Distributed power supply data anti-misoperation strategy analysis method and related equipment

Abstract

The invention provides a distributed power supply data anti-misoperation strategy analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: 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 analysis processing on the monitored multi-dimensional power supply monitoring data, and performing data preprocessing after identifying abnormal data points; 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 based on the misoperation analysis result. According to the distributed power supply data anti-misoperation strategy analysis scheme provided by the invention, the accuracy and reliability of data processing of a distributed power supply system are improved through a mode of combining refined classification, multi-dimensional data monitoring and deep data analysis, and meanwhile, the risk of data misoperation is prevented and reduced, so that the reliability of the distributed power supply system is improved. Therefore, the operation efficiency and safety of the power system are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a distributed power supply data misoperation prevention strategy analysis method, device, electronic device and storage medium. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy technologies, distributed power systems (such as solar energy, wind energy, and hydropower) play 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. The amount of data generated by distributed power systems is huge and complex, involving multiple dimensions such as power generation output, power quality, event records, and equipment status. These data are essential for the dispatching, load balancing, and equipment maintenance of power grids. However, due to the massive amount and complexity of data, traditional data processing methods face the problems of low efficiency and insufficient accuracy. In addition, the risks of misoperation that may occur during the storage, transmission, access, and processing of data, such as storage failure, transmission distortion, illegal access, data tampering, and data deletion, may have an adverse effect on the normal operation of the system.

[0003] In the prior art, the prevention of misoperation of data in distributed power systems mainly relies on manual regular inspection and review, as well as recording detailed logs of data operations for subsequent tracing and analysis. These methods are not only inefficient, but also easily affected by human factors, and cannot achieve real-time prevention of data misoperation. In addition, existing technical solutions often lack refined classification and multi-dimensional monitoring of data, resulting in reduced accuracy in capturing abnormal risks and a large consumption of time and human resources. Therefore, there is an urgent need for an intelligent and efficient data analysis method to ensure the accuracy and reliability of data and prevent potential risks of data misoperation.

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

[0005] In response to the above-mentioned technical problems, the present application provides a distributed power supply data anti-misoperation strategy analysis method, device, electronic device and storage medium to solve the problems of low efficiency, insufficient accuracy and high cost caused by reliance on manual inspection in the prior art, effectively improve the accuracy and reliability of data processing in the distributed power supply system, and at the same time prevent and reduce the risk of data misoperation, thereby improving the operating efficiency and safety of the power system.

[0006] In order to solve the above technical problems, the present application provides a distributed power supply data misoperation prevention strategy analysis method, comprising the following steps:

[0007] Collecting distributed power data of different power types and classifying the distributed power data;

[0008] Performing real-time multi-dimensional monitoring on the classified distributed power supply data to obtain corresponding multi-dimensional power supply monitoring data;

[0009] Performing mining and analysis on the multi-dimensional power supply monitoring data, identifying abnormal data points, and performing data preprocessing on the abnormal data points in the multi-dimensional power supply monitoring data to obtain preprocessed multi-dimensional power supply monitoring data;

[0010] Performing error operation analysis on the pre-processed multi-dimensional power monitoring data to obtain corresponding error operation analysis results, wherein the data analysis results include the error operation type corresponding to the power monitoring data and its error operation impact;

[0011] Based on the error operation analysis result, a corresponding error operation prevention strategy is generated.

[0012] Furthermore, in some embodiments of the present application, the collecting of distributed power supply data of different power supply 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] Collecting real-time power generation through the sensor and the data acquisition device;

[0015] Monitoring voltage, frequency and harmonics by means of the power quality monitoring device;

[0016] Record fault records, operation records and maintenance activity records of distributed power points through pre-deployed systems;

[0017] The status of the circuit breaker, transformer and switch are monitored in real time by the sensor.

[0018] Further, in some embodiments of the present application, the classifying the distributed power supply data includes:

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

[0020] The data type information and the usage type information of the distributed power supply data are obtained, and the distributed power supply data is classified based on the data type information and the usage type information to obtain a corresponding data type classification result.

[0021] Further, in some embodiments of the present application, the real-time multi-dimensional monitoring of the classified distributed power supply data to obtain corresponding multi-dimensional power supply monitoring data includes:

[0022] Real-time monitoring of power generation output data of distributed power systems of different power types, the power generation output data 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 record data of distributed power systems of different power types, the event record data including fault records, operation records and maintenance activity records;

[0025] Real-time monitoring of state quantity data of distributed power supply systems of different power supply types, wherein the state quantity data includes circuit breaker state data, transformer state data and switch state data.

[0026] Further, in some embodiments of the present application, the multi-dimensional power monitoring data is mined and analyzed to identify abnormal data points, and the abnormal data points in the multi-dimensional power monitoring data are preprocessed to obtain preprocessed multi-dimensional power monitoring data, including:

[0027] Extracting features from the multi-dimensional power supply monitoring data using a machine learning algorithm to obtain corresponding operating features;

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

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

[0030] Based on the abnormal data points, data cleaning and abnormal value denoising are performed on the multi-dimensional power supply monitoring data to obtain pre-processed multi-dimensional power supply monitoring data.

[0031] Further, in some embodiments of the present application, performing misoperation analysis on the pre-processed multi-dimensional power supply monitoring data to obtain corresponding misoperation analysis results includes:

[0032] Analyze the storage failure risk based on the pre-processed multi-dimensional monitoring data to obtain a storage failure analysis result;

[0033] Analyzing the transmission distortion risk based on the pre-processed multi-dimensional monitoring data to obtain a transmission distortion analysis result;

[0034] Analyze the illegal access risk based on the pre-processed multi-dimensional monitoring data to obtain illegal access analysis results;

[0035] Analyze the data tampering risk based on the pre-processed multi-dimensional monitoring data to obtain the data tampering analysis result

[0036] The risk of accidental data deletion is analyzed based on the pre-processed multi-dimensional monitoring data to obtain an analysis result of accidental data deletion.

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

[0038] Based on the types of erroneous operations and their impacts in the erroneous operation analysis results, an anti-erroneous operation strategy corresponding to each type of erroneous operation is generated, wherein the anti-erroneous operation strategy includes a data backup and recovery strategy, an access control and authority management strategy, a data encryption and protection strategy, and a data check and verification strategy;

[0039] Use the filtering method and principal component analysis method in the machine learning algorithm to conduct deep learning on historical distributed power data to extract the behavioral characteristics and behavior patterns during normal operation;

[0040] Applying the behavior pattern to identify potential abnormal behaviors during real-time data monitoring to obtain abnormal behavior identification results;

[0041] The anti-misoperation strategy is adjusted according to the abnormal behavior identification result.

[0042] Accordingly, the present application also provides a distributed power supply data misoperation prevention strategy analysis device, comprising:

[0043] A collection module, used to collect distributed power data of different power types and classify the distributed power data;

[0044] A monitoring module, used to perform real-time multi-dimensional monitoring of the classified distributed power supply data to obtain corresponding multi-dimensional power supply monitoring data;

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

[0046] An analysis module, used to perform an error operation analysis on the pre-processed multi-dimensional power monitoring data to obtain a corresponding error operation analysis result, wherein the data analysis result includes an error operation type corresponding to the power monitoring data and its error operation impact;

[0047] A generation module is used to generate a corresponding anti-misoperation strategy based on the misoperation analysis result.

[0048] The present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the distributed power supply data misoperation prevention strategy analysis method as described above are implemented.

[0049] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the distributed power supply data misoperation prevention strategy analysis method as described above are implemented.

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

[0051] As described above, the present application provides a distributed power supply data anti-misoperation strategy analysis method, device, electronic device and storage medium, and the distributed power supply data anti-misoperation strategy analysis method includes: collecting distributed power supply data of different power supply types and classifying the distributed power supply data; performing real-time multi-dimensional monitoring of the classified distributed power supply data to obtain corresponding multi-dimensional power supply monitoring data; mining and analyzing the multi-dimensional power supply monitoring data to identify abnormal data points, and performing data preprocessing on the abnormal data points in the multi-dimensional power supply monitoring data to obtain preprocessed multi-dimensional power supply monitoring data; performing misoperation analysis on the preprocessed multi-dimensional power supply monitoring data to obtain corresponding misoperation analysis results, and the data analysis results include the misoperation type corresponding to the power supply monitoring data and its misoperation impact; based on the misoperation analysis results, generating corresponding anti-misoperation strategies. The distributed power supply data anti-misoperation strategy analysis scheme proposed in the present application improves the accuracy and reliability of distributed power 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0053] Figure 1 It is a flow chart of a distributed power supply data misoperation prevention strategy analysis method provided in an embodiment of the present application;

[0054] Figure 2 It is a schematic diagram of the distributed power supply data division type provided in an embodiment of the present application;

[0055] Figure 3 is a schematic diagram of a process for monitoring and analyzing distributed power supply data provided by an embodiment of the present application;

[0056] Figure 4 is a schematic diagram of data misoperation types provided in an embodiment of the present application;

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

[0058] Figure 6 It is another flow chart of the distributed power supply data misoperation prevention strategy analysis method provided in an embodiment of the present application;

[0059] Figure 7 It is a structural schematic diagram of a distributed power supply data misoperation prevention strategy analysis device provided in an embodiment of the present application;

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

[0061] The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The above-mentioned drawings have shown clear embodiments of this application, which will be described in more detail later. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0062] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0063] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0064] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0066] With the widespread application of renewable energy, the proportion of distributed power systems in power systems is gradually increasing. The amount of data generated by these systems is huge and complex, so in actual operation, distributed power systems may encounter various risks of data misoperation, such as storage failure, illegal access, etc. These misoperations may have an adverse effect on the system. Traditional data error prevention strategies may not be able to effectively cope with these challenges, such as: relying on manual regular inspection and review of data to detect potential errors or anomalies; recording detailed logs of data operations for tracing and analysis when problems arise. These methods are inefficient and easily affected by human factors, and cannot prevent data misoperation in real time. Therefore, a more intelligent and efficient data analysis method is needed to ensure the accuracy and reliability of data and prevent potential data misoperation risks.

[0067] In response to the above technical problems, the embodiments of the present application provide a distributed power supply data anti-misoperation strategy analysis method, device, electronic device and storage medium, which improves the accuracy and reliability of distributed power 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.

[0068] The embodiments of the present application provide a distributed power supply data misoperation prevention strategy analysis method, device, electronic device and storage medium.

[0069] Among them, the distributed power supply data anti-misoperation strategy analysis device can be deployed in a terminal or a server. The server can include an independently running server or a distributed server, or a server cluster composed of multiple servers. The terminal can include a mobile phone, a tablet computer or a personal computer (PC, Personal Computer).

[0070] It should be noted that the order of description of the following embodiments is not intended to limit the priority order of the embodiments.

[0071] See also Figure 1 , Figure 1 The flowchart of the distributed power data anti-misoperation strategy analysis method provided in the embodiment of the present application is as follows. The distributed power data anti-misoperation strategy analysis method provided in the present embodiment can be executed in the power system, and the distributed power data anti-misoperation strategy analysis method can specifically include the following steps:

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

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

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

[0075] Specifically, for step S2, the classified data is monitored in real time to ensure that any abnormal changes in the data can be captured in a timely manner. The dimensions of multi-dimensional monitoring include power generation output, power quality, event records, and equipment status.

[0076] S3. Perform mining and 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;

[0077] Specifically, for step S3, the monitored data is deeply mined and analyzed to identify abnormal points in the data. The abnormal data is preprocessed, including data cleaning and outlier denoising, to ensure the quality of the data. The data analysis process can use machine learning algorithms and big data analysis tools to improve the accuracy and efficiency of anomaly detection.

[0078] S4. Perform misoperation analysis on the pre-processed multi-dimensional power monitoring data to obtain corresponding misoperation analysis results, where the data analysis results include misoperation types and misoperation impacts corresponding to the power monitoring data;

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

[0080] S5. Generate a corresponding anti-misoperation strategy based on the misoperation analysis results;

[0081] Specifically, for step S5, a corresponding anti-misoperation strategy is generated according to the result of the misoperation analysis to prevent or mitigate the adverse effects caused by misoperation of data.

[0082] It can be seen that 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 illegal 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 the cost and workload of system maintenance by predicting potential data problems and automating misoperation prevention; 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] Collect real-time power generation through sensors and data acquisition equipment;

[0086] Monitor voltage, frequency and harmonics through power quality monitoring equipment;

[0087] Record fault records, operation records and maintenance activity records of distributed power points through pre-deployed systems;

[0088] Sensors monitor the status of circuit breakers, transformers and switches in real time.

[0089] Specifically, for the data collection 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, and power quality monitoring devices are installed at the grid connection point to monitor power quality indicators such as voltage, frequency and harmonics. Then, the real-time power generation is collected through sensors and data acquisition devices, which is the key data for evaluating the performance of distributed power sources. Voltage, frequency and harmonics are monitored by power quality monitoring equipment, which are essential to ensure grid stability and power quality. Next, the fault records, operation records and maintenance activity records of distributed power source points are recorded through the pre-deployed system, which are very important for analyzing system performance and preventing future failures. And 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 used, which can perform preliminary data processing and analysis while collecting data, reducing the delay and load of data transmission. Unified data collection standards and protocols are formulated to ensure that sensors and data collection devices of different types and brands can be seamlessly integrated and work together. Encryption and security measures are implemented during the data collection process to protect data from unauthorized access or tampering. High-speed communication technology and real-time data processing technology are used to ensure that data can be transmitted and processed in real time to meet the needs of real-time monitoring and rapid response.

[0091] This embodiment can comprehensively collect key data of the distributed power system by installing sensors and data acquisition equipment at key points. Real-time data acquisition can provide accurate information about the current state of the system, which is essential for real-time monitoring and rapid response. Through power quality monitoring, power quality problems can be discovered and solved in a timely manner, improving the stability and reliability of the power grid. By recording faults and maintenance activities, the causes of faults can be analyzed, maintenance strategies can be optimized, and unexpected downtime can be reduced.

[0092] Furthermore, in some embodiments, the “classifying the distributed power supply data” in step S1 may specifically include:

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

[0094] The data type information and usage type information of the distributed power source data are obtained, and the distributed power source data are classified based on the data type information and usage type information to obtain corresponding data type classification results.

[0095] Specifically, for the data classification in step S1, the type information of each power source point is collected according to the type of distributed power source (such as solar energy, wind energy, hydropower, etc.). The distributed power source data is classified using the collected power source type information so as to conduct targeted analysis and processing of the data of different types of power sources. In addition to the power source type information, the type information of the data (such as power generation output data, grid-connected power quality data, etc.) and the use type information (such as for power grid dispatching, equipment maintenance, etc.) are also collected. Combined with the data type and use type information, the distributed power source data is further classified to achieve more refined data management and analysis. In addition, an automated classification system can also be developed to automatically identify the type and use of data and classify it into corresponding categories. Intelligent labels are automatically assigned to the data, which contain information such as power source type, data type and use type to facilitate data retrieval and analysis. According to the operation of the system and the needs of data analysis, the data classification rules are dynamically adjusted to improve the accuracy and efficiency of classification.

[0096] In a specific embodiment, since distributed power sources include solar energy, wind energy, hydropower and other types, the data generated is not only large in volume but also highly complex, so it is necessary to classify and process these data in a refined manner. In addition to classifying the data of different types of distributed power sources, it is also possible to classify them according to the type and purpose of the data, such as Figure 2 As shown, the categories of distributed power data include:

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

[0098] (2) Grid-connected power quality data: voltage, frequency, harmonics, etc. Voltage and frequency are the core indicators for evaluating power quality, while harmonics reveal the nonlinear components in power. 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 50 Hz or 60 Hz). They may cause voltage distortion, increase line losses, and cause malfunction of protection devices. These data are crucial to ensuring stable operation of the grid and improving power quality.

[0099] (3) Event record data: fault records, operation records, maintenance activities, etc. Through in-depth analysis of these 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 key role in monitoring the health status of equipment and preventing potential failures.

[0101] Through refined data classification, this embodiment can quickly locate and process specific types of data, thereby improving the efficiency of data processing. Data of different types and uses require different analysis methods. Through accurate classification, it can be ensured that the most appropriate analysis method is adopted to improve the accuracy of the analysis. Through classification management of data, system resources such as storage space, processing power and monitoring focus can be allocated more reasonably. When an abnormality or failure occurs, relevant data can be quickly located, which speeds up the system's response speed 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 the need for large-scale system modifications.

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

[0103] Real-time monitoring of the power generation output 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 of distributed power systems of different power types, including voltage, frequency and harmonics;

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

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

[0107] Specifically, for step S2, after the distributed power data is divided according to data type and purpose, a method combining multi-dimensional data monitoring and deep data analysis is used to monitor and analyze the collected distributed power data to ensure the accuracy of the data. First, multi-dimensional data monitoring is used to monitor the data in real time from multiple angles and levels, and real-time monitoring is performed according to the above classification. The method for monitoring distributed power data is as follows Figure 3 shown.

[0108] Through real-time monitoring, this embodiment can timely capture the state changes of the distributed power system and provide support for rapid response; accurate power output and power quality data are helpful for grid scheduling and load balancing, and improve the reliability of power supply; real-time monitoring of power quality can timely discover and correct power quality problems, reduce grid losses and equipment failures; through event recording and real-time monitoring of state quantity data, equipment failures can be predicted, maintenance plans can be optimized, and maintenance costs can be reduced; real-time monitoring of equipment status can timely discover potential safety hazards and prevent grid accidents.

[0109] Further, in some embodiments, step S3 of "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" may specifically include:

[0110] The multi-dimensional power supply monitoring data is extracted through machine learning algorithms to obtain the corresponding operating characteristics;

[0111] Build a data anomaly detection model based on operation characteristics;

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

[0113] Based on abnormal data points, data cleaning and outlier denoising are performed on the multi-dimensional power supply monitoring data to obtain pre-processed multi-dimensional power supply monitoring data.

[0114] Specifically, for step S3, Figure 3 As shown in the figure, deep data analysis is used to deeply mine and analyze these multi-dimensional data. The core goal is to identify those data points that are beyond the normal range or do not conform to the expected pattern, and perform data cleaning and outlier denoising to ensure the credibility of the data. The specific process is as follows: Use machine learning algorithms to extract features from multi-dimensional power monitoring data to obtain corresponding operating characteristics. These features include average power output, maximum load ratio, volatility of power output, stability of power quality, frequency of event records, etc. Based on the extracted operating 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 beyond the normal range. The data anomaly detection model is used to mine and analyze the multi-dimensional power monitoring data monitored in real time to identify the corresponding abnormal data points and their abnormal behaviors. Based on the identified abnormal data points, the multi-dimensional power monitoring data is cleaned and outlier denoised to obtain pre-processed multi-dimensional power monitoring data. This includes correcting or deleting erroneous data and filling 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 and reduce false positives and false negatives; 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 processing process; and promptly detects and processes abnormal data to reduce system instability or failures caused by data errors.

[0116] Further, in some embodiments, step S4 of "performing an error operation analysis on the pre-processed multi-dimensional power supply monitoring data to obtain a corresponding error operation analysis result" may specifically include:

[0117] Analyze storage failure risks based on pre-processed multi-dimensional monitoring data to obtain storage failure analysis results;

[0118] Analyze the transmission distortion risk based on the pre-processed multi-dimensional monitoring data to obtain the transmission distortion analysis results;

[0119] Analyze illegal access risks based on pre-processed multi-dimensional monitoring data to obtain illegal access analysis results;

[0120] Analyze the data tampering risk based on the pre-processed multi-dimensional monitoring data and obtain the data tampering analysis results

[0121] The risk of accidental data deletion is analyzed based on the preprocessed multi-dimensional monitoring data to obtain the analysis results of accidental data deletion.

[0122] Specifically, for step S4, in order to further improve the accuracy of distributed power system analysis and decision-making, it is not enough to simply obtain accurate distributed power data. Because in the actual operation process, due to various reasons, such as human operation errors, equipment failures, etc., the risk of data misoperation always exists. Therefore, after obtaining accurate distributed power data, it is necessary to consider how to generate an effective data error prevention strategy when data misoperation occurs to ensure that the integrity and security of the data are not threatened.

[0123] However, in order to generate an effective data error prevention strategy, it is necessary to first analyze the various possibilities of data error operation to provide strong support for the formulation of targeted error prevention strategies. Data error operation is mainly analyzed based on various errors and anomalies that may occur during data storage, transportation, access and processing, such as Figure 4 As shown, 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 storage device damage, program defects, or external conditions (such as power failure). This situation may cause data loss or corruption, which in turn interferes with the normal function of the entire system.

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

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

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

[0128] (5) Accidental deletion of data: Key data may be accidentally deleted due to negligence of maintenance personnel, operational errors, or system problems. Such accidental deletion often results in data loss, making it difficult to accurately analyze.

[0129] An example of data classification and data misoperation based on distributed power data: The power output data of a wind power station is monitored and collected once every hour, and then transmitted to the data center. At this time, the operation and maintenance personnel accidentally deleted three hours of power output data when accessing the data, and then used this incomplete data to predict the power output. The work based on this prediction result will lead to waste of resources or insufficient power supply. Similarly, different distributed power data types will affect the analysis and judgment of the system after different data misoperations occur.

[0130] In a specific embodiment, based on the pre-processed multi-dimensional monitoring data, analyze the risk factors that may lead to data storage failure, such as storage device failure, data write error, etc. Analyze the risk of data distortion that may occur during transmission, including network delays, packet loss, signal interference, etc. Identify and analyze the risk of unauthorized access to data, including security issues such as intrusion detection and abuse of authority. Detect and analyze the risk of data tampering to ensure the integrity and originality of the data. Analyze the risk of key data being accidentally deleted due to operational errors or system errors.

[0131] It can be seen that this embodiment improves the security and integrity of data 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 and enhance the resilience of the system; 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 to meet the requirements of data protection regulations; timely identification and response to misoperation risks reduce economic losses and reputation 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 result” may specifically include:

[0133] Based on the analysis of the types of erroneous operations and their impacts in the erroneous operation analysis results, an anti-erroneous operation strategy corresponding to each type of erroneous operation is generated. The anti-erroneous operation strategy includes data backup and recovery strategy, access control and authority management strategy, data encryption and protection strategy, and data verification and validation strategy;

[0134] Use the filtering method and principal component analysis method in the machine learning algorithm to conduct deep learning on historical distributed power data to extract the behavioral characteristics and behavior patterns during normal operation;

[0135] In the process of real-time data monitoring, the behavior pattern is applied to identify potential abnormal behaviors and obtain abnormal behavior identification results;

[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, a misoperation prevention strategy can be formulated in a targeted manner according to the types of misoperations and their impacts, which can fundamentally prevent or minimize the adverse effects of data misoperations on the system and improve the stability of the system. The data misoperation prevention strategies formulated according to different types of data misoperations are as follows:

[0138] (1) Data backup and recovery: Back up important data regularly and ensure the integrity and availability of the backup data. In the event of data storage failure or data deletion, the backup data can be restored to reduce losses.

[0139] (2) Access control and authority management: Ensure that only authorized users or programs can access and operate key data. By setting different levels of authority, the operating scope of users or programs is limited to prevent illegal access and data tampering.

[0140] (3) Data encryption and protection: Encrypt sensitive data to ensure data security during transmission and storage. At the same time, use security protocols to protect the integrity and confidentiality of data transmission.

[0141] (4) Data verification and validation: Data verification and validation mechanisms are introduced during the data transmission process. These mechanisms can detect errors in the data transmission process 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] At the same time, in the process of formulating error prevention strategies, the filtering method and principal component analysis method in the machine learning algorithm are used to deeply analyze and learn a large amount of historical data, which can accurately extract the behavioral characteristics and patterns of the system during normal operation, apply the extracted patterns to the real-time data monitoring process, identify potential abnormal behaviors, and take corresponding preventive measures or adjust strategies based on the identification results. For example, if there is an abnormal increase in traffic or an unusual access request, based on this identification result, corresponding preventive measures or adjustment strategies can be taken, such as strengthening firewall rules, restricting access to suspicious IP addresses, etc., to prevent potential network attacks or security threats.

[0143] Among them, the process of using filtering method and principal component analysis method to form error prevention strategy is as follows Figure 5 As shown, the following steps are included:

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

[0145] (2) Data preprocessing: De-noising, outlier processing and standardization are performed on the collected data to improve data quality.

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

[0147] (4) Feature extraction (principal component analysis): Principal component analysis is used to map the raw data into 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: Use the classification algorithm to build a model, and use the data after feature selection and feature extraction to train the selected algorithm so that it can learn the normal behavior patterns 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 abnormal behavior is detected, corresponding preventive measures or adjustment strategies will be triggered, such as starting recovery backup and sending abnormal signals to operation and maintenance personnel.

[0151] (8) Continuous learning and optimization: Regularly review and update 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 the distributed power system through a comprehensive anti-misoperation strategy; timely identifies and responds to abnormal behaviors, reduces system failures and data errors, and enhances system stability and reliability; through intelligent strategy adjustment, more effective use of system resources and improved operational efficiency; the adaptive learning system enables the strategy to adapt to the ever-changing environment and needs, maintaining the timeliness and effectiveness of the strategy; automated and intelligent strategy generation and adjustment reduces human errors and interventions, and improves the accuracy and efficiency of operations.

[0153] It can be seen that this embodiment provides an implementation method of a distributed power supply data error prevention strategy analysis method based on machine learning, such as Figure 6 As shown in the figure, firstly, a large amount of complex data generated by distributed power sources is finely classified, including power output data, grid-connected power quality data, etc., to facilitate grid dispatching, load balancing and equipment health monitoring. Subsequently, a method combining multidimensional data monitoring with deep data analysis is used to monitor these data in real time and identify anomalies to ensure the accuracy and reliability of the data. In addition, in view of the possible 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 anti-misoperation strategies are proposed based on this, including data backup and recovery measures. Finally, the filtering method and principal component analysis method in machine learning are used to conduct deep learning of historical data, and the behavioral characteristics and patterns of normal system operation are extracted, which are applied to real-time data monitoring to prevent potential abnormal behaviors.

[0154] In summary, this embodiment can significantly improve the stability and reliability of the distributed power supply system. This embodiment has formulated an effective anti-error strategy for the risk of data misoperation, ensured the integrity and security of the data, and avoided resource waste or power shortage due to data errors. In addition, 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 actively prevents potential network attacks or security threats, further enhancing the security protection capabilities of the entire distributed power supply system. In general, this embodiment provides strong technical support for the efficient, stable and safe operation of distributed power supply systems.

[0155] Compared with the prior art, the distributed power data anti-misoperation strategy analysis method provided in this embodiment not only has the core advantages of the hydropower computer monitoring system control safety strategy technology such as miscontrol prevention, data security and control audit, but also introduces the integrated application of refined classification, multi-dimensional data monitoring and deep data analysis, which can more accurately identify and prevent the risk of misoperation in distributed power data; through machine learning algorithms, multi-dimensional data monitoring of distributed power data can be performed, and potential abnormal behaviors can be effectively prevented in real-time monitoring, which not only fundamentally improves the stability and reliability of the distributed power system, but also provides more advanced and comprehensive technical method support for the safe operation of smart grids.

[0156] It can be seen that the distributed power data anti-misoperation strategy analysis solution proposed in this embodiment improves the accuracy and reliability of distributed power 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.

[0157] In order to facilitate the implementation of the distributed power data anti-misoperation strategy analysis method of the embodiment of the present application, the embodiment of the present application also provides a distributed power data anti-misoperation strategy analysis device. The meaning of the terms is the same as in the above-mentioned distributed power data anti-misoperation strategy analysis method, and the specific implementation details can refer to the description in the method embodiment.

[0158] See also Figure 7 , Figure 7 A schematic diagram of the structure of a distributed power data anti-misoperation strategy analysis device provided in an embodiment of the present application, wherein the distributed power data anti-misoperation strategy analysis device may specifically include a collection module 201, a monitoring module 202, a mining module 203, an analysis module 204 and a generation module 205, which may be specifically as follows:

[0159] The collection module 201 is used to collect distributed power data of different power types and classify the distributed power data;

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

[0161] The mining module 203 is used to perform mining and analysis on the multi-dimensional power supply monitoring data, identify abnormal data points, and perform data preprocessing on the abnormal data points in the multi-dimensional power supply monitoring data to obtain preprocessed multi-dimensional power supply monitoring data;

[0162] The analysis module 204 is used to perform error operation analysis on the pre-processed multi-dimensional power monitoring data to obtain corresponding error operation analysis results, where the data analysis results include the error operation type corresponding to the power monitoring data and its error operation impact;

[0163] The generating module 205 is used to generate a corresponding anti-misoperation strategy based on the misoperation analysis result.

[0164] To sum up, the distributed power data anti-misoperation strategy analysis device provided in this embodiment improves the accuracy and reliability of distributed power 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.

[0165] In addition, the present application also provides an electronic device, such as Figure 8 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application. Specifically, the electronic device may include a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, an input unit 304 and other components. Those skilled in the art can understand that Figure 8 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0166] The processor 301 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. 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 processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned 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 a distributed power supply data misoperation prevention strategy analysis method 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, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an 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 a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. 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 for supplying power to each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so that the power management system can manage charging, discharging, power consumption and other functions. The power supply 303 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

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

[0170] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, thereby realizing various functions, as follows:

[0171] Collect distributed power data of different power types and classify the distributed power data; perform real-time multi-dimensional monitoring of the classified distributed power data to obtain corresponding multi-dimensional power monitoring data; perform mining and analysis on the multi-dimensional power monitoring data to identify abnormal data points, and perform data preprocessing on the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data; perform error operation analysis on the preprocessed multi-dimensional power monitoring data to obtain corresponding error operation analysis results, and the data analysis results include the error operation type corresponding to the power monitoring data and its error operation impact; based on the error operation analysis results, generate corresponding anti-error operation strategies.

[0172] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0173] The embodiments of the present application improve the accuracy and reliability of data processing in distributed power systems 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.

[0174] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0175] To this end, an embodiment of the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any of the distributed power supply data misoperation prevention strategy analysis methods provided in the embodiments of the present application. For example, the instructions can execute the following steps:

[0176] Collect distributed power data of different power types and classify the distributed power data; perform real-time multi-dimensional monitoring of the classified distributed power data to obtain corresponding multi-dimensional power monitoring data; perform mining and analysis on the multi-dimensional power monitoring data to identify abnormal data points, and perform data preprocessing on the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data; perform error operation analysis on the preprocessed multi-dimensional power monitoring data to obtain corresponding error operation analysis results, and the data analysis results include the error operation type corresponding to the power monitoring data and its error operation impact; based on the error operation analysis results, generate corresponding anti-error operation strategies.

[0177] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0178] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an 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 the present application, the beneficial effects that can be achieved by any of the distributed power data anti-misoperation strategy analysis methods provided in the embodiments of the present application can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0179] The above is a detailed introduction to a distributed power supply data anti-misoperation strategy analysis method, device, electronic device and storage medium provided in an embodiment of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A distributed power supply data misoperation prevention strategy analysis method, characterized in that: The steps include: Collecting distributed power data of different power types and classifying the distributed power data; Performing real-time multi-dimensional monitoring on the classified distributed power supply data to obtain corresponding multi-dimensional power supply monitoring data; Performing mining and analysis on the multi-dimensional power supply monitoring data, identifying abnormal data points, and performing data preprocessing on the abnormal data points in the multi-dimensional power supply monitoring data to obtain preprocessed multi-dimensional power supply monitoring data; Performing error operation analysis on the pre-processed multi-dimensional power monitoring data to obtain corresponding error operation analysis results, wherein the data analysis results include the error operation type corresponding to the power monitoring data and its error operation impact; Based on the error operation analysis result, a corresponding error operation prevention strategy is generated.

2. The distributed power supply data anti-misoperation strategy analysis method according to claim 1 is characterized in that: The collecting of distributed power 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; Collecting real-time power generation through the sensor and the data acquisition device; Monitoring voltage, frequency and harmonics by means of the power quality monitoring device; Record fault records, operation records and maintenance activity records of distributed power points through pre-deployed systems; The status of the circuit breaker, transformer and switch are monitored in real time by the sensor.

3. The distributed power supply data misoperation prevention strategy analysis method according to claim 2 is characterized in that: The classifying the distributed power supply data includes: Acquire power type information of the distributed power data, classify the distributed power data based on the power type information, and obtain a corresponding power type classification result; The data type information and the usage type information of the distributed power supply data are obtained, and the distributed power supply data is classified based on the data type information and the usage type information to obtain a corresponding data type classification result.

4. The distributed power supply data misoperation prevention strategy analysis method according to claim 1 is characterized in that: The real-time multi-dimensional monitoring of the classified distributed power supply data to obtain corresponding multi-dimensional power supply monitoring data includes: Real-time monitoring of power generation output data of distributed power systems of different power types, the power generation output data 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 record data of distributed power systems of different power types, the event record data including fault records, operation records and maintenance activity records; Real-time monitoring of state quantity data of distributed power supply systems of different power supply types, wherein the state quantity data includes circuit breaker state data, transformer state data and switch state data.

5. The distributed power supply data misoperation prevention strategy analysis method according to claim 1 is characterized in that: The mining and analyzing of the multi-dimensional power supply monitoring data to identify abnormal data points and preprocessing the abnormal data points in the multi-dimensional power supply monitoring data to obtain preprocessed multi-dimensional power supply monitoring data includes: Extracting features from the multi-dimensional power supply monitoring data using a machine learning algorithm to obtain corresponding operating features; Based on the operation characteristics, a data anomaly detection model is constructed; Based on the data anomaly detection model, the multi-dimensional power supply monitoring data monitored in real time is mined and analyzed to identify corresponding abnormal data points and their abnormal behaviors; Based on the abnormal data points, data cleaning and abnormal value denoising are performed on the multi-dimensional power supply monitoring data to obtain pre-processed multi-dimensional power supply monitoring data.

6. The distributed power supply data misoperation prevention strategy analysis method according to claim 1 is characterized in that: The performing error operation analysis on the pre-processed multi-dimensional power supply monitoring data to obtain corresponding error operation analysis results includes: Analyze the storage failure risk based on the pre-processed multi-dimensional monitoring data to obtain a storage failure analysis result; Analyzing the transmission distortion risk based on the pre-processed multi-dimensional monitoring data to obtain a transmission distortion analysis result; Analyze the illegal access risk based on the pre-processed multi-dimensional monitoring data to obtain illegal access analysis results; Analyze the data tampering risk based on the pre-processed multi-dimensional monitoring data to obtain the data tampering analysis result The risk of accidental data deletion is analyzed based on the pre-processed multi-dimensional monitoring data to obtain an analysis result of accidental data deletion.

7. The distributed power supply data misoperation prevention strategy analysis method according to claim 1 is characterized in that: The generating a corresponding anti-misoperation strategy based on the misoperation analysis result includes: Based on the types of erroneous operations and their impacts in the erroneous operation analysis results, an anti-erroneous operation strategy corresponding to each type of erroneous operation is generated, wherein the anti-erroneous operation strategy includes a data backup and recovery strategy, an access control and authority management strategy, a data encryption and protection strategy, and a data check and verification strategy; Use the filtering method and principal component analysis method in the machine learning algorithm to conduct deep learning on historical distributed power data to extract the behavioral characteristics and behavior patterns during normal operation; Applying the behavior pattern to identify potential abnormal behaviors during real-time data monitoring to obtain abnormal behavior identification results; The anti-misoperation strategy is adjusted according to the abnormal behavior identification result.

8. A distributed power supply data misoperation prevention strategy analysis device, characterized in that: include: A collection module, used to collect distributed power data of different power types and classify the distributed power data; A monitoring module, used to perform real-time multi-dimensional monitoring of the classified distributed power supply data to obtain corresponding multi-dimensional power supply monitoring data; A mining module is used to perform mining and analysis on the multi-dimensional power monitoring data, identify abnormal data points, and perform data preprocessing on the abnormal data points in the multi-dimensional power monitoring data to obtain preprocessed multi-dimensional power monitoring data; An analysis module, used to perform an error operation analysis on the pre-processed multi-dimensional power monitoring data to obtain a corresponding error operation analysis result, wherein the data analysis result includes an error operation type corresponding to the power monitoring data and its error operation impact; A generation module is used to generate a corresponding anti-misoperation strategy based on the misoperation analysis result.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the distributed power supply data misoperation prevention strategy analysis method according to 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 distributed power supply data misoperation prevention strategy analysis method according to any one of claims 1 to 7 are implemented.

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