Analysis method for generating Anti-misoperation strategy on basis of distributed power supply data, and related device

Abstract

The present application provides an analysis method and apparatus for generating an anti-misoperation strategy on the basis of distributed power supply data, an electronic device, and a storage medium. The method comprises: collecting distributed power supply data of different power supply types, and classifying the distributed power supply data; performing real-time multi-dimensional monitoring on the classified distributed power supply data, performing mining and analysis processing on the detected multi-dimensional power supply monitoring data, and upon identification of an abnormal data point, performing data preprocessing; performing misoperation analysis processing on the preprocessed multi-dimensional power supply monitoring data to obtain a misoperation analysis result; and generating an anti-misoperation strategy on the basis of the misoperation analysis result. The analysis solution for generating an anti-misoperation strategy on the basis of distributed power supply data provided in the present application improves the accuracy and reliability of distributed power supply system data processing by combining refined classification, multi-dimensional data monitoring and depth data analysis, and prevents and reduces the risk of data misoperation, thereby improving the operation efficiency and security of power systems.

Description

Analysis Methods and Related Equipment for Distributed Power Supply Data Misoperation Prevention Strategies Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, electronic device and storage medium for analyzing strategies to prevent misoperation of distributed power supply data. Background Technology

[0002] With the transformation of the global energy structure and the rapid development of renewable energy technologies, distributed power systems (such as solar, wind, and hydropower) are playing an increasingly important role in power systems. These systems are favored for their flexibility and environmental friendliness, but they also bring a series of technical challenges, especially in data processing. Distributed power systems generate massive and complex amounts of data, involving multiple dimensions such as power generation output, power quality, event logs, and equipment status. This data is crucial for grid dispatching, load balancing, and equipment maintenance. However, due to the sheer volume and complexity of the data, traditional data processing methods suffer from inefficiency and inaccuracy. Furthermore, the risks of misoperation during data storage, transmission, access, and processing, such as storage failures, transmission distortion, unauthorized access, data tampering, and accidental data deletion, can all adversely affect the normal operation of the system.

[0003] In existing technologies, data manipulation prevention in distributed power systems primarily relies on regular manual checks and audits, as well as detailed logs of data operations for post-event traceability and analysis. These methods are not only inefficient but also susceptible to human error, failing to achieve real-time prevention of data manipulation. Furthermore, existing solutions often lack refined data classification and multi-dimensional monitoring, leading to reduced accuracy in identifying anomalies and significant time and human resource costs. Therefore, there is an urgent need for an intelligent and efficient data analysis method to ensure data accuracy and reliability and prevent potential data manipulation risks.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method, apparatus, electronic device, and storage medium for analyzing distributed power source data to prevent misoperation. This solves the problems of low efficiency, insufficient accuracy, and high cost caused by reliance on manual inspection in existing technologies. It effectively improves the accuracy and reliability of data processing in distributed power source systems, while preventing and reducing the risk of data misoperation, thereby improving the operating efficiency and safety of power systems.

[0006] To address the aforementioned technical problems, this application provides a method for analyzing distributed power supply data anti-misoperation strategies, comprising the following steps:

[0007] Collect distributed power source data of different power source types and classify the distributed power source data;

[0008] Real-time multi-dimensional monitoring is performed on the classified distributed power source data to obtain corresponding multi-dimensional power source monitoring data.

[0009] The multi-dimensional power monitoring data is mined and analyzed to identify abnormal data points. The abnormal data points in the multi-dimensional power monitoring data are then preprocessed to obtain preprocessed multi-dimensional power monitoring data.

[0010] The preprocessed multi-dimensional power monitoring data is subjected to misoperation analysis to obtain corresponding misoperation analysis results. The data analysis results include the misoperation type and its impact corresponding to the power monitoring data.

[0011] Based on the results of the erroneous operation analysis, a corresponding erroneous operation prevention strategy is generated.

[0012] Furthermore, in some embodiments of this application, the collection of distributed power data of different power types includes:

[0013] Pre-install corresponding sensors and data acquisition equipment at each distributed power source point, and install power quality monitoring equipment at the grid connection point;

[0014] Real-time power generation is collected through the sensor and the data acquisition device;

[0015] The power quality monitoring equipment is used to monitor voltage, frequency, and harmonics.

[0016] The system records fault records, operation records, and maintenance activity records of distributed power points through a pre-deployed system;

[0017] The sensors monitor the status of circuit breakers, transformers, and switches in real time.

[0018] Furthermore, in some embodiments of this application, the classification of the distributed power data includes:

[0019] Obtain the power type information of the distributed power data, classify the distributed power data based on the power type information, and obtain the corresponding power type classification result;

[0020] Obtain the data type information and usage type information of the distributed power source data, classify the distributed power source data based on the data type information and usage type information, and obtain the corresponding data type classification results.

[0021] Furthermore, in some embodiments of this application, the step of performing real-time multi-dimensional monitoring on the classified distributed power source data to obtain corresponding multi-dimensional power source monitoring data includes:

[0022] Real-time monitoring of power generation data of distributed power systems of different power types, including real-time power generation and future predicted power generation;

[0023] Real-time monitoring of grid-connected power quality data of distributed power systems of different power types, wherein the grid-connected power quality data includes voltage, frequency and harmonics;

[0024] Real-time monitoring of event log data of distributed power systems with different power types, including fault records, operation records and maintenance activity records;

[0025] Real-time monitoring of status data of distributed power systems with different power types, including circuit breaker status data, transformer status data, and switch status data.

[0026] Furthermore, in some embodiments of this application, the step of mining and analyzing the multi-dimensional power monitoring data to identify abnormal data points, and preprocessing the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data, includes:

[0027] The corresponding operational characteristics are obtained by extracting features from the multi-dimensional power monitoring data using machine learning algorithms.

[0028] Based on the aforementioned operational characteristics, a data anomaly detection model is constructed;

[0029] Based on the data anomaly detection model, the multi-dimensional power monitoring data monitored in real time is mined, analyzed and processed to identify the corresponding abnormal data points and their abnormal behaviors.

[0030] Based on the abnormal data points, the multi-dimensional power monitoring data is cleaned and denoised to obtain preprocessed multi-dimensional power monitoring data.

[0031] Furthermore, in some embodiments of this application, the step of performing malfunction analysis on the preprocessed multi-dimensional power monitoring data to obtain corresponding malfunction analysis results includes:

[0032] Based on the preprocessed multi-dimensional monitoring data, the storage failure risk is analyzed to obtain the storage failure analysis results.

[0033] Based on the preprocessed multi-dimensional monitoring data, the risk of transmission distortion is analyzed, and the transmission distortion analysis results are obtained.

[0034] Based on the preprocessed multi-dimensional monitoring data, the risk of unauthorized access is analyzed to obtain the results of the unauthorized access analysis.

[0035] Based on the preprocessed multi-dimensional monitoring data, the risk of data tampering is analyzed, and the data tampering analysis results are obtained.

[0036] Based on the preprocessed multi-dimensional monitoring data, the risk of accidental data deletion is analyzed, and the results of the accidental data deletion analysis are obtained.

[0037] Furthermore, in some embodiments of this application, generating a corresponding anti-misoperation strategy based on the misoperation analysis results includes:

[0038] Based on the types of misoperations and their impact in the analysis results, a misoperation prevention strategy is generated for each type of misoperation. The misoperation prevention strategy includes data backup and recovery strategy, access control and permission management strategy, data encryption and protection strategy, and data verification and validation strategy.

[0039] By using filtering and principal component analysis algorithms in machine learning, we can perform deep learning on historical distributed power source data to extract behavioral characteristics and patterns during normal operation.

[0040] The aforementioned behavior pattern is applied during real-time data monitoring to identify potential abnormal behaviors, thereby obtaining abnormal behavior identification results.

[0041] The anti-misoperation strategy is adjusted based on the abnormal behavior identification results.

[0042] Accordingly, this application also provides a distributed power supply data anti-misoperation strategy analysis device, including:

[0043] The acquisition module is used to acquire distributed power data of different power types and classify the distributed power data.

[0044] The monitoring module is used to perform real-time multi-dimensional monitoring of the classified distributed power data to obtain corresponding multi-dimensional power monitoring data.

[0045] The mining module is used to perform mining analysis on the multi-dimensional power monitoring data, identify abnormal data points, and preprocess the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data.

[0046] The analysis module is used to perform misoperation analysis on the preprocessed multi-dimensional power monitoring data to obtain corresponding misoperation analysis results. The data analysis results include the misoperation type corresponding to the power monitoring data and its impact.

[0047] The generation module is used to generate corresponding anti-misoperation strategies based on the results of the misoperation analysis.

[0048] This application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the distributed power data anti-misoperation strategy analysis method as described above.

[0049] This application also provides a computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the distributed power data anti-misoperation strategy analysis method as described above.

[0050] Implementing the embodiments of this application has the following beneficial effects:

[0051] As described above, this application provides a method, apparatus, electronic device, and storage medium for analyzing distributed power source data to prevent misoperation. The method includes: collecting distributed power source data of different power types and classifying the data; performing real-time multi-dimensional monitoring of the classified data to obtain corresponding multi-dimensional power monitoring data; performing data mining and analysis on the multi-dimensional power monitoring data to identify abnormal data points, and preprocessing these abnormal data points to obtain preprocessed multi-dimensional power monitoring data; performing misoperation analysis on the preprocessed multi-dimensional power monitoring data to obtain corresponding misoperation analysis results, including the misoperation type and its impact on the power monitoring data; and generating corresponding misoperation prevention strategies based on the misoperation analysis results. The distributed power source data misoperation prevention strategy analysis scheme proposed in this application improves the accuracy and reliability of distributed power source system data processing by combining refined classification, multi-dimensional data monitoring, and deep data analysis, while preventing and reducing the risk of data misoperation, thereby improving the operating efficiency and safety of the power system. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0053] Figure 1 is a flowchart illustrating the distributed power data anti-misoperation strategy analysis method provided in an embodiment of this application;

[0054] Figure 2 is a schematic diagram of the distributed power data partitioning type provided in an embodiment of this application;

[0055] Figure 3 is a flowchart illustrating the monitoring and analysis of distributed power source data provided in an embodiment of this application;

[0056] Figure 4 is a schematic diagram of data misoperation types provided in the embodiments of this application;

[0057] Figure 5 is a schematic diagram of the process for generating a mistake prevention strategy provided in an embodiment of this application;

[0058] Figure 6 is another flowchart illustrating the distributed power data anti-misoperation strategy analysis method provided in an embodiment of this application.

[0059] Figure 7 is a schematic diagram of the structure of the distributed power data anti-misoperation strategy analysis device provided in an embodiment of this application;

[0060] Figure 8 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application.

[0061] The realization of the objectives, functional features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and textual descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0062] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0063] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0064] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0065] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0066] With the widespread application of renewable energy, distributed power systems are playing an increasingly important role in the power system. These systems generate massive and complex amounts of data, which can lead to various risks of data misoperation during operation, such as storage failures and unauthorized access. These misoperations can adversely affect the system. Traditional data error prevention strategies may not be effective in addressing these challenges. For example, relying on manual, periodic data checks and audits to identify potential errors or anomalies, and recording detailed logs of data operations for traceability and analysis in case of problems, are inefficient and susceptible to human error, failing to prevent data misoperation in real time. Therefore, a more intelligent and efficient data analysis method is needed to ensure data accuracy and reliability and prevent potential data misoperation risks.

[0067] To address the aforementioned technical issues, this application provides a distributed power source data anti-misoperation strategy analysis method, device, electronic device, and storage medium. By combining refined classification, multi-dimensional data monitoring, and in-depth data analysis, it improves the accuracy and reliability of distributed power source system data processing, while preventing and reducing the risk of data misoperation, thereby improving the operating efficiency and safety of the power system.

[0068] This application provides a method, apparatus, electronic device, and storage medium for analyzing distributed power data anti-misoperation strategies.

[0069] The distributed power data anti-misoperation strategy analysis device can be deployed in a terminal or a server. The server can include a standalone server or a distributed server, or a server cluster composed of multiple servers. The terminal can include a mobile phone, tablet computer or personal computer (PC).

[0070] The following sections provide detailed descriptions of each example. It should be noted that the order in which the embodiments are described is not intended to limit the priority of the embodiments.

[0071] Please refer to Figure 1, which is a flowchart illustrating the distributed power source data misoperation prevention strategy analysis method provided in this embodiment. The distributed power source data misoperation prevention strategy analysis method provided in this embodiment can be executed in a power system, and specifically includes the following steps:

[0072] S1. Collect distributed power source data of different power source types and classify the distributed power source data;

[0073] Specifically, for step S1, data is collected from different types of distributed power sources. This data includes, but is not limited to, power generation output data, grid-connected power quality data, event log data, and state quantity data. The collected data is then categorized to facilitate more effective subsequent monitoring and analysis.

[0074] S2. Perform real-time multi-dimensional monitoring on the classified distributed power source data to obtain the corresponding multi-dimensional power source monitoring data;

[0075] Specifically, in step S2, the categorized data is monitored in real time to ensure that any abnormal changes in the data can be captured promptly. The multi-dimensional monitoring dimensions include power generation output, power quality, event logs, and equipment status.

[0076] S3. Mining and analyzing the multi-dimensional power monitoring data, identifying abnormal data points, and preprocessing the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data.

[0077] Specifically, in step S3, the monitored data undergoes in-depth analysis to identify anomalies. Anomaly data is preprocessed, including data cleaning and outlier denoising, to ensure data quality. The data analysis process can utilize machine learning algorithms and big data analytics tools to improve the accuracy and efficiency of anomaly detection.

[0078] S4. Perform misoperation analysis on the preprocessed multi-dimensional power monitoring data to obtain the corresponding misoperation analysis results. The data analysis results include the misoperation type and its impact on the power monitoring data.

[0079] Specifically, for step S4, based on the preprocessed data, an error analysis is performed to determine the possible types of errors in the data and their impact.

[0080] S5. Based on the results of the misoperation analysis, generate corresponding anti-misoperation strategies;

[0081] Specifically, for step S5, based on the results of the misoperation analysis, a corresponding anti-misoperation strategy is generated to prevent or mitigate the adverse effects of data misoperation.

[0082] As can be seen, the distributed power data anti-misoperation strategy analysis method provided in this embodiment ensures data integrity and reduces decision-making errors caused by data errors through real-time monitoring and abnormal data processing; enhances the system's protection against unauthorized access and data tampering through misoperation analysis and anti-misoperation strategies; improves the operating efficiency and power supply reliability of the distributed power system through refined data management and analysis; reduces system maintenance costs and workload by predicting potential data problems and automating misoperation prevention; and the system can adapt to different types of distributed power sources and flexibly respond to various data misoperation risks.

[0083] Furthermore, in some embodiments, the "collecting distributed power data of different power types" in step S1 may specifically include:

[0084] Pre-install corresponding sensors and data acquisition equipment at each distributed power source point, and install power quality monitoring equipment at the grid connection point;

[0085] Real-time power generation is collected through sensors and data acquisition devices;

[0086] Voltage, frequency, and harmonics are monitored using power quality monitoring equipment;

[0087] The system records fault records, operation records, and maintenance activity records of distributed power points through a pre-deployed system;

[0088] The status of circuit breakers, transformers and switches is monitored in real time using sensors.

[0089] Specifically, for the data acquisition process in step S1, firstly, corresponding sensors and data acquisition devices are pre-installed at each distributed power source point. These devices can monitor and record various key parameters in real time. Power quality monitoring devices are installed at the grid connection point to monitor power quality indicators such as voltage, frequency, and harmonics. Then, real-time power generation is collected through sensors and data acquisition devices, which is key data for evaluating the performance of distributed power sources. Monitoring voltage, frequency, and harmonics through power quality monitoring devices is crucial for ensuring grid stability and power quality. Next, fault records, operation records, and maintenance activity records of distributed power source points are recorded through a pre-deployed system. This data is important for analyzing system performance and preventing future faults. Finally, the status of circuit breakers, transformers, and switches is monitored in real time through sensors to prevent potential equipment failures and grid accidents.

[0090] In specific embodiments, smart sensors and edge computing devices can be employed. These devices can perform preliminary data processing and analysis simultaneously with data acquisition, reducing data transmission latency and load. Unified data acquisition standards and protocols should be established to ensure seamless integration and collaborative operation of sensors and data acquisition devices of different types and brands. Encryption and security measures should be implemented during data acquisition to protect data from unauthorized access or tampering. High-speed communication and real-time data processing technologies should be employed to ensure real-time data transmission and processing, meeting the needs of real-time monitoring and rapid response.

[0091] This embodiment, by installing sensors and data acquisition devices at key points, enables comprehensive collection of critical data from the distributed power system. Real-time data acquisition provides accurate information on the system's current status, crucial for real-time monitoring and rapid response. Power quality monitoring allows for the timely detection and resolution of power quality issues, improving the stability and reliability of the power grid. Recording faults and maintenance activities enables analysis of fault causes, optimization of maintenance strategies, and reduction of unexpected downtime.

[0092] Furthermore, in some embodiments, "classifying distributed power source data" in step S1 may specifically include:

[0093] Obtain the power type information of the distributed power source data, classify the distributed power source data based on the power type information, and obtain the corresponding power type classification results;

[0094] Obtain data type and usage type information of distributed power source data, classify the distributed power source data based on the data type and usage type information, and obtain the corresponding data type classification results.

[0095] Specifically, for the data classification in step S1, type information for each power source is collected based on its type (e.g., solar, wind, hydropower). The collected power source type information is used to classify the distributed power source data, enabling targeted analysis and processing of data from different types of power sources. In addition to power source type information, data type information (e.g., power generation output data, grid-connected power quality data) and usage type information (e.g., for grid dispatching, equipment maintenance) are also collected. Combining data type and usage type information, the distributed power source data is further classified to achieve more refined data management and analysis. Furthermore, an automated classification system can be developed to automatically identify the data type and usage, and categorize it accordingly. Smart tags are automatically assigned to the data, containing information such as power source type, data type, and usage type, to facilitate data retrieval and analysis. Data classification rules are dynamically adjusted based on system operation and data analysis needs to improve classification accuracy and efficiency.

[0096] In specific embodiments, since distributed power sources include various types such as solar, wind, and hydropower, the generated data is not only large in volume but also highly complex. Therefore, it is necessary to perform refined classification and processing of this data. In addition to classifying data from different types of distributed power sources, it is also possible to further categorize the data based on its type and purpose, as shown in Figure 2. The categories for distributed power source data include:

[0097] (1) Power generation data: real-time power generation and future projected power generation. Real-time power generation refers to the actual electrical energy generated by distributed power sources at the current moment, while future projected power generation is a prediction of the electrical energy that distributed power sources may generate in the future based on historical data and meteorological information. These data are of great significance for grid dispatch and load balancing.

[0098] (2) Grid-connected power quality data: voltage, frequency, harmonics, etc. Voltage and frequency are core indicators for evaluating power quality, while harmonics reveal nonlinear components in the power supply. Nonlinear components are non-fundamental frequency components in the grid voltage or current waveform, and their frequencies are integer multiples of the fundamental frequency (usually 50Hz or 60Hz). They can cause voltage distortion, increased line losses, and malfunctions of protection devices. These data are crucial for ensuring stable grid operation and improving power quality.

[0099] (3) Event log data: fault records, operation records, maintenance activities, etc. Through in-depth analysis of this data, we can gain insight into the operating status and performance of distributed power sources, thereby identifying and addressing existing problems in a timely manner.

[0100] (4) Status data: circuit breaker status, transformer status, switch status, etc. These data play a crucial role in monitoring the health status of equipment and preventing potential faults.

[0101] This embodiment, through refined data classification, can quickly locate and process specific types of data, improving data processing efficiency. Different types and uses of data require different analysis methods; precise classification ensures the use of the most appropriate analysis method, improving analysis accuracy. Classified data management allows for more rational allocation of system resources, such as storage space, processing power, and monitoring priorities. In the event of anomalies or faults, relevant data can be quickly located, accelerating system response and fault handling capabilities. With the addition of new types of distributed power sources, the flexible data classification system can easily adapt to new data types without large-scale system modifications.

[0102] Furthermore, in some embodiments, step S2, "real-time multi-dimensional monitoring of the classified distributed power source data to obtain corresponding multi-dimensional power source monitoring data," may specifically include:

[0103] Real-time monitoring of power generation data of distributed power systems of different power types, including real-time power generation and future predicted power generation;

[0104] Real-time monitoring of grid-connected power quality data for distributed power systems of different power types, including voltage, frequency, and harmonics;

[0105] Real-time monitoring of event log data for distributed power systems of different power types, including fault logs, operation logs, and maintenance activity logs;

[0106] Real-time monitoring of status data of distributed power systems with different power types, including circuit breaker status data, transformer status data, and switch status data.

[0107] Specifically, for step S2, after classifying the distributed power source data according to data type and purpose, a combination of multidimensional data monitoring and deep data analysis is used to monitor and analyze the collected distributed power source data to ensure data accuracy. First, multidimensional data monitoring is used to monitor the data in real time from multiple angles and levels, according to the above classification. The method for monitoring distributed power source data is shown in Figure 3.

[0108] This embodiment, through real-time monitoring, can promptly capture changes in the state of the distributed power system, providing support for rapid response; accurate power generation output and power quality data contribute to grid dispatching and load balancing, improving power supply reliability; real-time monitoring of power quality allows for timely detection and correction of power quality problems, reducing grid losses and equipment failures; real-time monitoring of event logs and state data enables prediction of equipment failures, optimization of maintenance plans, and reduction of maintenance costs; real-time monitoring of equipment status allows for timely detection of potential safety hazards, preventing grid accidents.

[0109] Furthermore, in some embodiments, step S3, "to perform mining analysis on the multi-dimensional power monitoring data, identify abnormal data points, and preprocess the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data," may specifically include:

[0110] Machine learning algorithms are used to extract features from multi-dimensional power monitoring data to obtain corresponding operational characteristics.

[0111] Based on operational characteristics, a data anomaly detection model is constructed;

[0112] Based on the data anomaly detection model, the real-time multi-dimensional power monitoring data is mined, analyzed and processed to identify the corresponding abnormal data points and their abnormal behaviors.

[0113] Based on outlier data points, data cleaning and outlier denoising are performed on multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data.

[0114] Specifically, for step S3, as shown in Figure 3, deep data analysis is used to deeply mine and analyze these multi-dimensional data. The core objective is to identify data points that exceed the normal range or do not conform to expected patterns, and to perform data cleaning, outlier denoising, etc., to ensure the reliability of the data. The specific process is as follows: Machine learning algorithms are used to extract features from the multi-dimensional power monitoring data to obtain corresponding operational characteristics. These characteristics include average power output, maximum load ratio, power generation fluctuation, power quality stability, and event recording frequency. Based on the extracted operational characteristics, a data anomaly detection model is constructed. This model can identify abnormal patterns in the data, such as sudden power drops and voltage fluctuations exceeding the normal range. The data anomaly detection model is applied to mine and analyze the real-time monitored multi-dimensional power monitoring data to identify corresponding abnormal data points and their abnormal behaviors. Based on the identified abnormal data points, data cleaning and outlier denoising are performed on the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data. This includes correcting or deleting erroneous data and filling in missing values.

[0115] This embodiment improves the accuracy and reliability of data through data cleaning and outlier denoising, providing a high-quality data foundation for subsequent analysis; the automated and adaptive anomaly detection model can promptly detect anomalies in the data, reducing false alarms and missed alarms; through in-depth analysis of the causes of anomalies, it helps to quickly locate the source of the problem and optimize the fault diagnosis and handling process; timely detection and processing of abnormal data reduces system instability or failure caused by data errors.

[0116] Furthermore, in some embodiments, step S4, "performing malfunction analysis on the preprocessed multi-dimensional power monitoring data to obtain corresponding malfunction analysis results," may specifically include:

[0117] Based on the preprocessed multi-dimensional monitoring data, storage failure risk is analyzed to obtain storage failure analysis results;

[0118] Based on the preprocessed multi-dimensional monitoring data, the risk of transmission distortion is analyzed, and the results of the transmission distortion analysis are obtained.

[0119] Based on the analysis of preprocessed multi-dimensional monitoring data, the risk of unauthorized access is analyzed, and the results of the unauthorized access analysis are obtained.

[0120] Based on the preprocessed multi-dimensional monitoring data, the risk of data tampering was analyzed, and the data tampering analysis results were obtained.

[0121] Based on the preprocessed multi-dimensional monitoring data, the risk of accidental data deletion is analyzed, and the results of the accidental data deletion analysis are obtained.

[0122] Specifically, for step S4, simply obtaining accurate distributed power source data is insufficient to further improve the accuracy of distributed power system analysis and decision-making. This is because, in actual operation, the risk of data misoperation always exists due to various reasons, such as human error and equipment failure. Therefore, after obtaining accurate distributed power source data, it is essential to consider how to generate effective data error prevention strategies in the event of data misoperation to ensure the integrity and security of the data are not compromised.

[0123] However, to generate an effective data error prevention strategy, it is necessary to first analyze the various possibilities of data misoperation, providing strong support for developing targeted error prevention strategies. Data misoperation is mainly analyzed based on various errors and anomalies that may occur during data storage, transportation, access, and processing, as shown in Figure 4, including but not limited to the following types:

[0124] (1) Storage failure: A situation in which data cannot be accurately stored or retrieved during the storage process due to factors such as damage to the storage device, program defects, or external conditions (e.g., power failure). This situation may result in data loss or corruption, thereby interfering with the normal functioning of the entire system.

[0125] (2) Transmission distortion: During data transmission, network delays, signal interference, packet loss, and other reasons may cause the received data to be inconsistent with the originally sent data, i.e., transmission error. This error may impair the integrity and accuracy of the data, thus adversely affecting analysis and judgment.

[0126] (3) Unauthorized access: Unauthorized users or applications perform access to or manipulation of data. Such unauthorized access may lead to data leakage, modification or damage, seriously endangering the system's protection capabilities and the integrity of information.

[0127] (4) Data tampering: When insiders or outsiders intentionally or unintentionally change the content of information, it becomes mismatched with the original information, which will distort decision analysis.

[0128] (5) Accidental data deletion: Critical data may be accidentally deleted due to negligence, operational errors, or system problems by maintenance personnel. Such accidental deletion often results in data loss and makes accurate analysis difficult.

[0129] Based on the classification of distributed power source data and examples of data misoperation: A wind power station collects and monitors its power output data hourly. After transmission, the data reaches the data center. If maintenance personnel unintentionally delete three hours of power output data while accessing the data, and then use this incomplete data to predict power output, this prediction will lead to wasted resources or insufficient power supply. Similarly, different types of distributed power source data will affect the system's analysis and judgment after different data misoperations.

[0130] In specific embodiments, based on preprocessed multi-dimensional monitoring data, risk factors that may lead to data storage failure are analyzed, such as storage device failure and data write errors. The risk of data distortion during transmission is analyzed, including network latency, packet loss, and signal interference. The risk of unauthorized access to data is identified and analyzed, including security issues such as intrusion detection and privilege abuse. The risk of data tampering is detected and analyzed to ensure data integrity and originality. The risk of accidental deletion of critical data due to operational errors or system malfunctions is analyzed.

[0131] As can be seen, this embodiment improves data security and integrity by comprehensively analyzing various risks of misoperation and taking effective preventive measures; real-time monitoring and rapid response mechanisms enable the system to recover from potential data misoperations more quickly, enhancing system resilience; risk assessment models help determine the priority of resource allocation and optimize the management of storage and transmission resources; by strengthening data protection measures, the compliance of system operations is improved, meeting the requirements of data protection regulations; and timely identification and response to risks of misoperation reduces economic losses and reputational damage caused by data errors, loss, or tampering.

[0132] Furthermore, in some embodiments, step S5, "generating a corresponding anti-misoperation strategy based on the misoperation analysis results," may specifically include:

[0133] Based on the analysis of the types of misoperations and their impact, a misoperation prevention strategy is generated for each type of misoperation. The misoperation prevention strategy includes data backup and recovery strategy, access control and permission management strategy, data encryption and protection strategy, and data verification and validation strategy.

[0134] By using filtering and principal component analysis algorithms in machine learning, we can perform deep learning on historical distributed power source data to extract behavioral characteristics and patterns during normal operation.

[0135] In the process of real-time data monitoring, behavioral patterns are applied to identify potential abnormal behaviors, and abnormal behavior identification results are obtained.

[0136] Adjust the anti-misoperation strategy based on the abnormal behavior identification results.

[0137] Specifically, for step S5, after in-depth analysis of the types of data misoperations, targeted prevention strategies can be formulated based on the type of misoperation and its impact. This can fundamentally prevent or minimize the adverse effects of data misoperations on the system, thereby improving system stability. The data prevention strategies formulated according to different types of data misoperations are as follows:

[0138] (1) Data backup and recovery: Regularly back up important data and ensure the integrity and availability of the backup data. In case of data storage failure or data deletion, the loss can be reduced by restoring the backup data.

[0139] (2) Access control and permission management: Ensure that only authorized users or programs can access and operate critical data. By setting different levels of permissions, limit the scope of operation of users or programs to prevent unauthorized access and data tampering.

[0140] (3) Data encryption and protection: Sensitive data is encrypted to ensure its security during transmission and storage. Simultaneously, security protocols are employed to protect the integrity and confidentiality of data transmission.

[0141] (4) Data verification and validation: Data verification and validation mechanisms are introduced during data transmission. These mechanisms can detect errors during data transmission and ensure that the data received by the receiver is consistent with the data sent by the sender. If an error is found, the sender can be asked to resend the data.

[0142] Meanwhile, in formulating error prevention strategies, filtering and principal component analysis (PCA) algorithms from machine learning are used for in-depth analysis and learning of large amounts of historical data. This allows for the precise extraction of behavioral characteristics and patterns during normal system operation. The extracted patterns are then applied to the real-time data monitoring process to identify potential abnormal behaviors. Based on the identification results, corresponding preventative measures or adjustment strategies can be implemented. For example, an abnormal increase in traffic or unusual access requests can be identified, allowing for preventative measures or adjustment strategies such as strengthening firewall rules and restricting access from suspicious IP addresses to prevent potential network attacks or security threats.

[0143] The process of forming a mis-prevention strategy using filtering and principal component analysis is shown in Figure 5, and includes the following steps:

[0144] (1) Data collection: Collect a large amount of historical data, which should cover various situations when the system is running normally, including server logs, user behavior data, etc.

[0145] (2) Data preprocessing: The collected data is denoised, outliers are processed and standardized to improve data quality.

[0146] (3) Feature selection (filtering method): Select the most relevant subset of features from the original features, such as calculating the current fluctuation rate from the current data and the voltage deviation from the voltage data.

[0147] (4) Feature extraction (principal component analysis): Principal component analysis maps the original data to a low-dimensional space while retaining the main variation information of the data, such as calculating the correlation between current, voltage and power.

[0148] (5) Model selection and training: The model is built using a classification algorithm, and the selected algorithm is trained using the data after feature selection and feature extraction so that it can learn the normal behavior pattern of the system.

[0149] (6) Real-time monitoring: Deploy the trained model to real-time monitoring, use the model to analyze new data streams, compare them with the learned normal behavior patterns, and identify potential abnormal behaviors, such as deleting a large amount of maintenance activity data.

[0150] (7) Anomaly detection and response: When an abnormal behavior is detected, corresponding preventive measures or adjustment strategies will be triggered, such as starting a recovery backup and sending an abnormal signal to the operation and maintenance personnel.

[0151] (8) Continuous learning and optimization: Regularly reviewing and updating the model, continuous learning and optimization can keep the model efficient and accurate and adapt to the ever-changing environment.

[0152] This embodiment significantly improves the security and protection level of data in distributed power systems through comprehensive anti-misoperation strategies; it promptly identifies and responds to abnormal behaviors, reducing system failures and data errors, and enhancing system stability and reliability; it utilizes system resources more effectively and improves operational efficiency through intelligent strategy adjustments; the adaptive learning system enables strategies to adapt to constantly changing environments and needs, maintaining the timeliness and effectiveness of strategies; and automated and intelligent strategy generation and adjustment reduce human error and intervention, improving operational accuracy and efficiency.

[0153] As can be seen, this embodiment provides an implementation method for a distributed power source data error prevention strategy analysis method based on machine learning. As shown in Figure 6, firstly, the large amount of complex data generated by the distributed power source is finely classified, including power generation output data, grid-connected power quality data, etc., to facilitate grid dispatching, load balancing, and equipment health monitoring. Subsequently, through a combination of multi-dimensional data monitoring and deep data analysis, these data are monitored in real time and anomalies are identified to ensure data accuracy and reliability. In addition, for potential data misoperation risks in actual operation, such as storage failure and transmission distortion, the types of these misoperations and their impact on the system are analyzed in detail, and a series of targeted error prevention strategies are proposed based on this, including data backup and recovery measures. Finally, filtering and principal component analysis methods in machine learning are used to perform deep learning on historical data to extract the behavioral characteristics and patterns of normal system operation, which are applied to real-time data monitoring to prevent potential abnormal behavior.

[0154] In summary, this embodiment significantly improves the stability and reliability of distributed power systems. It employs effective error prevention strategies to address the risk of data misoperation, ensuring data integrity and security and preventing resource waste or power shortages caused by data errors. Furthermore, the use of advanced data analysis techniques, such as filtering and principal component analysis, not only improves the accuracy and efficiency of data analysis but also proactively prevents potential network attacks or security threats, further enhancing the overall security capabilities of the distributed power system. Overall, this embodiment provides strong technical support for the efficient, stable, and secure operation of distributed power systems.

[0155] Compared with existing technologies, the distributed power source data anti-misoperation strategy analysis method provided in this embodiment not only possesses the core advantages of hydropower computer monitoring system control safety strategy technology, such as misoperation prevention, data security, and control auditing, but also introduces the integrated application of refined classification, multi-dimensional data monitoring, and deep data analysis. This enables more accurate identification and prevention of misoperation risks in distributed power source data. By using machine learning algorithms to perform multi-dimensional data monitoring of distributed power source data, potential abnormal behaviors can be effectively prevented in real-time monitoring. This not only fundamentally improves the stability and reliability of distributed power source systems but also provides more advanced and comprehensive technical support for the safe operation of smart grids.

[0156] As can be seen, the distributed power source data anti-misoperation strategy analysis scheme proposed in this embodiment improves the accuracy and reliability of distributed power source system data processing by combining refined classification, multi-dimensional data monitoring and in-depth data analysis, while preventing and reducing the risk of data misoperation, thereby improving the operating efficiency and safety of the power system.

[0157] To facilitate better implementation of the distributed power data anti-misoperation strategy analysis method of this application embodiment, this application embodiment also provides a distributed power data anti-misoperation strategy analysis device. The meanings of the terms used are the same as in the above-described distributed power data anti-misoperation strategy analysis method, and specific implementation details can be found in the description of the method embodiment.

[0158] Please refer to Figure 7, which is a schematic diagram of the structure of the distributed power data anti-misoperation strategy analysis device provided in the embodiment of this application. The distributed power data anti-misoperation strategy analysis device may specifically include a data acquisition module 201, a monitoring module 202, a data mining module 203, an analysis module 204, and a generation module 205, as follows:

[0159] The acquisition module 201 is used to acquire distributed power source data of different power source types and classify the distributed power source data.

[0160] Monitoring module 202 is used to perform real-time multi-dimensional monitoring of the classified distributed power source data to obtain corresponding multi-dimensional power source monitoring data;

[0161] The mining module 203 is used to mine and analyze multi-dimensional power monitoring data, identify abnormal data points, and preprocess the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data.

[0162] Analysis module 204 is used to perform misoperation analysis on the preprocessed multi-dimensional power monitoring data to obtain the corresponding misoperation analysis results. The data analysis results include the misoperation type corresponding to the power monitoring data and its impact.

[0163] The generation module 205 is used to generate corresponding anti-misoperation strategies based on the results of the misoperation analysis.

[0164] In summary, the distributed power source data anti-misoperation strategy analysis device provided in this embodiment improves the accuracy and reliability of distributed power source system data processing by combining refined classification, multi-dimensional data monitoring and in-depth data analysis, while preventing and reducing the risk of data misoperation, thereby improving the operating efficiency and safety of the power system.

[0165] Furthermore, this application also provides an electronic device, as shown in FIG8, which illustrates a structural schematic diagram of the electronic device involved in this application embodiment. Specifically, the electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that the electronic device structure shown in FIG8 does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0166] The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0167] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and distributed power data anti-misoperation strategy analysis methods by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0168] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0169] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0170] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows:

[0171] Data from distributed power sources of different power types is collected and categorized. Real-time multi-dimensional monitoring is performed on the categorized distributed power source data to obtain corresponding multi-dimensional power source monitoring data. Mining and analysis are conducted on the multi-dimensional power source monitoring data to identify abnormal data points. These abnormal data points are then preprocessed to obtain preprocessed multi-dimensional power source monitoring data. Misoperation analysis is performed on the preprocessed multi-dimensional power source monitoring data to obtain corresponding misoperation analysis results, including the types of misoperations corresponding to the power source monitoring data and their impact. Based on the misoperation analysis results, corresponding anti-misoperation strategies are generated.

[0172] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0173] This application's embodiments improve the accuracy and reliability of distributed power system data processing by combining refined classification, multi-dimensional data monitoring, and in-depth data analysis, while preventing and reducing the risk of data misoperation, thereby improving the operating efficiency and safety of the power system.

[0174] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0175] Therefore, embodiments of this application provide a storage medium storing multiple instructions that can be loaded by a processor to execute steps in any of the distributed power data anti-misoperation strategy analysis methods provided in embodiments of this application. For example, the instructions can execute the following steps:

[0176] Data from distributed power sources of different power types is collected and categorized. Real-time multi-dimensional monitoring is performed on the categorized distributed power source data to obtain corresponding multi-dimensional power source monitoring data. Mining and analysis are conducted on the multi-dimensional power source monitoring data to identify abnormal data points. These abnormal data points are then preprocessed to obtain preprocessed multi-dimensional power source monitoring data. Misoperation analysis is performed on the preprocessed multi-dimensional power source monitoring data to obtain corresponding misoperation analysis results, including the types of misoperations corresponding to the power source monitoring data and their impact. Based on the misoperation analysis results, corresponding anti-misoperation strategies are generated.

[0177] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0178] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk, or optical disk, etc. Since the instructions stored in the storage medium can execute the steps in any of the distributed power data anti-misoperation strategy analysis methods provided in the embodiments of this application, the beneficial effects achievable by any of the distributed power data anti-misoperation strategy analysis methods provided in the embodiments of this application can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0179] The above provides a detailed description of the distributed power data anti-misoperation strategy analysis method, device, electronic device, and storage medium provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for analyzing distributed power source data anti-misoperation strategies, characterized in that, Includes the following steps: Collect distributed power source data of different power source types and classify the distributed power source data; Real-time multi-dimensional monitoring is performed on the classified distributed power source data to obtain corresponding multi-dimensional power source monitoring data. The multi-dimensional power monitoring data is mined and analyzed to identify abnormal data points. The abnormal data points in the multi-dimensional power monitoring data are then preprocessed to obtain preprocessed multi-dimensional power monitoring data. The preprocessed multi-dimensional power monitoring data is subjected to misoperation analysis to obtain corresponding misoperation analysis results. The data analysis results include the misoperation type and its impact corresponding to the power monitoring data. Based on the results of the erroneous operation analysis, a corresponding erroneous operation prevention strategy is generated.

2. The distributed power supply data anti-misoperation strategy analysis method according to claim 1, characterized in that, The collection of distributed power source data of different power types includes: Pre-install corresponding sensors and data acquisition equipment at each distributed power source point, and install power quality monitoring equipment at the grid connection point; Real-time power generation is collected through the sensor and the data acquisition device; The power quality monitoring equipment is used to monitor voltage, frequency, and harmonics. The system records fault records, operation records, and maintenance activity records of distributed power points through a pre-deployed system; The sensors monitor the status of circuit breakers, transformers, and switches in real time.

3. The distributed power supply data anti-misoperation strategy analysis method according to claim 2, characterized in that, The classification of the distributed power source data includes: Obtain the power type information of the distributed power data, classify the distributed power data based on the power type information, and obtain the corresponding power type classification result; Obtain the data type information and usage type information of the distributed power source data, classify the distributed power source data based on the data type information and usage type information, and obtain the corresponding data type classification results.

4. The distributed power supply data anti-misoperation strategy analysis method according to claim 1, characterized in that, The real-time multi-dimensional monitoring of the categorized distributed power source data to obtain corresponding multi-dimensional power source monitoring data includes: Real-time monitoring of power generation data of distributed power systems of different power types, including real-time power generation and future predicted power generation; Real-time monitoring of grid-connected power quality data of distributed power systems of different power types, wherein the grid-connected power quality data includes voltage, frequency and harmonics; Real-time monitoring of event log data of distributed power systems with different power types, including fault records, operation records and maintenance activity records; Real-time monitoring of status data of distributed power systems with different power types, including circuit breaker status data, transformer status data, and switch status data.

5. The distributed power supply data anti-misoperation strategy analysis method according to claim 1, characterized in that, The process of mining and analyzing the multi-dimensional power monitoring data to identify abnormal data points, and then preprocessing the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data, includes: The corresponding operational characteristics are obtained by extracting features from the multi-dimensional power monitoring data using machine learning algorithms. Based on the aforementioned operational characteristics, a data anomaly detection model is constructed; Based on the data anomaly detection model, the multi-dimensional power monitoring data monitored in real time is mined, analyzed and processed to identify the corresponding abnormal data points and their abnormal behaviors. Based on the abnormal data points, the multi-dimensional power monitoring data is cleaned and denoised to obtain preprocessed multi-dimensional power monitoring data.

6. The distributed power supply data anti-misoperation strategy analysis method according to claim 1, characterized in that, The step of performing erroneous operation analysis on the preprocessed multi-dimensional power monitoring data to obtain corresponding erroneous operation analysis results includes: Based on the preprocessed multi-dimensional monitoring data, the storage failure risk is analyzed to obtain the storage failure analysis results. Based on the preprocessed multi-dimensional monitoring data, the risk of transmission distortion is analyzed, and the transmission distortion analysis results are obtained. Based on the preprocessed multi-dimensional monitoring data, the risk of unauthorized access is analyzed to obtain the results of the unauthorized access analysis. Based on the preprocessed multi-dimensional monitoring data, the risk of data tampering is analyzed, and the data tampering analysis results are obtained. Based on the preprocessed multi-dimensional monitoring data, the risk of accidental data deletion is analyzed, and the results of the accidental data deletion analysis are obtained.

7. The distributed power supply data anti-misoperation strategy analysis method according to claim 1, characterized in that, The step of generating a corresponding anti-misoperation strategy based on the misoperation analysis results includes: Based on the types of misoperations and their impact in the analysis results, a misoperation prevention strategy is generated for each type of misoperation. The misoperation prevention strategy includes data backup and recovery strategy, access control and permission management strategy, data encryption and protection strategy, and data verification and validation strategy. By using filtering and principal component analysis algorithms in machine learning, we can perform deep learning on historical distributed power source data to extract behavioral characteristics and patterns during normal operation. The aforementioned behavior pattern is applied during real-time data monitoring to identify potential abnormal behaviors, thereby obtaining abnormal behavior identification results. The anti-misoperation strategy is adjusted based on the abnormal behavior identification results.

8. A distributed power supply data anti-misoperation strategy analysis device, characterized in that, include: The acquisition module is used to acquire distributed power data of different power types and classify the distributed power data. The monitoring module is used to perform real-time multi-dimensional monitoring of the classified distributed power data to obtain corresponding multi-dimensional power monitoring data. The mining module is used to perform mining analysis on the multi-dimensional power monitoring data, identify abnormal data points, and preprocess the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data. The analysis module is used to perform misoperation analysis on the preprocessed multi-dimensional power monitoring data to obtain corresponding misoperation analysis results. The data analysis results include the misoperation type corresponding to the power monitoring data and its impact. The generation module is used to generate corresponding anti-misoperation strategies based on the results of the misoperation analysis.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the distributed power data anti-misoperation strategy analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed power data anti-misoperation strategy analysis method as described in any one of claims 1 to 7.

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