Power grid user electrical load sensing system and method based on multi-source data fusion

Abstract

The invention relates to the technical field of power equipment operation and maintenance, provides a power grid user electrical load sensing system and method based on multi-source data fusion, and aims to improve data safety and load analysis accuracy. The safety of user data in the transmission, storage and analysis process is ensured, the multi-source data fusion technology integrates historical load, weather, social economy and user behavior data, comprehensive load perception is achieved, a dual-load analysis mechanism combines original data and hidden danger processing data, the analysis accuracy is improved, and the user experience is improved. And a reliable basis is provided for dispatching, optimization and fault prediction of a power system.

Description

Technical Field

[0001] This invention relates to the field of power equipment operation and maintenance technology, and in particular to a power grid user load sensing system and method based on multi-source data fusion. Background Technology

[0002] Multi-source data in the power grid primarily originates from various stages of power production and consumption, including generation, transmission, transformation, distribution, consumption, and dispatch. This multi-source data can be categorized into three types: Power grid operation and equipment monitoring data (such as power equipment condition monitoring data, grid load data, and power quality data); Power company marketing data (such as data on electricity trading prices, electricity sales volume, and customer data); and Power company management data (data related to enterprise operations and management decision-making).

[0003] The power grid user load sensing system is an integrated system combining multiple data sources and advanced data analysis technologies. Its aim is to more accurately sense and predict the power load of power grid users, providing strong support for the operation and management of the power system. This multi-source data fusion system collects information from various data sources, such as historical load data, meteorological data, socioeconomic data, and user behavior data. Utilizing advanced data fusion and analysis techniques, it comprehensively and accurately senses and predicts user load. The system can reflect the real-time electricity consumption of power grid users, providing crucial information for power system scheduling, optimization, and fault prediction.

[0004] The power grid user load sensing system based on multi-source data fusion has the following technical pain points in practical use: It involves a large amount of user electricity consumption data and enterprise operation data. When sensing power grid user load, if the data used for sensing is leaked and tampered with, or if the source data contains errors, errors will occur in the sensing, making it impossible to accurately determine whether the user load is in a safe state. To improve the security and data processing accuracy of the power grid user load sensing system based on multi-source data fusion, this invention provides a power grid user load sensing system and method based on multi-source data fusion. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a power grid user load sensing system and method based on multi-source data fusion. This solves the problem that when the data used for power grid user load sensing is leaked and tampered with, or when there are errors in the data source, errors will occur in the power grid user load sensing, making it impossible to accurately determine whether the user load is in a safe state.

[0006] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention provides a power grid user load sensing system based on multi-source data fusion, comprising:

[0008] The data acquisition module is used to acquire power grid user data, which includes historical load data, meteorological data, socio-economic data, and user behavior data. The power grid user data is preprocessed and data features are extracted to obtain a power grid user data feature set.

[0009] The data encryption module is used to classify the power grid user data to obtain sensitive data and non-sensitive data, encrypt the sensitive data using the AES encryption algorithm, encrypt the non-sensitive data using the RSA encryption algorithm, establish an encrypted data key storage store, configure user permissions for the encrypted data key storage store, and store the keys corresponding to the sensitive data encrypted by the AES encryption algorithm and the non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key storage store.

[0010] The security inspection module is used to set the security inspection time period for power grid user data. According to the set security inspection time period, it performs security inspections on each interface that acquires power grid user data and the data acquisition terminal. If security threats and vulnerabilities are found in the inspection results, data security warning information is generated.

[0011] The data fusion module is used to extract data interface or data collection terminal information corresponding to security risks from data security early warning information, determine the scope of security risk data based on the data interface and data collection terminal information, filter out data with security risks based on the scope of security risk data, perform data analysis on the data with security risks, establish security risk simulation data, replace the original data with the security risk simulation data according to the data generation time and category to obtain the first data group, and use the original data as the second data group;

[0012] The load analysis module receives load analysis tasks, determines the data access range based on the task, matches personnel permissions with the data access range to obtain personnel permission information, generates an access request command based on the personnel permission information, provides feedback information based on the obtained access request command, retrieves the key from the encrypted data key storage store, decrypts the first and second data groups using the key, substitutes the decrypted first and second data groups into the preset load analysis to obtain the load analysis results for the first and second data groups, compares the load analysis results of the first and second data groups with the preset load safety value, and determines whether the electrical load is in a safe state, a suspected unsafe state, or an unsafe state based on the comparison results.

[0013] Furthermore, in the power grid user load sensing system based on multi-source data fusion provided by the present invention, the data acquisition module is also used for:

[0014] The set of power grid user data features includes basic load data features, time series features, external influencing factor features, user behavior features, and load features;

[0015] Load characteristics include load trend characteristics, load anomaly detection characteristics, and load pattern classification characteristics.

[0016] Furthermore, in the power grid user load sensing system based on multi-source data fusion provided by the present invention, the data encryption module is also used for:

[0017] Based on the sensitivity of the data, power grid user data is divided into sensitive data and non-sensitive data. Sensitive data includes personal identity information, payment information, etc., while non-sensitive data includes electricity consumption habits and non-precise geographical locations.

[0018] Sensitive data is encrypted using the AES encryption algorithm, and non-sensitive data is encrypted using the RSA encryption algorithm, generating a public key and a private key pair. The public key is used to encrypt the data, and the private key is used to decrypt the data.

[0019] Establish an encrypted data key repository for the keys generated by the AES and RSA encryption algorithms to store the keys for all encrypted data;

[0020] Configure user permissions for the encrypted data key repository, and store the keys corresponding to sensitive data encrypted with the AES encryption algorithm and non-sensitive data encrypted with the RSA encryption algorithm in the encrypted data key repository.

[0021] Furthermore, in the multi-source data fusion-based power grid user load sensing system provided by the present invention, the data fusion module is further used for:

[0022] Receive early warning information from the data security early warning system, parse the early warning information, and extract information related to the data interface or data collection terminal of the security risks;

[0023] Based on the extracted data interface and data acquisition terminal information, analyze the data range involved in the data interface and acquisition terminal, and determine the specific range of the safety hazard data after determining the data range.

[0024] Based on the scope of the safety hazard data, filter out the data that poses safety hazards from the raw data;

[0025] In-depth data analysis was conducted on the screened data that posed safety hazards, and safety hazard simulation data was established based on the analysis results;

[0026] The safety hazard simulation data is replaced in the original data according to the data generation time and category to obtain the first data group. The original data that has not been replaced is retained as the second data group.

[0027] Furthermore, in the power grid user load sensing system based on multi-source data fusion provided by the present invention, the load analysis module is also used for:

[0028] Receive load analysis tasks, determine the range of data to be accessed based on the load analysis tasks, match the corresponding personnel permissions through the data access range, generate permission request commands based on the personnel permission information, and retrieve the corresponding keys from the encrypted data key store based on the feedback information of the permission request commands.

[0029] The first and second data groups are decrypted using the retrieved key. The decrypted first and second data groups are then prepared to be substituted into the preset load analysis for processing.

[0030] Substitute the decrypted data into the preset load analysis model to obtain the load analysis results of the first data set and the second data set;

[0031] The load analysis results of the first and second data sets are compared with the preset load safety values ​​to assess the safety status of the electrical load.

[0032] If the load analysis results of the first data group and the second data group are both within the preset load safety value range, the electrical load is in a safe state. If the load analysis results of the first data group are within the preset load safety value range, but the load analysis results of the second data group are not within the preset load safety value range, the electrical load is in a suspected unsafe state. If the load analysis results of the first data group are not within the preset load safety value range, and the load analysis results of the second data group are not within the preset load safety value range, the electrical load is in an unsafe state.

[0033] Secondly, the present invention provides a method for sensing the electricity load of power grid users based on multi-source data fusion, comprising:

[0034] Step S101: Obtain power grid user data, which includes historical load data, meteorological data, socio-economic data, and user behavior data. Perform preprocessing and data feature extraction on the power grid user data to obtain a power grid user data feature set.

[0035] Step S102: Classify the power grid user data to obtain sensitive data and non-sensitive data. Use AES encryption algorithm to encrypt the sensitive data and RSA encryption algorithm to encrypt the non-sensitive data. Establish an encrypted data key storage store and configure user permissions for the encrypted data key storage store. Store the keys corresponding to the sensitive data encrypted with AES encryption algorithm and the non-sensitive data encrypted with RSA encryption algorithm in the encrypted data key storage store.

[0036] Step S103: Set the power grid user data security inspection time period. According to the set power grid user data security inspection time period, perform security inspections on each interface for obtaining power grid user data and the data acquisition terminal. If there are security threats and security vulnerabilities in the detection results, generate data security warning information.

[0037] Step S104: Extract the data interface or data collection terminal information corresponding to the security risks from the data security early warning information. Determine the scope of security risk data based on the data interface and data collection terminal information. Filter out the data with security risks based on the scope of security risk data. Perform data analysis on the data with security risks to establish security risk simulation data. Replace the original data with the security risk simulation data according to the data generation time and category to obtain the first data group. Use the original data as the second data group.

[0038] Step S105: Receive the load analysis task; determine the data access range based on the load analysis task; match personnel permissions through the data access range to obtain personnel permission information; generate a permission request command based on the personnel permission information; receive feedback information based on the obtained permission request command; retrieve the key from the encrypted data key storage store; decrypt the first data group and the second data group using the key; substitute the decrypted first data group and the second data group into the preset load analysis to obtain the load analysis results of the first data group and the second data group; compare the load analysis results of the first data group and the second data group with the preset load safety value; and determine whether the electrical load is in a safe state, a suspected unsafe state, or an unsafe state based on the comparison results.

[0039] Furthermore, in the multi-source data fusion-based power grid user load sensing method provided by the present invention, step S101 includes:

[0040] The set of power grid user data features includes basic load data features, time series features, external influencing factor features, user behavior features, and load features;

[0041] Load characteristics include load trend characteristics, load anomaly detection characteristics, and load pattern classification characteristics.

[0042] Furthermore, in the multi-source data fusion-based power grid user load sensing method provided by the present invention, step S103 includes:

[0043] Based on the sensitivity of the data, power grid user data is divided into sensitive data and non-sensitive data. Sensitive data includes personal identity information, payment information, etc., while non-sensitive data includes electricity consumption habits and non-precise geographical locations.

[0044] Sensitive data is encrypted using the AES encryption algorithm, and non-sensitive data is encrypted using the RSA encryption algorithm, generating a public key and a private key pair. The public key is used to encrypt the data, and the private key is used to decrypt the data.

[0045] Establish an encrypted data key repository for the keys generated by the AES and RSA encryption algorithms to store the keys for all encrypted data;

[0046] Configure user permissions for the encrypted data key repository, and store the keys corresponding to sensitive data encrypted with the AES encryption algorithm and non-sensitive data encrypted with the RSA encryption algorithm in the encrypted data key repository.

[0047] Furthermore, in the multi-source data fusion-based power grid user load sensing method provided by the present invention, step S104 includes:

[0048] Receive early warning information from the data security early warning system, parse the early warning information, and extract information related to the data interface or data collection terminal of the security risks;

[0049] Based on the extracted data interface and data acquisition terminal information, analyze the data range involved in the data interface and acquisition terminal, and determine the specific range of the safety hazard data after determining the data range.

[0050] Based on the scope of the safety hazard data, filter out the data that poses safety hazards from the raw data;

[0051] In-depth data analysis was conducted on the screened data that posed safety hazards, and safety hazard simulation data was established based on the analysis results;

[0052] The safety hazard simulation data is replaced in the original data according to the data generation time and category to obtain the first data group. The original data that has not been replaced is retained as the second data group.

[0053] Furthermore, in the multi-source data fusion-based power grid user load sensing method provided by the present invention, step S105 includes:

[0054] Receive load analysis tasks, determine the range of data to be accessed based on the load analysis tasks, match the corresponding personnel permissions through the data access range, generate permission request commands based on the personnel permission information, and retrieve the corresponding keys from the encrypted data key store based on the feedback information of the permission request commands.

[0055] The first and second data groups are decrypted using the retrieved key. The decrypted first and second data groups are then prepared to be substituted into the preset load analysis for processing.

[0056] Substitute the decrypted data into the preset load analysis model to obtain the load analysis results of the first data set and the second data set;

[0057] The load analysis results of the first and second data sets are compared with the preset load safety values ​​to assess the safety status of the electrical load.

[0058] If the load analysis results of the first data group and the second data group are both within the preset load safety value range, the electrical load is in a safe state. If the load analysis results of the first data group are within the preset load safety value range, but the load analysis results of the second data group are not within the preset load safety value range, the electrical load is in a suspected unsafe state. If the load analysis results of the first data group are not within the preset load safety value range, and the load analysis results of the second data group are not within the preset load safety value range, the electrical load is in an unsafe state.

[0059] Beneficial effects of the present invention:

[0060] This invention categorizes and encrypts power grid user data. Sensitive data uses the AES encryption algorithm, while non-sensitive data uses the RSA encryption algorithm, effectively preventing the risk of data leakage and tampering during transmission and storage. An encrypted data key repository is established, and user permissions are configured to ensure that only authorized personnel can access the keys, further enhancing data security.

[0061] This invention establishes a time cycle for power grid user data security inspections, periodically conducting security inspections on various interfaces and data acquisition terminals to promptly identify and address potential security threats and vulnerabilities, ensuring stable system operation. It extracts the data range corresponding to security risks from data security early warning information, performs data analysis and simulation replacement, ensuring that even if the original data has problems, the system can still perform accurate load analysis based on the processed data.

[0062] This invention utilizes multi-source data fusion technology to collect data from various sources, including historical load data, meteorological data, socioeconomic data, and user behavior data, to comprehensively perceive and predict the electricity load of power grid users. During the load analysis phase, load analysis is simultaneously performed on both the encrypted and decrypted first data set and the unmodified second data set. By comparing the results of these two analyses, the accuracy and reliability of the load analysis are improved.

[0063] The system and method of this invention can reflect the electricity consumption of grid users in real time, providing important basis for power system scheduling, optimization, and fault prediction, and helping power system operators make more scientific and rational decisions. By outputting detailed load analysis results and reports, it helps grid operators understand the safety status of user electricity loads and promptly identify and respond to potential safety risks.

[0064] In summary, this invention effectively solves the data security problem in the perception of power load by grid users through key technical measures such as data encryption, encrypted data key management, data security inspection, data fusion and hidden danger handling, and dual load analysis and comparison. It improves the accuracy of load analysis and the stability and reliability of the system, and provides strong support for the optimized operation and scientific management of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0066] Figure 1 A schematic diagram of a power grid user load sensing method based on multi-source data fusion provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0068] To better understand the purpose of this invention, the invention will now be described in further detail.

[0069] In a first aspect, the present invention provides a power grid user load sensing system based on multi-source data fusion, comprising:

[0070] The data acquisition module is used to acquire power grid user data, which includes historical load data, meteorological data, socio-economic data, and user behavior data. The power grid user data is preprocessed and data features are extracted to obtain a power grid user data feature set.

[0071] The data acquisition module in this invention obtains relevant data from power grid users from multiple sources, preprocesses this data, and extracts data features to ultimately form a power grid user data feature set. The specific functions and processes of this module are as follows:

[0072] Historical load data: records the past electricity consumption of grid users, reflecting users' electricity consumption habits and load change trends.

[0073] Meteorological data includes meteorological factors such as temperature, humidity, and wind speed. These factors have a significant impact on users' electricity consumption, such as the increased electricity consumption of air conditioners during the high temperatures of summer.

[0074] Socioeconomic data, such as regional economic development level, industrial structure, and demographic data, helps to understand the socioeconomic background of user load.

[0075] User behavior data: Real-time electricity consumption information collected through devices such as smart meters and smart home systems, such as device on / off times and power consumption.

[0076] Data acquisition methods: Historical load data and socioeconomic data are obtained through the power grid company's data center. Meteorological data is obtained in cooperation with meteorological departments or by using publicly available meteorological data sources. User behavior data is obtained through smart home systems, user apps, and other channels.

[0077] Data cleaning: Remove invalid or erroneous data such as duplicates, missing values, and outliers to ensure data quality.

[0078] Data format standardization: Convert data from different sources into a unified format to facilitate subsequent processing and analysis.

[0079] Data standardization: Scaling or normalizing continuous data to eliminate the influence of units and make data from different sources comparable.

[0080] Basic load data characteristics: Extract basic load characteristics such as average load, peak load, and valley load of users.

[0081] Time series characteristics: Analyze the trends and periodic characteristics of user load changes over time.

[0082] External influencing factors characteristics: Extract external factors that affect user load from meteorological and socioeconomic data, such as temperature and holidays.

[0083] User behavior characteristics: By analyzing user behavior data, we can extract user behavior characteristics such as electricity consumption habits and device usage patterns.

[0084] Load trend characteristics: Describes the long-term trend of user load changes over time.

[0085] Load anomaly detection features: Identify abnormal points or abnormal patterns in user load.

[0086] Load pattern classification characteristics: User load is classified according to certain rules, such as weekday mode, weekend mode, etc.

[0087] After preprocessing and data feature extraction, the data acquisition module will output a set of power grid user data features. This set contains rich user load features, providing a solid foundation for subsequent data encryption, security inspection, data fusion and load analysis.

[0088] Through these steps, the data acquisition module ensures the comprehensiveness and accuracy of the input data, providing a reliable data source for subsequent multi-source data fusion and load sensing.

[0089] The data encryption module is used to classify the power grid user data to obtain sensitive data and non-sensitive data, encrypt the sensitive data using the AES encryption algorithm, encrypt the non-sensitive data using the RSA encryption algorithm, establish an encrypted data key storage store, configure user permissions for the encrypted data key storage store, and store the keys corresponding to the sensitive data encrypted by the AES encryption algorithm and the non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key storage store.

[0090] The data encryption module enhances the security and privacy protection of power grid user data in a power grid user load perception system based on multi-source data fusion.

[0091] The data encryption module first categorizes the acquired power grid user data to distinguish between sensitive and non-sensitive data. Sensitive data typically includes users' personal identification information, payment information, etc., the leakage of which could pose serious privacy and security risks to users. Non-sensitive data includes users' electricity usage habits, imprecise geographical locations, etc., the risk of such information being leaked is relatively low.

[0092] AES Encryption Algorithm: For sensitive data, the AES (Advanced Encryption Standard) encryption algorithm is used for encryption. AES is a widely used symmetric encryption algorithm that is efficient and secure.

[0093] RSA Encryption Algorithm: For non-sensitive data, the RSA encryption algorithm is used. RSA is an asymmetric encryption algorithm that uses a pair of keys (public and private keys) for encryption and decryption. The public key is used to encrypt the data, and the private key is used to decrypt it. Because the RSA encryption algorithm has a relatively long key length (typically 1024 bits or more), it provides a higher level of security.

[0094] An encrypted data key repository was established to securely store the keys generated during the encryption process. This repository not only stores the keys for all encrypted data but also strictly controls access permissions. Only authorized personnel can access and use these keys, thus ensuring key security.

[0095] The data encryption module also configures permissions for the encrypted data key repository. This includes setting access permissions for different users or user groups, ensuring that only personnel with the appropriate permissions can access and manipulate the keys in the repository.

[0096] Finally, the encrypted data key repository will store sensitive data keys encrypted with the AES encryption algorithm and non-sensitive data keys encrypted with the RSA encryption algorithm. The storage and management of these keys are crucial to ensuring the smooth operation of the data encryption and decryption process.

[0097] In summary, the data encryption module enhances the confidentiality and integrity of power grid user data by classifying, encrypting, and managing keys.

[0098] The security inspection module is used to set the security inspection time period for power grid user data. According to the set security inspection time period, it performs security inspections on each interface that acquires power grid user data and the data acquisition terminal. If security threats and vulnerabilities are found in the inspection results, data security warning information is generated.

[0099] The safety inspection module is responsible for monitoring and maintaining the security of the data acquisition process in the power grid user load perception system based on multi-source data fusion.

[0100] The safety inspection module first sets the safety inspection time cycle for power grid user data according to actual needs. This cycle can be daily, weekly, or monthly, depending on the sensitivity of the data and the system's security requirements.

[0101] Within the set inspection cycle, the safety inspection module will conduct a comprehensive inspection of all interfaces that acquire power grid user data and the data acquisition terminal.

[0102] Interface security: Verify whether the interface is subject to unauthorized access or attack, whether the data transmitted by the interface is encrypted, and whether there are other potential security risks.

[0103] Data acquisition terminal security: Check the physical and network security of the data acquisition terminal to ensure that the data is not tampered with or leaked during the acquisition process.

[0104] System log review: Analyze system logs to look for signs that may indicate a security threat, such as abnormal login attempts and changes in data access patterns.

[0105] Detecting security threats and vulnerabilities

[0106] During the inspection process, if the security inspection module detects any security threats or vulnerabilities, such as unauthorized access, data tampering, or virus infection, it will immediately record them.

[0107] When a security threat or vulnerability is detected, the security inspection module immediately generates a data security alert. These alerts typically include the type, location, and severity of the threat, along with recommended countermeasures. This information is promptly communicated to system administrators or relevant security teams so they can take appropriate action to eliminate the threat or patch the vulnerability.

[0108] Upon receiving an alert, system administrators or security teams will take necessary measures based on the specific circumstances, such as shutting down affected interfaces, isolating infected devices, and updating security patches. They will also record the entire incident handling process and its outcome to provide a reference for future security inspections and risk management.

[0109] Through regular security inspections, the security inspection module ensures the security of power grid user data during the acquisition process, preventing data leakage, tampering, or other security risks. This provides important security for the power grid user load sensing system based on multi-source data fusion.

[0110] The data fusion module is used to extract data interface or data collection terminal information corresponding to security risks from data security early warning information, determine the scope of security risk data based on the data interface and data collection terminal information, filter out data with security risks based on the scope of security risk data, perform data analysis on the data with security risks, establish security risk simulation data, replace the original data with the security risk simulation data according to the data generation time and category to obtain the first data group, and use the original data as the second data group;

[0111] The data fusion module in the power grid user load sensing system based on multi-source data fusion is responsible for processing data security early warning information, identifying and processing potential security hazard data, thereby improving the accuracy and reliability of load analysis. The specific functions and processes of this module are as follows:

[0112] After the security inspection module generates a data security warning, the data fusion module will first receive and parse the warning information, which contains specific information about the data interface or data collection terminal that discovered the security risk.

[0113] Based on the data interface and collection terminal information provided in the early warning information, the data fusion module further analyzes the data scope involved by these interfaces and collection terminals. By tracing the flow and correlation of data, the module can determine which data may be affected by security risks, thereby defining the specific scope of the data with security risks.

[0114] After determining the scope of the data with security risks, the data fusion module will filter out the data with security risks from the raw data. This data may have been tampered with, damaged, or contain inaccurate information, and therefore cannot be used directly for load analysis.

[0115] For data identified as having potential security risks, the data fusion module conducts in-depth data analysis. By analyzing the characteristics, patterns, and potential anomalies of this data, the module can better understand the nature and impact of the security risks. Based on these analysis results, the module creates simulated security risk data. This simulated data aims to reflect the potential security problems in the original data while maintaining data integrity and consistency for subsequent analysis.

[0116] After establishing the simulated safety hazards, the data fusion module replaces the original data according to its generation time and category. The replaced dataset, referred to as the first data set, contains the processed simulated safety hazards. Simultaneously, the module retains a copy of the original, unreplaced data as the second data set for subsequent comparative analysis and verification.

[0117] After the above processing, the data fusion module will output two datasets: a first data set (containing simulated safety hazards) and a second data set (raw data). These two datasets will be used in the subsequent load analysis phase to assess the electricity load status of grid users.

[0118] Through the data fusion module, the system can respond quickly when safety hazards are detected, reducing the impact on load analysis results by replacing the data related to the safety hazards. Simultaneously, retaining the original data as a benchmark helps verify the processing effectiveness and improves the accuracy of the analysis. This dual data set design increases the system's flexibility and reliability, providing strong support for accurate perception of power grid users' electricity load.

[0119] The load analysis module receives load analysis tasks, determines the data access range based on the task, matches personnel permissions with the data access range to obtain personnel permission information, generates an access request command based on the personnel permission information, provides feedback information based on the obtained access request command, retrieves the key from the encrypted data key storage store, decrypts the first and second data groups using the key, substitutes the decrypted first and second data groups into the preset load analysis to obtain the load analysis results for the first and second data groups, compares the load analysis results of the first and second data groups with the preset load safety value, and determines whether the electrical load is in a safe state, a suspected unsafe state, or an unsafe state based on the comparison results.

[0120] The load analysis module is responsible for conducting in-depth load analysis on the processed data in the power grid user load perception system based on multi-source data fusion, in order to assess the power load status of power grid users.

[0121] The load analysis module first receives load analysis tasks from the system or other modules. These tasks include analyzing the electricity load of a user within a specific time period or monitoring the electricity consumption patterns of a specific user.

[0122] Based on the requirements of the load analysis task, the load analysis module will determine the range of data to be called, including specific time ranges, data types (such as historical load data, meteorological data, etc.) or specific user groups.

[0123] To ensure data security and compliance, the load analysis module matches the corresponding personnel permissions based on the scope of the data being accessed. The system needs to verify whether the user or process executing the load analysis task has the permission to access this data.

[0124] If the current user or process does not have sufficient permissions to access the required data, the load analysis module will generate a permission request command. This permission request command will send a request to the system's permission management system to request permission to access the required data.

[0125] Once permissions are granted, the load analysis module will retrieve the encryption key corresponding to the required data from the encrypted data key repository. The key will be used to decrypt the stored encrypted data.

[0126] Using the retrieved key, the load analysis module will decrypt the first data set (containing simulated security risks) and the second data set (raw data). The decrypted data will be restored to its original format for subsequent analysis.

[0127] The decrypted first and second data sets will be fed into a pre-defined load analysis model for analysis. These models may be based on machine learning, statistical analysis, or other data analysis techniques to assess the electricity load status of grid users.

[0128] The load analysis model will output two sets of load analysis results. These results will be compared with preset load safety values. By comparing the results, the system can determine whether the electrical load is in a safe state, a suspected unsafe state, or an unsafe state.

[0129] Safety status: If the load analysis results of both sets of data are within the preset load safety value range, then the electrical load is judged to be in a safe state.

[0130] Suspected unsafe condition: If the load analysis results of one set of data are within the preset range, while the other set are not, the electrical load is judged to be in a suspected unsafe condition. This may require further investigation or preventive measures.

[0131] Unsafe condition: If the load analysis results of both sets of data are outside the preset range, the electrical load is judged to be in an unsafe condition. Immediate measures are needed to reduce the risk and prevent potential accidents.

[0132] Output Results and Reports

[0133] Finally, the load analysis module will output results and corresponding reports, including specific load analysis data, charts, and suggested action plans, for reference and use by system administrators, grid operators, or other relevant parties.

[0134] Through the processing and evaluation of the load analysis module, the system can accurately determine the electricity load status of grid users, providing important basis for power system dispatching, optimization, and fault prediction. This not only helps improve the operating efficiency and security of the power system, but also provides users with more reliable and high-quality electricity services.

[0135] Specifically, in the power grid user load sensing system based on multi-source data fusion provided by the present invention, the data acquisition module is further used for:

[0136] The set of power grid user data features includes basic load data features, time series features, external influencing factor features, user behavior features, and load features;

[0137] Load characteristics include load trend characteristics, load anomaly detection characteristics, and load pattern classification characteristics.

[0138] Specifically, in the power grid user load sensing system based on multi-source data fusion provided by the present invention, the data encryption module is further used for:

[0139] Based on the sensitivity of the data, power grid user data is divided into sensitive data and non-sensitive data. Sensitive data includes personal identity information, payment information, etc., while non-sensitive data includes electricity consumption habits and non-precise geographical locations.

[0140] Sensitive data is encrypted using the AES encryption algorithm, and non-sensitive data is encrypted using the RSA encryption algorithm, generating a public key and a private key pair. The public key is used to encrypt the data, and the private key is used to decrypt the data.

[0141] Establish an encrypted data key repository for the keys generated by the AES and RSA encryption algorithms to store the keys for all encrypted data;

[0142] Configure user permissions for the encrypted data key repository, and store the keys corresponding to sensitive data encrypted with the AES encryption algorithm and non-sensitive data encrypted with the RSA encryption algorithm in the encrypted data key repository.

[0143] Specifically, in the power grid user load sensing system based on multi-source data fusion provided by the present invention, the data fusion module is further used for:

[0144] Receive early warning information from the data security early warning system, parse the early warning information, and extract information related to the data interface or data collection terminal of the security risks;

[0145] Based on the extracted data interface and data acquisition terminal information, analyze the data range involved in the data interface and acquisition terminal, and determine the specific range of the safety hazard data after determining the data range.

[0146] Based on the scope of the safety hazard data, filter out the data that poses safety hazards from the raw data;

[0147] In-depth data analysis was conducted on the screened data that posed safety hazards, and safety hazard simulation data was established based on the analysis results;

[0148] The safety hazard simulation data is replaced in the original data according to the data generation time and category to obtain the first data group. The original data that has not been replaced is retained as the second data group.

[0149] Specifically, in the power grid user load sensing system based on multi-source data fusion provided by the present invention, the load analysis module is further used for:

[0150] Receive load analysis tasks, determine the range of data to be accessed based on the load analysis tasks, match the corresponding personnel permissions through the data access range, generate permission request commands based on the personnel permission information, and retrieve the corresponding keys from the encrypted data key store based on the feedback information of the permission request commands.

[0151] The first and second data groups are decrypted using the retrieved key. The decrypted first and second data groups are then prepared to be substituted into the preset load analysis for processing.

[0152] Substitute the decrypted data into the preset load analysis model to obtain the load analysis results of the first data set and the second data set;

[0153] The load analysis results of the first and second data sets are compared with the preset load safety values ​​to assess the safety status of the electrical load.

[0154] If the load analysis results of the first data group and the second data group are both within the preset load safety value range, the electrical load is in a safe state. If the load analysis results of the first data group are within the preset load safety value range, but the load analysis results of the second data group are not within the preset load safety value range, the electrical load is in a suspected unsafe state. If the load analysis results of the first data group are not within the preset load safety value range, and the load analysis results of the second data group are not within the preset load safety value range, the electrical load is in an unsafe state.

[0155] Secondly, the present invention provides a method for sensing the electricity load of power grid users based on multi-source data fusion, comprising:

[0156] Step S101: Obtain power grid user data, which includes historical load data, meteorological data, socio-economic data, and user behavior data. Perform preprocessing and data feature extraction on the power grid user data to obtain a power grid user data feature set.

[0157] Step S102: Classify the power grid user data to obtain sensitive data and non-sensitive data. Use AES encryption algorithm to encrypt the sensitive data and RSA encryption algorithm to encrypt the non-sensitive data. Establish an encrypted data key storage store and configure user permissions for the encrypted data key storage store. Store the keys corresponding to the sensitive data encrypted with AES encryption algorithm and the non-sensitive data encrypted with RSA encryption algorithm in the encrypted data key storage store.

[0158] Step S103: Set the power grid user data security inspection time period. According to the set power grid user data security inspection time period, perform security inspections on each interface for obtaining power grid user data and the data acquisition terminal. If there are security threats and security vulnerabilities in the detection results, generate data security warning information.

[0159] Step S104: Extract the data interface or data collection terminal information corresponding to the security risks from the data security early warning information. Determine the scope of security risk data based on the data interface and data collection terminal information. Filter out the data with security risks based on the scope of security risk data. Perform data analysis on the data with security risks to establish security risk simulation data. Replace the original data with the security risk simulation data according to the data generation time and category to obtain the first data group. Use the original data as the second data group.

[0160] Step S105: Receive the load analysis task; determine the data access range based on the load analysis task; match personnel permissions through the data access range to obtain personnel permission information; generate a permission request command based on the personnel permission information; receive feedback information based on the obtained permission request command; retrieve the key from the encrypted data key storage store; decrypt the first data group and the second data group using the key; substitute the decrypted first data group and the second data group into the preset load analysis to obtain the load analysis results of the first data group and the second data group; compare the load analysis results of the first data group and the second data group with the preset load safety value; and determine whether the electrical load is in a safe state, a suspected unsafe state, or an unsafe state based on the comparison results.

[0161] Specifically, the method for sensing power grid user electricity load based on multi-source data fusion provided by the present invention includes step S101, which includes:

[0162] The set of power grid user data features includes basic load data features, time series features, external influencing factor features, user behavior features, and load features;

[0163] Load characteristics include load trend characteristics, load anomaly detection characteristics, and load pattern classification characteristics.

[0164] Specifically, the method for sensing power grid user electricity load based on multi-source data fusion provided by the present invention includes step S103, which includes:

[0165] Based on the sensitivity of the data, power grid user data is divided into sensitive data and non-sensitive data. Sensitive data includes personal identity information, payment information, etc., while non-sensitive data includes electricity consumption habits and non-precise geographical locations.

[0166] Sensitive data is encrypted using the AES encryption algorithm, and non-sensitive data is encrypted using the RSA encryption algorithm, generating a public key and a private key pair. The public key is used to encrypt the data, and the private key is used to decrypt the data.

[0167] Establish an encrypted data key repository for the keys generated by the AES and RSA encryption algorithms to store the keys for all encrypted data;

[0168] Configure user permissions for the encrypted data key repository, and store the keys corresponding to sensitive data encrypted with the AES encryption algorithm and non-sensitive data encrypted with the RSA encryption algorithm in the encrypted data key repository.

[0169] Specifically, the method for sensing power grid user electricity load based on multi-source data fusion provided by the present invention includes step S104, which includes:

[0170] Receive early warning information from the data security early warning system, parse the early warning information, and extract information related to the data interface or data collection terminal of the security risks;

[0171] Based on the extracted data interface and data acquisition terminal information, analyze the data range involved in the data interface and acquisition terminal, and determine the specific range of the safety hazard data after determining the data range.

[0172] Based on the scope of the safety hazard data, filter out the data that poses safety hazards from the raw data;

[0173] In-depth data analysis was conducted on the screened data that posed safety hazards, and safety hazard simulation data was established based on the analysis results;

[0174] The safety hazard simulation data is replaced in the original data according to the data generation time and category to obtain the first data group. The original data that has not been replaced is retained as the second data group.

[0175] Specifically, the method for sensing power grid user electricity load based on multi-source data fusion provided by the present invention includes step S105, which includes:

[0176] Receive load analysis tasks, determine the range of data to be accessed based on the load analysis tasks, match the corresponding personnel permissions through the data access range, generate permission request commands based on the personnel permission information, and retrieve the corresponding keys from the encrypted data key store based on the feedback information of the permission request commands.

[0177] The first and second data groups are decrypted using the retrieved key. The decrypted first and second data groups are then prepared to be substituted into the preset load analysis for processing.

[0178] Substitute the decrypted data into the preset load analysis model to obtain the load analysis results of the first data set and the second data set;

[0179] The load analysis results of the first and second data sets are compared with the preset load safety values ​​to assess the safety status of the electrical load.

[0180] If the load analysis results of the first data group and the second data group are both within the preset load safety value range, the electrical load is in a safe state. If the load analysis results of the first data group are within the preset load safety value range, but the load analysis results of the second data group are not within the preset load safety value range, the electrical load is in a suspected unsafe state. If the load analysis results of the first data group are not within the preset load safety value range, and the load analysis results of the second data group are not within the preset load safety value range, the electrical load is in an unsafe state.

[0181] The technical solution of this invention effectively solves the problem that when the data used for power grid user load sensing is leaked and tampered with, or when the data source has errors, it is impossible to accurately determine whether the user load is in a safe state.

[0182] Data Encryption: The data encryption module categorizes and processes power grid user data, encrypting sensitive data (such as personal identification information and payment information) and non-sensitive data (such as electricity usage habits and imprecise geographical locations) using AES and RSA encryption algorithms respectively. This not only improves data security and prevents unauthorized access or tampering during transmission and storage, but also provides appropriate levels of protection for different types of data through different encryption algorithms.

[0183] Encrypted data key management: Establish an encrypted data key repository and configure user permissions for this repository to ensure that only authorized personnel can access and use the keys. This way, even if data is illegally obtained, it cannot be decrypted without the key, thus protecting the confidentiality of the data.

[0184] Data security inspection: Set a time cycle for data security inspection of power grid users, and regularly conduct security inspections on all interfaces and data acquisition terminals that acquire power grid user data. When a security threat or vulnerability is detected, a data security warning is immediately generated to promptly identify and address potential security risks.

[0185] Data fusion and hazard handling: Information from the data security early warning system is extracted to identify the data interfaces or data acquisition terminals corresponding to security hazards. The scope of the security hazard data is determined, and data with potential security hazards is filtered out. Subsequently, these data are analyzed in depth, and simulated security hazard data is created to replace them, generating a new dataset (first data group). Simultaneously, the original data is retained (second data group). This method allows the system to perform accurate load analysis based on the processed data even if the original data has issues.

[0186] Dual load analysis comparison: During the load analysis phase, load analysis is performed simultaneously on both the encrypted and decrypted first data set and the unmodified second data set. By comparing the analysis results of the two sets of data with preset load safety values, the system can more accurately determine the safety status of the electrical load. This dual analysis method improves the accuracy and reliability of the analysis because even if one set of data deviates, the other set can provide effective verification.

[0187] In summary, this invention effectively solves the security problems caused by leakage, tampering, or errors in the load perception data of power grid users through key technical measures such as data encryption, encrypted data key management, data security inspection, data fusion and hidden danger handling, and dual load analysis and comparison, thereby improving the accuracy and security of load perception.

[0188] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations. The above-described embodiments of this invention do not constitute a limitation on the scope of protection of this invention.

Claims

1. A power grid user power load perception system based on multi-source data fusion, characterized in that: include: A data acquisition module is used to acquire power grid user data, the power grid user data includes historical load data, meteorological data, socio-economic data and user behavior data, pre-process the power grid user data and extract data features to obtain a power grid user data feature set; A data encryption module, used to classify the power grid user data to obtain sensitive data and non-sensitive data, encrypt the sensitive data using the AES encryption algorithm, encrypt the non-sensitive data using the RSA encryption algorithm, establish an encrypted data key repository, and configure user permissions for the encrypted data key repository, and store the secret keys corresponding to the sensitive data encrypted by the AES encryption algorithm and the non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key repository; The security inspection module is used to set the security inspection time period of power grid user data, and to perform security inspections on various interfaces and data acquisition terminals for obtaining power grid user data according to the set security inspection time period of power grid user data. If there are security threats and security loopholes in the detection results, data security warning information is generated; A data fusion module is used to extract the data interface or data collection terminal information corresponding to the safety hazard from the data security warning information, determine the safety hazard data range according to the data interface and the data collection terminal information, screen out the data with safety hazard according to the safety hazard data range, perform data analysis on the data with safety hazard, establish safety hazard simulation data, replace the safety hazard simulation data in the original data according to the data generation time and category, obtain the first data group, and use the original data as the second data group; The load analysis module is used to receive a load analysis task, determine a data call scope according to the load analysis task, match personnel authority through the data call scope, obtain personnel authority information, generate an authority request command according to the personnel authority information, retrieve a secret key from an encrypted data key storage repository according to the obtained authority request command feedback information, decrypt the first data group and the second data group through the secret key, substitute the decrypted first data group and the second data group into a preset load analysis, obtain a load analysis result of the first data group and a load analysis result of the second data group, compare the load analysis result of the first data group and the load analysis result of the second data group with a preset load safety value, and judge whether the power load is in a safe state, a suspected unsafe state, or an unsafe state according to the comparison result.

2. The power grid user power load perception system based on multi-source data fusion according to claim 1 is characterized in that: The data acquisition module is also used for: The power grid user data feature set includes basic load data features, time series features, external influencing factor features, user behavior features and load features; Load characteristics include load trend characteristics, load anomaly detection characteristics, and load pattern classification characteristics.

3. The power grid user power load perception system based on multi-source data fusion according to claim 1 is characterized in that: The data encryption module is also used for: According to the sensitivity of the data, the power grid user data is divided into sensitive data and non-sensitive data. Sensitive data includes personal identity information, payment information, etc. Non-sensitive data includes electricity usage habits and non-precise geographic location; Use the AES encryption algorithm to encrypt sensitive data and the RSA encryption algorithm to encrypt non-sensitive data. Generate a pair of public and private keys. The public key is used to encrypt data and the private key is used to decrypt data. Establish an encrypted data key repository for the keys generated by AES and RSA encryption algorithms to store the keys for all encrypted data; Configure user permissions for the encrypted data key repository, and store the keys corresponding to sensitive data encrypted by the AES encryption algorithm and non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key repository.

4. The power grid user power load perception system based on multi-source data fusion according to claim 1 is characterized in that: The data fusion module is further used for: Receive warning information from the data security warning system, analyze the warning information, and extract information related to the safety hazard data interface or data collection terminal; According to the extracted data interface and data collection terminal information, the data range involved in the data interface and the collection terminal is analyzed, and after the data range is determined, the specific range of the safety hazard data is determined; According to the scope of potential safety hazard data, filter out data with potential safety hazard from the original data; Conduct in-depth data analysis on the screened data with potential safety hazards, and establish potential safety hazard simulation data based on the analysis results; The safety hazard simulation data is replaced in the original data according to the data generation time and category to obtain a first data group, and the original data that has not been replaced is retained as a second data group.

5. The power grid user power load perception system based on multi-source data fusion according to claim 1 is characterized in that: The load analysis module is also used for: Receive the load analysis task, determine the data range that needs to be called according to the load analysis task, match the corresponding personnel authority through the data call range, generate the authority request command according to the personnel authority information, and retrieve the corresponding key from the encrypted data key storage library according to the feedback information of the authority request command; Decrypting the first data group and the second data group using the retrieved key, and preparing to substitute the decrypted first data group and the second data group into a preset load analysis for processing; Substituting the decrypted data into a preset load analysis model to obtain load analysis results of the first data group and the second data group; Comparing the load analysis results of the first data group and the second data group with a preset load safety value to evaluate the safety status of the power load; If the load analysis results of the first data group and the load analysis results of the second data group are both within the preset load safety value range, the power load is in a safe state; if the load analysis results of the first data group are within the preset load safety value range, and the load analysis results of the second data group are not within the preset load safety value range, the power load is in a suspected unsafe state; if the load analysis results of the first data group are not within the preset load safety value range, and the load analysis results of the second data group are not within the preset load safety value range, the power load is in an unsafe state.

6. A method for sensing power load of power grid users based on multi-source data fusion, characterized in that: include: Step S101, acquiring power grid user data, the power grid user data including historical load data, meteorological data, social and economic data and user behavior data, preprocessing the power grid user data and extracting data features to obtain a power grid user data feature set; Step S102, classifying the power grid user data to obtain sensitive data and non-sensitive data, encrypting the sensitive data using the AES encryption algorithm, encrypting the non-sensitive data using the RSA encryption algorithm, establishing an encrypted data key repository, configuring user permissions for the encrypted data key repository, and storing the keys corresponding to the sensitive data encrypted by the AES encryption algorithm and the non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key repository; Step S103, setting a grid user data security inspection time period, performing security inspections on various interfaces for obtaining grid user data and data acquisition terminals according to the set grid user data security inspection time period, and generating data security warning information if there are security threats and security loopholes in the inspection results; Step S104, extracting the data interface or data collection terminal information corresponding to the safety hazard from the data security warning information, determining the safety hazard data range according to the data interface and the data collection terminal information, screening out the safety hazard data according to the safety hazard data range, performing data analysis on the safety hazard data, establishing safety hazard simulation data, replacing the safety hazard simulation data in the original data according to the data generation time and category, obtaining a first data group, and using the original data as the second data group; Step S105, receiving a load analysis task, determining a data call scope according to the load analysis task, matching personnel authority through the data call scope to obtain personnel authority information, generating an authority request command according to the personnel authority information, retrieving a secret key from an encrypted data key storage repository according to the obtained authority request command feedback information, decrypting the first data group and the second data group through the secret key, substituting the decrypted first data group and the second data group into a preset load analysis to obtain a load analysis result of the first data group and a load analysis result of the second data group, comparing the load analysis result of the first data group and the load analysis result of the second data group with a preset load safety value, and judging whether the power load is in a safe state, a suspected unsafe state, or an unsafe state according to the comparison result.

7. The method for sensing power load of power grid users based on multi-source data fusion according to claim 6, characterized in that: The step S101 includes: The power grid user data feature set includes basic load data features, time series features, external influencing factor features, user behavior features and load features; Load characteristics include load trend characteristics, load anomaly detection characteristics, and load pattern classification characteristics.

8. The method for sensing power load of power grid users based on multi-source data fusion according to claim 6, characterized in that: The step S103 includes: According to the sensitivity of the data, the power grid user data is divided into sensitive data and non-sensitive data. Sensitive data includes personal identity information, payment information, etc. Non-sensitive data includes electricity usage habits and non-precise geographic location; Use the AES encryption algorithm to encrypt sensitive data and the RSA encryption algorithm to encrypt non-sensitive data. Generate a pair of public and private keys. The public key is used to encrypt data and the private key is used to decrypt data. Establish an encrypted data key repository for the keys generated by AES and RSA encryption algorithms to store the keys for all encrypted data; Configure user permissions for the encrypted data key repository, and store the keys corresponding to sensitive data encrypted by the AES encryption algorithm and non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key repository.

9. The method for sensing power load of power grid users based on multi-source data fusion according to claim 6, characterized in that: The step S104 includes: Receive warning information from the data security warning system, analyze the warning information, and extract information related to the safety hazard data interface or data collection terminal; According to the extracted data interface and data collection terminal information, the data range involved in the data interface and the collection terminal is analyzed, and after the data range is determined, the specific range of the safety hazard data is determined; According to the scope of potential safety hazard data, filter out data with potential safety hazard from the original data; Conduct in-depth data analysis on the screened data with potential safety hazards, and establish potential safety hazard simulation data based on the analysis results; The safety hazard simulation data is replaced in the original data according to the data generation time and category to obtain a first data group, and the original data that has not been replaced is retained as a second data group.

10. The method for sensing power load of power grid users based on multi-source data fusion according to claim 6, characterized in that: The step S105 includes: Receive the load analysis task, determine the data range that needs to be called according to the load analysis task, match the corresponding personnel authority through the data call range, generate the authority request command according to the personnel authority information, and retrieve the corresponding key from the encrypted data key storage library according to the feedback information of the authority request command; Decrypting the first data group and the second data group using the retrieved key, and preparing to substitute the decrypted first data group and the second data group into a preset load analysis for processing; Substituting the decrypted data into a preset load analysis model to obtain load analysis results of the first data group and the second data group; Comparing the load analysis results of the first data group and the second data group with a preset load safety value to evaluate the safety status of the power load; If the load analysis results of the first data group and the load analysis results of the second data group are both within the preset load safety value range, the power load is in a safe state; if the load analysis results of the first data group are within the preset load safety value range, and the load analysis results of the second data group are not within the preset load safety value range, the power load is in a suspected unsafe state; if the load analysis results of the first data group are not within the preset load safety value range, and the load analysis results of the second data group are not within the preset load safety value range, the power load is in an unsafe state.

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