A power grid user power load perception system and method based on multi-source data fusion

Abstract

The present invention relates to the technical field of power equipment operation and maintenance. The present invention provides a power grid user power load perception system and method based on multi-source data fusion. The present invention aims to improve data security and load analysis accuracy. The system ensures the security of user data during transmission, storage and analysis through encryption, security inspection, data fusion and hidden danger processing. The multi-source data fusion technology integrates historical load, meteorological, social and economic and user behavior data to achieve comprehensive load perception. The dual load analysis mechanism combines original data with hidden danger processing data to improve analysis accuracy, providing a reliable basis for power system scheduling, optimization and fault prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment operation and maintenance, and in particular to a power grid user power load perception system and method based on multi-source data fusion. Background Art

[0002] The multi-source data in the power grid mainly comes from various links of power production and power use, including power generation, transmission, transformation, distribution, power consumption and dispatching. The multi-source data in the power grid is divided into three categories: power grid operation and equipment monitoring data: such as power equipment status monitoring data, power grid load data, power quality data, etc. Power enterprise marketing data: such as transaction electricity prices, electricity sales, electricity customers and other data. Power enterprise management data: data related to enterprise operations, management decisions and other aspects.

[0003] The power grid user load perception system is a system that integrates multiple data sources and advanced data analysis technologies. It aims to more accurately perceive and predict the power load of power grid users and provide strong support for the operation and management of the power system. The multi-source data fusion power grid user load perception system collects information from different data sources, such as historical load data, meteorological data, socio-economic data, user behavior data, etc., and uses advanced data fusion and analysis technologies to comprehensively and accurately perceive and predict user power loads. The system can reflect the power consumption of power grid users in real time, providing an important basis for the dispatch, optimization and fault prediction of the power system.

[0004] The power grid user power load perception system based on multi-source data fusion has the following technical pain points in actual use. The power grid user power load perception system based on multi-source data fusion involves a large amount of user power consumption data and enterprise operation data. When performing power grid user power load perception, when the data used for power grid user power load perception is leaked and tampered with, or there are errors in the data source data, it will cause errors in power grid user power load perception, and it will be impossible to accurately judge whether the user load is in a safe state. In order to improve the security of the power grid user power load perception system based on multi-source data fusion and the accuracy of data processing, the present invention provides a power grid user power load perception system and method based on multi-source data fusion. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a power grid user power load perception system and method based on multi-source data fusion, which solves the problem that when the data used for power grid user power load perception is leaked or tampered with, or there are errors in the data source data, it will cause errors in power grid user power load perception and it is impossible to accurately judge whether the user load is in a safe state.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

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

[0008] A data acquisition module is used to acquire power grid user data, the power grid user data includes historical load data, meteorological data, social and economic data, and user behavior data, and pre-process the power grid user data and extract data features to obtain a power grid user data feature set;

[0009] 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;

[0010] The security inspection module is used to set the time period for the security inspection 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 time period for the security inspection of power grid user data. If there are security threats and security loopholes in the detection results, data security warning information is generated;

[0011] 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;

[0012] 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.

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

[0014] 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;

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

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

[0017] 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;

[0018] 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.

[0019] Establish an encrypted data key repository for the keys generated by 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 by the AES encryption algorithm and non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key repository.

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

[0022] 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;

[0023] 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;

[0024] According to the scope of potential safety hazard data, filter out data with potential safety hazard from the original data;

[0025] 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;

[0026] 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.

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

[0028] 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;

[0029] 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;

[0030] 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;

[0031] 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;

[0032] 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.

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

[0034] 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;

[0035] 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;

[0036] 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;

[0037] 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;

[0038] 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.

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

[0040] 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;

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

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

[0043] 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;

[0044] 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.

[0045] Establish an encrypted data key repository for the keys generated by 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 by the AES encryption algorithm and non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key repository.

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

[0048] 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;

[0049] 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;

[0050] According to the scope of potential safety hazard data, filter out data with potential safety hazard from the original data;

[0051] 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;

[0052] 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.

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

[0054] 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;

[0055] 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;

[0056] 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;

[0057] 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;

[0058] 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.

[0059] Beneficial effects of the present invention:

[0060] The present invention classifies and encrypts the data of power grid users, uses the AES encryption algorithm for sensitive data and the RSA encryption algorithm for non-sensitive data, 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 key, further enhancing data security.

[0061] The present invention sets a time period for security inspection of power grid user data, regularly conducts security inspections on various interfaces and data acquisition terminals, promptly discovers and responds to potential security threats and loopholes, and ensures stable operation of the system. The data range corresponding to the potential safety hazard is extracted from the data security warning information, and data analysis and simulation replacement are performed to ensure that even if there are problems with the original data, the system can perform accurate load analysis based on the processed data.

[0062] The present invention uses multi-source data fusion technology to collect multiple data sources including historical load data, meteorological data, social and economic data, user behavior data, etc., to comprehensively perceive and predict the power load of power grid users. In the load analysis stage, the load analysis is performed on the encrypted and decrypted first data group and the unmodified second data group at the same time. By comparing the double analysis results, the accuracy and reliability of the load analysis are improved.

[0063] The system and method of the present invention can reflect the power consumption of power grid users in real time, provide important basis for the dispatch, optimization and fault prediction of power systems, and help power system operators make more scientific and reasonable decisions. By outputting detailed load analysis results and reports, it helps power grid operators understand the safety status of user power loads and timely discover and respond to potential safety risks.

[0064] In summary, the present invention effectively solves the data security problem in power grid users' power load perception through key technical measures such as data encryption, encrypted data key management, data security inspection, data fusion and hidden danger processing, and dual load analysis and comparison, 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] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

[0067] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0068] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

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

[0070] A data acquisition module is used to acquire power grid user data, the power grid user data includes historical load data, meteorological data, social and economic data, and user behavior data, and pre-process the power grid user data and extract data features to obtain a power grid user data feature set;

[0071] The data acquisition module in the present invention obtains relevant data of power grid users from multiple sources, preprocesses these data and extracts data features, and finally forms a power grid user data feature set. The following are the specific functions and processes of this module:

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

[0073] Meteorological data: including temperature, humidity, wind speed and other meteorological factors, which have a significant impact on users' electricity consumption. For example, the power consumption of air conditioners increases during high temperatures in summer.

[0074] Socioeconomic data: Such as regional economic development level, industrial structure, demographics, etc. These data help understand the socioeconomic background of user load.

[0075] User behavior data: Real-time electricity usage behavior information of users collected through smart meters, smart home systems and other devices, such as device switching time, power consumption, etc.

[0076] Data acquisition method: Obtain historical load data and socio-economic data through the data center of the power grid company. Cooperate with the meteorological department or use public meteorological data sources to obtain meteorological data. Obtain user behavior data through smart home systems, user apps and other channels.

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

[0078] Data format unification: 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 impact of dimension and make data from different sources comparable.

[0080] Basic load data characteristics: Extract basic load characteristics such as user's average load, peak load, valley load, etc.

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

[0082] Characteristics of external influencing factors: Extract characteristics of external factors that affect user load, such as temperature, holidays, etc., from meteorological data and socio-economic data.

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

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

[0085] Load anomaly detection feature: Identifies abnormal points or abnormal patterns in user load.

[0086] Load pattern classification characteristics: User loads are 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 grid user data feature set, which contains rich user load characteristics, providing a solid foundation for subsequent data encryption, security inspections, 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 perception.

[0089] 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;

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

[0091] The data encryption module will first classify the acquired power grid user data to distinguish between sensitive data and non-sensitive data. Sensitive data usually includes user personal identity information, payment information, etc. The leakage of this data may bring serious privacy and security risks to users. Non-sensitive data includes information such as users' electricity usage habits and non-precise geographic locations. Even if this information is leaked, the risk is relatively low.

[0092] AES encryption algorithm: For sensitive data, use the AES (Advanced Encryption Standard) encryption algorithm for encryption. AES is a widely used symmetric encryption algorithm with the characteristics of high efficiency and security.

[0093] RSA encryption algorithm: For non-sensitive data, the RSA encryption algorithm is used for encryption. RSA is an asymmetric encryption algorithm that uses a pair of keys (public key and private key) for encryption and decryption. The public key is used to encrypt data, and the private key is used to decrypt data. Since the key length of the RSA encryption algorithm is longer (usually 1024 bits or longer), it can provide a higher level of security.

[0094] Encrypted data key repository, in order to safely store the keys generated during the encryption process, an encrypted data key repository is established. This repository is not only used to store the keys for all encrypted data, but also strictly controls access rights. Only authorized personnel can access and use these keys, thus ensuring the security of the keys.

[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 to ensure that only people with appropriate permissions can access and operate the keys in the repository.

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

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

[0098] 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;

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

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

[0101] During the set inspection cycle, the security inspection module will conduct a comprehensive inspection of each interface for obtaining power grid user data and the data collection 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] Security of data collection end: Check the physical and network security of the data collection end to ensure that the data will not be tampered with or leaked during the collection process.

[0104] System log review: Analyze system logs for unusual login attempts, changes in data access patterns, and other signs that could indicate a security threat.

[0105] Detect security threats and vulnerabilities

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

[0107] When a security threat or vulnerability is detected, the security inspection module will immediately generate data security warning information. These warnings usually contain the type, location, severity and recommended countermeasures of the threat. This information will be communicated to the system administrator or relevant security team in a timely manner so that they can take appropriate measures to eliminate the threat or repair the vulnerability.

[0108] After receiving the warning information, the system administrator or security team will take necessary countermeasures according to the specific situation, such as shutting down the affected interface, isolating the infected device, updating the security patch, etc. At the same time, they will also record the entire incident handling process and results to provide 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 acquisition, preventing data leakage, tampering or other security risks. This provides important security guarantees for the power grid user load perception system based on multi-source data fusion.

[0110] 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;

[0111] The data fusion module is responsible for processing data security warning information in the power grid user load perception system based on multi-source data fusion, identifying and processing potential safety hazard data to improve the accuracy and reliability of load analysis. The following are the specific functions and processes of this module:

[0112] When the security inspection module generates data security warning information, the data fusion module will first receive and parse the warning information, which contains the specific information of the data interface or data acquisition terminal where the safety hazard was discovered.

[0113] According to the data interface and collection end information provided in the early warning information, the data fusion module will further analyze the data scope involved in these interfaces and collection ends. By tracking the flow and association of data, the module can determine which data may be affected by security risks, thereby defining the specific scope of security risk data.

[0114] After determining the scope of the potential safety hazard data, the data fusion module will filter out the data with potential safety hazards from the original data. These data may have been tampered with, damaged or contain inaccurate information, and therefore cannot be directly used for load analysis.

[0115] For the data with potential safety hazards that have been screened out, the data fusion module will conduct in-depth data analysis. By analyzing the characteristics, patterns, and possible anomalies of these data, the module can better understand the nature and impact of the safety hazards. Based on these analysis results, the module will establish safety hazard simulation data, which is designed to reflect the potential safety issues in the original data, but at the same time maintain the integrity and consistency of the data for subsequent analysis.

[0116] After the safety hazard simulation data is established, the data fusion module will make corresponding replacements in the original data according to the data generation time and category. The replaced data set is called the first data group, which contains the processed safety hazard simulation data. At the same time, the module will also retain a copy of the original data that has not been replaced as the second data group for subsequent comparative analysis and verification.

[0117] After the above processing, the data fusion module will output two data sets: the first data set (containing safety hazard simulation data) and the second data set (original data). These two data sets will be used in the subsequent load analysis stage to evaluate the power load status of power grid users.

[0118] Through the processing of the data fusion module, the system can respond quickly when potential safety hazards are discovered, and reduce the impact on the load analysis results by replacing potential safety hazard data. At the same time, retaining the original data as a comparison benchmark helps to verify the processing effect and improve the accuracy of the analysis. This dual data set design increases the flexibility and reliability of the system and provides strong support for the accurate perception of power load by power grid users.

[0119] 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.

[0120] The load analysis module is responsible for conducting in-depth load analysis on the processed data in the power grid user power load perception system based on multi-source data fusion to evaluate the power grid user's power load status.

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

[0122] According to the requirements of the load analysis task, the load analysis module will determine the data range that needs to be called, including a specific time range, data type (such as historical load data, meteorological data, etc.) or a specific user group.

[0123] To ensure data security and compliance, the load analysis module will match the corresponding personnel permissions based on the called data scope. The system needs to verify whether the user or process performing the load analysis task has the permission to access the 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, which will send a request to the system's permission management system to request permission to access the required data.

[0125] When the permission is approved, the load analysis module will retrieve the encryption key corresponding to the required data from the encrypted data key repository, and 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 group (containing the safety hazard simulation data) and the second data group (the original data). The decrypted data will be restored to its original format for subsequent analysis.

[0127] The decrypted first data group and second data group will be substituted into a preset load analysis model for analysis. These models may be based on machine learning, statistical analysis or other data analysis techniques to evaluate the power load status of power grid users.

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

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

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

[0131] Unsafe state: If the load analysis results of both sets of data are not within the preset range, the power load is judged to be in an unsafe state. At this time, immediate measures need to be taken to reduce risks and prevent potential accidents.

[0132] Output results and reports

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

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

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

[0136] 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;

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

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

[0139] 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;

[0140] 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.

[0141] Establish an encrypted data key repository for the keys generated by 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 by the AES encryption algorithm and non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key repository.

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

[0144] 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;

[0145] 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;

[0146] According to the scope of potential safety hazard data, filter out data with potential safety hazard from the original data;

[0147] 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;

[0148] 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.

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

[0150] 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;

[0151] 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;

[0152] 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;

[0153] 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;

[0154] 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.

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

[0156] 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;

[0157] 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;

[0158] 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;

[0159] 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;

[0160] 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.

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

[0162] 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;

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

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

[0165] 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;

[0166] 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.

[0167] Establish an encrypted data key repository for the keys generated by 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 by the AES encryption algorithm and non-sensitive data encrypted by the RSA encryption algorithm in the encrypted data key repository.

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

[0170] 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;

[0171] 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;

[0172] According to the scope of potential safety hazard data, filter out data with potential safety hazard from the original data;

[0173] 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;

[0174] 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.

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

[0176] 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;

[0177] 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;

[0178] 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;

[0179] 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;

[0180] 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.

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

[0182] Data encryption: Through the data encryption module, the data of power grid users is classified and processed, and sensitive data (such as personal identity information, payment information, etc.) and non-sensitive data (such as electricity usage habits, non-precise geographic location, etc.) are encrypted using AES and RSA encryption algorithms respectively. This not only improves the security of data and prevents data from being illegally obtained or tampered with during transmission and storage, but also provides appropriate protection levels 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 the repository to ensure that only authorized personnel can access and use the keys. In this way, even if the data is illegally obtained, it cannot be decrypted without the key, thus protecting the confidentiality of the data.

[0184] Data security inspection: Set the time period for grid user data security inspection, and regularly conduct security inspections on various interfaces for obtaining grid user data and data collection terminals. When security threats or security vulnerabilities are detected, data security warning information is immediately generated to timely discover and respond to potential security risks.

[0185] Data fusion and hidden danger processing: Extract the data interface or data collection terminal information corresponding to the hidden danger in the data security warning information, determine the range of hidden danger data, and filter out the data with hidden dangers. Then, conduct in-depth analysis on these data, establish hidden danger simulation data for replacement, generate a new data set (first data group), and retain the original data (second data group). This method can perform accurate load analysis based on the processed data even if there are problems with the original data.

[0186] Double load analysis comparison: During the load analysis phase, the encrypted and decrypted first data group and the unmodified second data group are analyzed at the same time. By comparing the analysis results of the two sets of data with the preset load safety value, the system can more accurately determine the safety status of the power load. This dual analysis method improves the accuracy and reliability of the analysis because even if one set of data deviates, the other set of data can provide effective verification.

[0187] To sum up, the present invention effectively solves the security problems caused by leakage, tampering or errors of power grid users' load perception data through key technical measures such as data encryption, encrypted data key management, data security inspection, data fusion and hidden danger processing, 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 changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present 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 further used for: According to the sensitivity of the data, the grid user data is divided into sensitive data and non-sensitive data. Sensitive data includes personal identity information and payment information, and 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 also 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 grid user data is divided into sensitive data and non-sensitive data. Sensitive data includes personal identity information and payment information, and 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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