score, Indicates the number of historical data records. Representation Node The abnormal location value in the lth record, Indicates the maximum value of all node abnormal location values, Representation Node The inaccessibility value in the lth record, represents the maximum value of the inaccessibility value of all nodes, represents the coefficient of the oth feature in the lth record, M is the total number of features, represents the weight of the oth feature, represents the oth input feature, Representation Node The intelligent decision support value of represents the normalization constant, Indicates the historical data length of the node, Representation Node The specific value in the rth data record, represents the adjustment parameter of the rth record, represents the integral variable, l, v, o and r are variable indices; The value range is ,when When The comprehensive decision weight is low; when When The comprehensive decision weight is medium; when When The comprehensive decision-making weight is high; The value range is ,like , then the node As low priority, the node is judged to be stable and continues to monitor the node status; when When The node is considered to have a potential problem and a maintenance task is generated. The maintenance personnel will give priority to checking the node in the next inspection. When the node High priority, generates a maintenance task, and immediately dispatches maintenance personnel to conduct a detailed inspection and repair of the node.

[0012] As a preferred solution of the abnormality positioning method based on meter reading data described in the present invention, the intelligent decision-making also includes using a neural network to analyze the power grid topology and perform abnormality positioning based on GPS and environmental data, and the formula is expressed as follows: ; ; After locating the abnormal meter reading data, combine GPS and environmental data to determine whether the current area is difficult to access. The formula is: ; in, Representation Node The abnormal location value of G represents the number of layers of the graph neural network. Indicates the total number of nodes, Represents the g-th layer node in the graph neural network The hidden state of and Represents the adjustment parameter, dist Representation Node The geographical distance Representation Node Environmental data, is the activation function, Representation Node The inaccessibility value of is the set of neighbor nodes of node v, is the normalized coefficient between node v and node u, is the weight matrix of the neighbor node u in the gth layer, is the hidden state of node u in the g-1th layer of the graph neural network, is the weight matrix of the g-th layer node v itself, is the hidden state of node v in the g-1th layer, Representation Node GPS coordinates, Indicates the maximum value of GPS data. Indicates the maximum value of environmental data. is the abnormal detection value of node v, g, v and u are variable indexes; The value range is ,when When Normal; when , then the node There is a slight abnormality, and further monitoring instructions are issued; when When If there is an obvious abnormality, an alarm message will be issued for immediate processing; The value range of is [0, 1]. When For easy access, generate normal approach instructions; when When For medium-difficulty approaches, generate inspection instructions, increase monitoring frequency, and remind maintenance personnel to wear safety equipment for maintenance; When For hard-to-reach areas, an alarm is sounded and problems are immediately identified through remote diagnostic tools and maintenance is performed.

[0013] As a preferred solution of the anomaly locating method based on meter reading data described in the present invention, the encrypted storage of the meter reading data includes encrypting the meter reading data using an AES symmetric encryption algorithm, encrypting the AES key using an RSA asymmetric encryption algorithm, and storing the encrypted key in a key management system.

[0014] An abnormality positioning system based on meter reading data adopting any method as described in the present invention comprises a data acquisition and processing module, an abnormality detection and analysis module, an intelligent decision-making and task allocation module, and an abnormality positioning and data security module.

[0015] The data acquisition and processing module is used to acquire multi-source data, and to fuse the data through a linear regression model and a normalization function to obtain a power system state matrix.

[0016] The anomaly detection and analysis module includes an anomaly detection unit and an anomaly analysis unit, which are used to standardize the fused data, construct a power system state matrix, identify abnormal conditions in the power system by analyzing the state matrix, quantify the degree of abnormality of each node, and establish an anomaly detection value model.

[0017] The intelligent decision-making and task allocation module includes an intelligent decision-making unit and a task allocation unit, which are used to build an intelligent decision-making model based on historical data and current anomaly detection results, evaluate the comprehensive decision score of the node, determine the priority of the node, and match the maintenance task.

[0018] The anomaly location and data security module is used to analyze the power grid topology through a graph neural network, and to locate anomalies in combination with GPS and environmental data, determine the inaccessibility of nodes, and generate processing instructions, and use the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm to encrypt and store meter reading data.

[0019] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0020] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0021] Beneficial effects of the present invention: The present invention collects multi-source data, including meter readings, environmental factors, etc., and uses linear regression models and normalization functions for fusion processing, thereby achieving comprehensive monitoring and in-depth understanding of the operating status of the power system; the fused data is standardized to generate a power system state matrix that can reflect the real-time state of the power system, providing a reliable data basis for subsequent anomaly detection and analysis, and significantly improving the accuracy and efficiency of anomaly positioning; by analyzing the power system state matrix, abnormal situations can be discovered in a timely manner and the degree of abnormality of each node can be quantified. Based on the anomaly detection value model, the objectivity and systematicness of anomaly detection are improved, and the possibility of human misjudgment is reduced. possibility; and using historical data and current anomaly detection results, an intelligent decision-making model is constructed to evaluate the comprehensive decision-making score of the node, and the node priority is determined according to the scoring results, thereby optimizing the allocation of maintenance resources and ensuring the efficiency and pertinence of the maintenance work; combining with the graph neural network to analyze the power grid topology, the present invention can more accurately locate anomalies and judge the inaccessibility of nodes, adjust the maintenance strategy, and improve the safety and efficiency of maintenance work; the meter reading data is encrypted and stored using the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm, thereby ensuring data security, preventing data leakage, protecting the privacy of users and the system, and improving the reliability of the system and user trust. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 An overall flow chart of an abnormality locating method based on meter reading data provided by the first embodiment of the present invention;

[0024] Figure 2 A system solution flow chart of an abnormality locating system based on meter reading data provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] Example 1

[0027] Reference Figure 1 , is an embodiment of the present invention, and provides an abnormality locating method based on meter reading data, comprising: S1: Collect multi-source data, use linear regression model and normalization function to fuse the collected multi-source data, and standardize the fused data to obtain the power system state matrix.

[0028] The data collected through high-precision electricity meters and sensors include meter reading data, historical data, weather data, geographic location data, time data, user type data, equipment status data, power load data, and user behavior data; the sensors include GPS, system clock, and load monitoring device.

[0029] The power system state matrix includes fusing various data sources collected to the same node through high-precision electric meters and sensors into a comprehensive result, and converting the comprehensive result into a power system state matrix that can show the comprehensive and integrated operating status of each node through standardized processing;

[0030] The fusion processing includes fusing the collected multi-source data through a linear regression model and a normalization function to reflect the operation status of the power system and the specific conditions of each node. The formula is expressed as follows: ; in, It represents the result after multi-source data fusion. Indicates the total number of data sources. Indicates Data source data, Indicates The mean of the data from each data source, Indicates The standard deviation of the data source data, represents the integral variable, Indicates The shape parameter of the gamma distribution of the data source, Represents the total number of normalized data. Indicates The normalized data are i and j, respectively.

[0031] The aim is to integrate the collected multi-source data and make comprehensive use of data from different sources to achieve comprehensive monitoring and analysis of the power system status. This comprehensiveness enables the system to better understand and predict power demand and load conditions. The integrated data combines multi-dimensional information, such as time, geographic location, and user behavior, which helps to conduct more in-depth analysis. This multi-dimensional analysis can reveal complex relationships and patterns that cannot be reflected by a single data source. The integration of multi-source data can improve the robustness and reliability of the data. Even if one data source is abnormal or erroneous, the remaining data sources can still provide sufficient information for effective analysis and decision-making.

[0032] Normalizing the data can eliminate the dimensional differences between different data sources, so that information from different data sources can be compared and analyzed on the same platform, which helps to improve the effect and accuracy of data fusion. Through the fusion of multi-source data, the system can more accurately monitor the operating status of the power system, identify potential problems and abnormal situations, and help improve the safety and stability of the power system.

[0033] Furthermore, the comprehensive use of the analysis results of multi-source data can provide more scientific and reasonable decision support for the operation and dispatch of the power system. For example, it can predict changes in power demand, optimize the allocation of power resources, and improve the operating efficiency of the power system. Using the results of multi-source data fusion, an intelligent decision-making system can be built to further enhance the data processing and analysis capabilities of the power system. Intelligent processing can automatically identify and process abnormal data, improving the system's response speed and processing efficiency.

[0034] S2: By analyzing the power system state matrix, the abnormal conditions in the power system are identified, and the degree of abnormality of each node is quantified. Based on the abnormal detection value model, it is determined whether the meter reading data is abnormal.

[0035] The power system state matrix also includes, based on the result of multi-source data fusion and data standardization processing, constructing the power system state matrix, the formula is expressed as: ; in, represents the power system state matrix, represents the weight matrix, represents the input data matrix, represents the mean matrix, represents the identity matrix, represents the normalized data matrix, represents the parameter matrix; The analysis of the power system state matrix includes: identifying abnormal conditions in the power system by analyzing the comprehensive parameters of the power system nodes reflected by the power system state matrix, quantifying the abnormal degree of each node, and establishing an abnormal detection value model, which is expressed as follows: ; in, represents the anomaly detection value model, Indicates the total number of data sources. Indicates The fusion result of the data sources is for The state matrix of a data source, i is the variable index; The value range is ,when When , the meter reading data is judged to be normal; when When , it is judged that there is a slight abnormality in the meter reading data and a monitoring instruction is issued; when When the meter reading data is seriously abnormal, an alarm message is issued for immediate processing.

[0036] The power system state matrix is ​​established through the processed data, and the power system is comprehensively analyzed using the state matrix, aiming to monitor and evaluate the operating status of the power system in real time, detect and handle abnormal situations in a timely manner, and ensure the safety and stability of the power system. By integrating data from multiple data sources, the state matrix can fully reflect the operating status of the power system. The introduction of weight matrix and parameter matrix allows the contribution of different data sources to the system status to be weighed and adjusted.

[0037] By introducing the mean matrix and the unit matrix, the system can be dynamically adjusted according to the actual operating conditions and adapt to different power system environments. At the same time, the design of the state matrix has good scalability and can introduce new data sources and parameters as needed. The establishment of the state matrix enables the operating status of the power system to be monitored and evaluated in real time. By quantifying and modeling the status of each node, potential operating anomalies and risks can be discovered in a timely manner. Comprehensive analysis of the power system state matrix can enhance the reliability and stability of the system. The system can make more accurate judgments and decisions based on real-time data to ensure the safe operation of the power system.

[0038] Furthermore, the anomaly detection formula can accurately identify abnormal data in the power system. The ratio of the fusion result and the state matrix value can be used to quantify the degree of anomaly and provide a basis for subsequent processing. The design of the anomaly detection value model can quantify the degree of anomaly, so as to grade the anomaly. This design enables the system to take different countermeasures according to the severity of the anomaly, improving the pertinence and effectiveness of the processing. By calculating and monitoring the anomaly detection value in real time, the system can realize dynamic monitoring and early warning of abnormal situations. For minor anomalies, further monitoring can be used to confirm; for obvious anomalies, immediate processing measures can be taken to prevent the problem from expanding; the anomaly detection model combines historical data and real-time data to provide strong support for the intelligent decision-making system. The system can make scientific and accurate decisions based on the anomaly detection results, improve the operating efficiency and management level of the power system. Through data-driven methods, the system can continuously learn and optimize the anomaly detection algorithm to improve the accuracy and robustness of anomaly detection. This intelligent management method can better adapt to the complexity and dynamic changes of the power system and improve the system's adaptive ability.

[0039] S3: Based on historical data and current anomaly detection results, an intelligent decision-making model is constructed using a linear regression model and a normalization function to evaluate the comprehensive decision score of the node. Based on the scoring results, the priority of the node is determined and the maintenance tasks are matched.

[0040] The constructing of the intelligent decision model includes constructing the intelligent decision model according to the linear regression model and the normalization function, and the formula is expressed as: ; ; in, Representation Node The overall decision score, Indicates the number of historical data records. Representation Node The abnormal location value in the lth record, Indicates the maximum value of all node abnormal location values, Representation Node The inaccessibility value in the lth record, represents the maximum value of the inaccessibility value of all nodes, represents the coefficient of the oth feature in the lth record, M is the total number of features, represents the weight of the oth feature, represents the oth input feature, Representation Node The intelligent decision support value of represents the normalization constant, Indicates the historical data length of the node, Representation Node The specific value in the rth data record, represents the adjustment parameter of the rth record, represents the integral variable, l, v, o and r are variable indices; The value range is ,when , then the node The comprehensive decision-making weight is low; when , then the node The comprehensive decision weight is medium; when , then the node The comprehensive decision-making weight is high; The value range is ,when When As low priority, the node is judged to be stable and continues to monitor the node status; when When The node is considered to have a potential problem and a maintenance task is generated. The maintenance personnel will give priority to checking the node in the next inspection. When the node High priority, generates a maintenance task, and immediately dispatches maintenance personnel to conduct a detailed inspection and repair of the node.

[0041] The intelligent decision-making also includes using graph neural networks to analyze the topology of the power grid and locate abnormalities based on GPS and environmental data. The formula is expressed as: ; ; After locating the abnormal meter reading data, combine GPS and environmental data to determine whether the current area is difficult to access. The formula is: ; in, Representation Node The abnormal location value of G represents the number of layers of the graph neural network. Indicates the total number of nodes, Represents the g-th layer node in the graph neural network The hidden state of and Represents the adjustment parameter, dist Representation Node The geographical distance Representation Node Environmental data, is the activation function, Representation Node The inaccessibility value of is the set of neighbor nodes of node v, is the normalized coefficient between node v and node u, is the weight matrix of the neighbor node u in the gth layer, is the hidden state of node u in the g-1th layer of the graph neural network, is the weight matrix of the g-th layer node v itself, is the hidden state of node v in the g-1th layer, Representation Node GPS coordinates, Indicates the maximum value of GPS data. Indicates the maximum value of environmental data. is the abnormal detection value of node v, g, v and u are variable indexes; The value range is ,when When Normal; when When There is a slight abnormality, and further monitoring instructions are issued; when When If there is an obvious abnormality, an alarm message will be issued for immediate processing; The value range of is [0, 1]. When For easy access, generate normal approach instructions; when When For medium-difficulty approaches, generate inspection instructions, increase monitoring frequency, and remind maintenance personnel to wear safety equipment for maintenance; When For hard-to-reach areas, an alarm is sounded and problems are immediately identified through remote diagnostic tools and maintenance is performed.

[0042] An intelligent decision-making model is constructed through a linear regression model and a normalization function to achieve a comprehensive decision-making score for each node in the power system, historical data, and real-time data, and to quantify the abnormal detection value and inaccessibility value of each node, thereby evaluating the comprehensive decision weight.

[0043] Combine the historical records and feature information of multiple data sources to ensure the comprehensiveness and accuracy of the decision model. Eliminate data dimension differences through normalization processing to ensure that information from different data sources can be compared and analyzed under the same standard. By quantifying the anomaly detection value and inaccessibility value of each node, the comprehensive decision weight of the node can be scientifically evaluated. The intelligent decision model can provide scientific and accurate decision support and optimize resource allocation. By comprehensively analyzing the status and historical data of each node, the accuracy of anomaly detection and processing can be improved, and the reliability and stability of the system can be enhanced.

[0044] Furthermore, the anomaly analysis model is combined with graph neural network (GNN) to analyze the topology of the power grid, and anomaly location is performed based on GPS and environmental data. Through the hierarchical structure of the neural network, the relationship between the nodes of the power grid is fully explored to improve the accuracy of anomaly location and ensure timely processing. By comprehensively considering the topology of the power grid and multi-source data, the accuracy of anomaly detection and location is improved to ensure timely discovery and processing of problems. Using GNN and multi-source data, an intelligent anomaly detection and location system is built to improve the management level and response capabilities of the power system.

[0045] Furthermore, combined with GPS and environmental data, the difficulty of accessing nodes is evaluated to determine the priority of maintenance personnel's actions. By quantifying the geographical location and environmental data of nodes, the node is evaluated to see whether it is easy to access. Classifying and prioritizing nodes according to the difficulty of access can improve maintenance efficiency and ensure that problems are solved in a timely manner. By scientifically evaluating the difficulty of accessing nodes, maintenance resources can be reasonably allocated to reduce unnecessary waste of time and costs. By reasonably evaluating the difficulty of accessing nodes, maintenance plans can be optimized and the maintenance management level of the power system can be improved. Timely and accurate maintenance and repair can ensure the stable operation of the power system and reduce the risk of power outages caused by equipment failures.

[0046] S4: Analyze the grid topology with graph neural network, locate anomalies based on GPS and environmental data, determine the inaccessibility of nodes, generate processing instructions, and encrypt and store meter reading data.

[0047] The encrypted storage of the meter reading data includes: encrypting the meter reading data using an AES symmetric encryption algorithm, encrypting the AES key using an RSA asymmetric encryption algorithm, and storing the encrypted key in a key management system.

[0048] Furthermore, it ensures the security of data access and prevents unauthorized access and data leakage. Multi-level verification and strict access control mechanisms can effectively prevent data from being illegally accessed and tampered with, ensuring the security and privacy of data. Through multi-level verification and access control, it can ensure that only authorized users can access and decrypt data, prevent data leakage and abuse, and improve the security and reliability of the system.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0050] Example 2, reference Figure 2 , which is the second embodiment of the present invention, provides an abnormality location system based on meter reading data, including a data acquisition and processing module, an abnormality detection and analysis module, an intelligent decision-making and task allocation module, and an abnormality location and data security module.

[0051] The data acquisition and processing module is used to acquire multi-source data, and to fuse the data through a linear regression model and a normalization function to obtain a power system state matrix.

[0052] The anomaly detection and analysis module includes an anomaly detection unit and an anomaly analysis unit, which are used to standardize the fused data, construct a power system state matrix, identify abnormal conditions in the power system by analyzing the state matrix, quantify the degree of abnormality of each node, and establish an anomaly detection value model.

[0053] The intelligent decision-making and task allocation module includes an intelligent decision-making unit and a task allocation unit, which are used to build an intelligent decision-making model based on historical data and current anomaly detection results, evaluate the comprehensive decision score of the node, determine the priority of the node, and match the maintenance task.

[0054] The anomaly location and data security module is used to analyze the power grid topology through a graph neural network, and to locate anomalies in combination with GPS and environmental data, determine the inaccessibility of nodes, and generate processing instructions, and use the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm to encrypt and store meter reading data.

[0055] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0056] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0057] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0058] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0059] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A method for locating anomalies based on meter reading data, characterized in that: include: Collect multi-source data, use linear regression model and normalization function to fuse the collected multi-source data, and standardize the fused data to obtain the power system state matrix; By analyzing the power system state matrix, the abnormal conditions in the power system are identified, and the degree of abnormality of each node is quantified. Based on the abnormal detection value model, it is determined whether the meter reading data is abnormal; Based on historical data and current anomaly detection results, an intelligent decision-making model is constructed using a linear regression model and a normalization function to evaluate the comprehensive decision-making score of the node. Based on the score results, the node priority is determined and maintenance tasks are matched; Analyze the grid topology with graph neural networks, locate anomalies based on GPS and environmental data, determine the inaccessibility of nodes, generate processing instructions, and encrypt and store meter reading data.

2. The method for locating anomalies based on meter reading data according to claim 1, characterized in that: The multi-source data collection includes collecting meter reading data, historical data, weather data, geographic location data, time data, user type data, equipment status data, power load data and user behavior data through electricity meters and sensors; the sensors include GPS, system clock and load monitoring device.

3. The abnormality locating method based on meter reading data according to claim 2, characterized in that: The power system state matrix includes fusing all data sources collected by electric meters and sensors to the same node into one result, and converting the fused result into the power system state matrix through standardized processing; The fusion processing includes fusing the collected multi-source data through a linear regression model and a normalization function to reflect the operation status of the power system and the specific conditions of each node. The formula is expressed as follows: ； in, It represents the result after multi-source data fusion. Indicates the total number of data sources. Indicates Data source data, Indicates The mean of the data from each data source, Indicates The standard deviation of the data source data, represents the integral variable, Indicates The shape parameter of the gamma distribution of the data source, Represents the total number of normalized data. Indicates The normalized data are i and j, respectively.

4. The method for locating anomalies based on meter reading data according to claim 3, characterized in that: The power system state matrix also includes the result after fusion of multi-source data. , use the Z-score formula to standardize the data and construct the power system state matrix, the formula is expressed as: ； in, represents the power system state matrix, represents the weight matrix, represents the input data matrix, represents the mean matrix, represents the identity matrix, represents the normalized data matrix, represents the parameter matrix; The analysis of the power system state matrix includes: identifying abnormal conditions in the power system by analyzing the comprehensive parameters of the power system nodes reflected by the power system state matrix, quantifying the abnormal degree of each node, and establishing an abnormal detection value model, which is expressed as follows: ； in, represents the anomaly detection value model, Indicates the total number of data sources. Indicates The fusion result of the data sources is for The state matrix of a data source, i is the variable index; The value range is ,when When , the meter reading data is judged to be normal; when When , it is judged that there is a slight abnormality in the meter reading data and a monitoring instruction is issued; when When the meter reading data is seriously abnormal, an alarm message is issued for immediate processing.

5. The method for locating anomalies based on meter reading data according to claim 4, characterized in that: The constructing of the intelligent decision model includes constructing the intelligent decision model according to the linear regression model and the normalization function, and the formula is expressed as: ； ； in, Representation Node The overall decision score, Indicates the number of historical data records. Representation Node The abnormal location value in the lth record, Indicates the maximum value of all node abnormal location values, Representation Node The inaccessibility value in the lth record, represents the maximum value of the inaccessibility value of all nodes, represents the coefficient of the oth feature in the lth record, M is the total number of features, represents the weight of the oth feature, represents the oth input feature, Representation Node The intelligent decision support value of represents the normalization constant, Indicates the historical data length of the node, Representation Node The specific value in the rth data record, represents the adjustment parameter of the rth record, represents the integral variable, l, v, o and r are variable indices; The value range is ,when When The comprehensive decision weight is low; when When The comprehensive decision weight is medium; when When The comprehensive decision-making weight is high; The value range is ,when When As low priority, the node is judged to be stable and continues to monitor the node status; when When The node is considered to have a potential problem and a maintenance task is generated. The maintenance personnel will give priority to checking the node in the next inspection. When the node High priority, generates a maintenance task, and immediately dispatches maintenance personnel to conduct a detailed inspection and repair of the node.

6. The abnormality locating method based on meter reading data according to claim 5, characterized in that: The intelligent decision-making also includes using a neural network to analyze the topology of the power grid and locate abnormalities based on GPS and environmental data. The formula is expressed as: ； ； After locating the abnormal meter reading data, combine GPS and environmental data to determine whether the current area is difficult to access. The formula is: ； in, Representation Node The abnormal location value of G represents the number of layers of the graph neural network. Indicates the total number of nodes, Represents the g-th layer node in the graph neural network The hidden state of and Represents the adjustment parameter, dist Representation Node The geographical distance Representation Node Environmental data, is the activation function, Representation Node The inaccessibility value of is the set of neighbor nodes of node v, is the normalized coefficient between node v and node u, is the weight matrix of the neighbor node u in the gth layer, is the hidden state of node u in the g-1th layer of the graph neural network, is the weight matrix of the g-th layer node v itself, is the hidden state of node v in the g-1th layer, Representation Node GPS coordinates, GPS) indicates the maximum value of GPS data. Indicates the maximum value of environmental data. is the abnormal detection value of node v, g, v and u are variable indexes; The value range is ,when When Normal; when When There is a slight abnormality, and further monitoring instructions are issued; when When If there is an obvious abnormality, an alarm message will be issued for immediate processing; The value range of is [0, 1]. When For easy access, generate normal approach instructions; when When For medium-difficulty approaches, generate inspection instructions, increase monitoring frequency, and remind maintenance personnel to wear safety equipment for maintenance; When For hard-to-reach areas, an alarm is sounded and problems are immediately identified through remote diagnostic tools and maintenance is performed.

7. The abnormality locating method based on meter reading data according to claim 6, characterized in that: The encrypted storage of the meter reading data includes: encrypting the meter reading data using an AES symmetric encryption algorithm, encrypting the AES key using an RSA asymmetric encryption algorithm, and storing the encrypted key in a key management system.

8. A system using the abnormality locating method based on meter reading data as claimed in any one of claims 1 to 7, characterized in that: It includes data collection and processing module, anomaly detection and analysis module, intelligent decision-making and task allocation module, and anomaly location and data security module; The data acquisition and processing module is used to collect multi-source data and perform data fusion processing through a linear regression model and a normalization function to obtain a power system state matrix; The anomaly detection and analysis module includes an anomaly detection unit and an anomaly analysis unit, which are used to standardize the fused data, construct a power system state matrix, identify abnormal conditions in the power system by analyzing the state matrix, quantify the degree of abnormality of each node, and establish an anomaly detection value model; The intelligent decision-making and task allocation module includes an intelligent decision-making unit and a task allocation unit, which are used to build an intelligent decision-making model based on historical data and current anomaly detection results, evaluate the comprehensive decision score of the node, determine the priority of the node, and match the maintenance task; The anomaly location and data security module is used to analyze the power grid topology through a graph neural network, and to locate anomalies in combination with GPS and environmental data, determine the inaccessibility of nodes, and generate processing instructions, and use the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm to encrypt and store meter reading data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the abnormality locating method based on meter reading data described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the abnormality locating method based on meter reading data described in any one of claims 1 to 7 are implemented.

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