Cloud-native-based power grid real-time data parallel processing system and method

Abstract

The invention discloses a cloud native-based power grid real-time data parallel processing system and method, and belongs to the field of power grid data parallel processing. The invention discloses a cloud native-based power grid real-time data parallel processing system, which comprises a data acquisition module, a data transmission module, a cloud native distributed database and a parallel processing module. According to the invention, the problems that data loss is easily caused in a transmission process and a data processing result is influenced due to an unstable condition during data transmission in the prior art are solved, the operation and maintenance cost is greatly reduced, the real-time data of the power grid can be effectively prevented from being accessed by unauthorized people in the transmission process, and the real-time data transmission efficiency is improved. According to the method, the confidentiality and integrity of the real-time data of the power grid are protected, the security and reliability of the real-time data transmission of the power grid are improved, a plurality of data blocks can be processed at the same time through parallel processing of the real-time data of the power grid, the data processing speed and efficiency are remarkably improved, analysis and processing of large-scale data can be completed more quickly, and the processing time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of parallel processing of power grid data, and in particular to a cloud-native-based real-time parallel processing system and method for power grid data. Background Art

[0002] A Chinese patent with publication number CN112583122A discloses a parallel processing device and method for real-time dynamic monitoring data of a power system. A parallel processing method for collected messages is adopted in the real-time dynamic monitoring client, and the collected dynamic data is allocated to the real-time comparison module of the dynamic monitoring data processing process for data comparison and filtering. After the comparison, the real-time transmission module and the storage control module are synchronously allocated, so that the real-time transmission module and the storage control module synchronously process the dynamic monitoring data, improve the processing efficiency of processing dynamic data, shorten the processing time, and solve the technical problem of low data processing efficiency of the existing real-time dynamic monitoring client and the intelligent remote motor PMU business service end.

[0003] In actual use, the above-mentioned patent is prone to data loss during transmission due to unstable data transmission, which in turn affects the results of data processing; therefore, it does not meet existing needs. In this regard, we propose a cloud-native-based power grid real-time data parallel processing system and method. Summary of the invention

[0004] The purpose of the present invention is to provide a cloud-native-based parallel processing system and method for real-time data of a power grid, which can dynamically adjust resources according to actual needs, avoid the performance bottleneck of traditional databases when facing peak loads, greatly reduce operation and maintenance costs, and effectively prevent the real-time data of the power grid from being accessed by unauthorized persons during transmission, protect the confidentiality and integrity of the real-time data of the power grid, and improve the security and reliability of the real-time data transmission of the power grid. By processing the real-time data of the power grid in parallel, multiple data blocks can be processed at the same time, significantly improving the speed and efficiency of data processing, and can complete the analysis and processing of large-scale data more quickly, reducing processing time, and solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a cloud-native-based real-time data parallel processing system for power grids, comprising:

[0006] The data acquisition module is used to collect real-time data of the power grid, remove interference and enhance the collected real-time data of the power grid, and clean the real-time data of the power grid;

[0007] A data transmission module, used to perform key generation and data encryption on the transmitted real-time power grid data using homomorphic encryption, generate chain encryption access logs and dynamic permission configuration files, and generate a backup database for real-time power grid data;

[0008] Cloud-native distributed database, which is used to establish a data storage database based on the cloud-native architecture, store the received real-time power grid data, and correct and complete abnormal real-time power grid data;

[0009] The parallel processing module is used to construct a parallel processing engine for real-time power grid data, group the data volume of real-time power grid data, and use the parallel processing engine for real-time power grid data to process multiple groups of real-time power grid data in parallel.

[0010] Preferably, the data acquisition module includes:

[0011] Data collection module, used to collect real-time data of power grid;

[0012] The preprocessing module is used to remove interference and enhance the collected real-time data of the power grid, and to clean the real-time data of the power grid.

[0013] Preferably, the preprocessing module comprises:

[0014] Remove communication interference, white noise interference and random pulse interference from the collected real-time data of the power grid;

[0015] Performing data enhancement on the data after interference removal so that the characteristic parameters in the data after interference removal are enhanced;

[0016] If there are two column names with the same column name or the same meaning in the real-time data column of the power grid, rename the column name of one of the data columns;

[0017] If there are duplicate data values ​​in the real-time data column of the power grid, the duplicate data values ​​will be deleted, and the first record of the duplicate data will be retained during the deletion;

[0018] If there are missing values ​​in the data of the real-time data column of the power grid, statistical methods are used to fill in the missing values ​​in the data;

[0019] If there are inconsistent data or naming rules in the data column, use the split function to split the data values ​​in the inconsistent data column;

[0020] Filter and sort the data in the data column, and identify and process outliers in the data.

[0021] Preferably, the data transmission module includes:

[0022] A transmission encryption module is used to encrypt the transmitted real-time data of the power grid using homomorphic encryption and generate chained encryption access logs and dynamic permission profiles;

[0023] The decryption module is used to obtain a secret key corresponding to the dynamic rights configuration file based on the dynamic rights configuration file, and decrypt the dynamic rights configuration file according to the corresponding secret key.

[0024] The data backup module is used to back up the uploaded real-time data of the power grid according to the time point of long transmission during the real-time data transmission of the power grid, and generate a backup database.

[0025] Preferably, the cloud-native distributed database includes:

[0026] The storage module is used to establish a data storage database based on the cloud-native architecture to store the received real-time power grid data;

[0027] The safety protection module is used to compare the real-time data of the power grid transmitted to the storage module, and to correct and complete the abnormal real-time data of the power grid according to the comparison result.

[0028] Preferably, the data defense module includes:

[0029] A data comparison module is used to compare the real-time data of the power grid transmitted to the storage module with the real-time data of the power grid in the backup database;

[0030] A data partitioning module, used to transmit the real-time data of the power grid with consistent comparison results to the first buffer pool for storage, and transmit the real-time data of the power grid with inconsistent comparison results to the second buffer pool;

[0031] The data compensation module is used to correct and complete abnormal values ​​of the real-time data of the power grid in the second cache database.

[0032] Preferably, the workflow of the data defense module includes:

[0033] Compare the real-time data of the power grid monitored by the data monitoring module with the real-time data of the power grid in the backup database;

[0034] If the comparison result is inconsistent, it proves that the real-time data of the power grid has been tampered with or lost, and the data is stored in the second cache pool, and the time point when the real-time data of the power grid has been tampered with or lost is marked;

[0035] If the results are consistent, it proves that the real-time database of the power grid has not been tampered with or lost, and the data is stored in the first cache pool;

[0036] Determine the abnormal time point of the abnormal real-time data of the power grid in the second buffer pool, retrieve the corresponding data from the backup data according to the abnormal time point for correction and completion, and transmit the data to the first buffer pool for storage after the correction and completion are completed.

[0037] Preferably, the parallel processing module includes:

[0038] The engine construction optimization module is used to construct a power grid real-time data parallel processing engine, and to evaluate and optimize the performance of the constructed power grid real-time data parallel processing engine;

[0039] A grouping module, used to judge the real-time data of the power grid in the first buffer pool, and group the real-time data of the power grid in the first buffer pool according to the judgment result;

[0040] The processing module is used to perform parallel processing on the grouped real-time power grid data using the parallel processing engine for real-time power grid data.

[0041] Preferably, the engine construction optimization module includes:

[0042] The engine building module is used to build multiple cores based on the Serverless protocol, build a hash map structure in each core, and use the hash map structure to build a parallel processing engine for real-time data in the power grid;

[0043] Performance evaluation module, used to evaluate the performance of the constructed power grid real-time data parallel processing engine, including throughput, response time, resource utilization and other indicators;

[0044] The optimization module is used to optimize the power grid real-time data parallel processing engine according to the performance evaluation result of the power grid real-time data parallel processing engine.

[0045] A cloud-native-based parallel processing method for real-time data of a power grid includes the following steps:

[0046] Step 1: Establish a data storage database based on the cloud native architecture, then collect real-time power grid data and pre-process the collected real-time power grid data;

[0047] Step 2: Transmit the pre-processed real-time power grid data. During the transmission process, homomorphic encryption is used to perform key generation and data encryption on the transmitted real-time power grid data, generate chain encryption access logs and dynamic permission configuration files, and generate a real-time power grid data backup database;

[0048] Step 3: storing the received real-time grid data and comparing the received real-time grid data with the data in the backup database. When the comparison result passes, the real-time grid data is sent to the first buffer pool to receive the next set of real-time grid data.

[0049] Step 4: When the comparison result fails, an analysis report corresponding to the comparison result is determined based on the comparison result to determine whether the transmitted real-time power grid data is tampered with or lost, and the data is transmitted to the second cache pool, and then the abnormal point of the transmitted real-time power grid data is determined, and the corresponding data is retrieved from the backup data according to the abnormal point for correction and completion;

[0050] Step 5: Build a parallel processing engine for real-time power grid data, group the data volume of real-time power grid data, and use the parallel processing engine for real-time power grid data to process multiple groups of real-time power grid data in parallel.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention utilizes cloud native to construct a data storage database, which can dynamically adjust resources according to actual needs, avoiding the performance bottleneck of traditional databases when facing peak loads, ensuring the availability and integrity of data in the event of a failure, and greatly reducing operation and maintenance costs. The real-time data of the power grid is encrypted during the process of real-time data transmission of the power grid, which can effectively prevent the real-time data of the power grid from being accessed by unauthorized persons during the transmission process, protect the confidentiality and integrity of the real-time data of the power grid, ensure that the real-time data of the power grid is not tampered with or stolen during the transmission process, and improve the security and reliability of the real-time data transmission of the power grid. By processing the real-time data of the power grid in parallel, multiple data blocks can be processed at the same time, which significantly improves the speed and efficiency of data processing, and can complete the analysis and processing of large-scale data more quickly, reducing processing time. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of a cloud-native-based power grid real-time data parallel processing system of the present invention;

[0054] Figure 2 It is a schematic diagram of a cloud-native-based parallel processing method for real-time power grid data of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0056] In order to solve the problem that the existing technology is unstable during data transmission, which easily leads to data loss during the transmission process and thus affects the results of data processing, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0057] A cloud-native-based power grid real-time data parallel processing system, comprising:

[0058] The data acquisition module is used to collect real-time data of the power grid, remove interference and enhance the collected real-time data of the power grid, and clean the real-time data of the power grid;

[0059] A data transmission module, used to perform key generation and data encryption on the transmitted real-time power grid data using homomorphic encryption, generate chain encryption access logs and dynamic permission configuration files, and generate a backup database for real-time power grid data;

[0060] Cloud-native distributed database, which is used to establish a data storage database based on the cloud-native architecture, store the received real-time power grid data, and correct and complete abnormal real-time power grid data;

[0061] The parallel processing module is used to construct a parallel processing engine for real-time power grid data, group the data volume of real-time power grid data, and use the parallel processing engine for real-time power grid data to process multiple groups of real-time power grid data in parallel.

[0062] Data acquisition module, including:

[0063] Data collection module, used to collect real-time data of power grid;

[0064] The preprocessing module is used to remove interference and enhance the collected real-time data of the power grid, and to clean the real-time data of the power grid.

[0065] Preprocessing modules include:

[0066] Remove communication interference, white noise interference and random pulse interference from the collected real-time data of the power grid;

[0067] Performing data enhancement on the data after interference removal so that the characteristic parameters in the data after interference removal are enhanced;

[0068] If there are two column names with the same column name or the same meaning in the real-time data column of the power grid, rename the column name of one of the data columns;

[0069] If there are duplicate data values ​​in the real-time data column of the power grid, the duplicate data values ​​will be deleted, and the first record of the duplicate data will be retained during the deletion;

[0070] If there are missing values ​​in the data of the real-time data column of the power grid, statistical methods are used to fill in the missing values ​​in the data;

[0071] If there are inconsistent data or naming rules in the data column, use the split function to split the data values ​​in the inconsistent data column;

[0072] Filter and sort the data in the data column, and identify and process outliers in the data.

[0073] Data transmission module, including:

[0074] The transmission encryption module is used to encrypt the transmitted real-time data of the power grid using homomorphic encryption, generate chain encryption access logs and dynamic permission configuration files, and ensure the security of data use;

[0075] The encryption process of the transmission encryption module specifically includes: using homomorphic encryption to perform data encryption on the transmitted real-time power grid data to obtain an encrypted real-time power grid data set; using a hash function to generate an encrypted log for the encrypted real-time power grid data set, and linking it into a chain data structure to generate a chain encryption access log; according to the operator's role and data access requirements, performing permission analysis on the chain encryption access log, dynamically updating access rules, allocating and adjusting access permissions, and generating a dynamic permission configuration file;

[0076] The decryption module is used to obtain a secret key corresponding to the dynamic rights configuration file based on the dynamic rights configuration file, and decrypt the dynamic rights configuration file according to the corresponding secret key.

[0077] The data backup module is used to back up the uploaded real-time data of the power grid according to the time point of long transmission during the real-time data transmission of the power grid, and generate a backup database.

[0078] Encrypting the real-time data of the power grid during the transmission process can effectively prevent the real-time data of the power grid from being accessed by unauthorized persons during the transmission process, reduce the risk of leakage of the real-time data of the power grid, protect the confidentiality and integrity of the real-time data of the power grid, and protect the privacy information of individuals and institutions from being leaked, ensure that the privacy rights and interests of users are protected, prevent personal privacy information from being maliciously used, ensure that the real-time data of the power grid is not tampered with or stolen during the transmission process, improve the security and reliability of the real-time data transmission of the power grid, and at the same time effectively prevent malicious attackers from stealing, tampering with or destroying the real-time data of the power grid, improve data security, and protect data from malicious attacks.

[0079] Cloud-native distributed database, including:

[0080] The storage module is used to establish a data storage database based on the cloud-native architecture to store the received real-time power grid data;

[0081] Using cloud native to build a data storage database can dynamically adjust resources according to actual needs, avoiding the performance bottleneck of traditional databases when facing peak loads, ensuring the availability and integrity of data in the event of a failure, and greatly reducing operation and maintenance costs.

[0082] The security protection module is used to compare the real-time data of the power grid transmitted to the storage module, and to correct and complete the abnormal real-time data of the power grid according to the comparison results. When the data is stolen or tampered with, it can timely understand the abnormal situation of the real-time data of the power grid and the time period when the abnormality occurs, saving the time for data anomaly investigation, improving work efficiency and ensuring the integrity of data transmission.

[0083] Data defense module, including:

[0084] A data comparison module is used to compare the real-time data of the power grid transmitted to the storage module with the real-time data of the power grid in the backup database;

[0085] A data partitioning module, used to transmit the real-time data of the power grid with consistent comparison results to the first buffer pool for storage, and transmit the real-time data of the power grid with inconsistent comparison results to the second buffer pool;

[0086] The data compensation module is used to correct and complete abnormal values ​​of the real-time data of the power grid in the second cache database.

[0087] The workflow of the data defense module includes:

[0088] Compare the real-time data of the power grid monitored by the data monitoring module with the real-time data of the power grid in the backup database;

[0089] If the comparison result is inconsistent, it proves that the real-time data of the power grid has been tampered with or lost, and the data is stored in the second cache pool, and the time point when the real-time data of the power grid has been tampered with or lost is marked;

[0090] If the results are consistent, it proves that the real-time database of the power grid has not been tampered with or lost, and the data is stored in the first cache pool;

[0091] Determine the abnormal time point of the abnormal real-time data of the power grid in the second buffer pool, retrieve the corresponding data from the backup data according to the abnormal time point for correction and completion, and transmit the data to the first buffer pool for storage after the correction and completion are completed.

[0092] The currently received real-time power grid data is compared with the real-time power grid data in the backup database. When the comparison result passes, it proves that the transmitted real-time power grid data has not been tampered with or lost, and the real-time power grid data is sent to the first cache pool to receive the next set of real-time power grid data. When the comparison result fails, based on the comparison result, an analysis report corresponding to the comparison result is determined to determine whether the transmitted real-time power grid data has been tampered with or lost, and the data is transmitted to the second cache pool to determine the abnormal points of the transmitted real-time power grid data, and retrieve the corresponding data from the backup data according to the abnormal points for correction and completion, and then receive the next set of real-time power grid data.

[0093] Parallel processing modules, including:

[0094] The engine construction optimization module is used to construct a power grid real-time data parallel processing engine, and to evaluate and optimize the performance of the constructed power grid real-time data parallel processing engine;

[0095] A grouping module, used to judge the real-time data of the power grid in the first buffer pool, and group the real-time data of the power grid in the first buffer pool according to the judgment result;

[0096] The grouping module specifically includes: determining whether the data volume of the real-time data of the power grid in the first buffer pool is less than the data volume threshold of the single task; if the data volume of the real-time data of the power grid is greater than or equal to the data volume threshold of the single task, intercepting the real-time data of the power grid in a time sequence with the data volume threshold of the single task as a unit step length until the remaining data volume is less than the data volume threshold of the single task, thereby obtaining multiple groups of real-time data of the power grid;

[0097] The processing module is used to perform parallel processing on the grouped real-time power grid data using the parallel processing engine for real-time power grid data.

[0098] The processing module specifically includes partitioning the real-time data of the power grid to obtain M target partition data, creating M process tasks for the M target partition data, where M is greater than or equal to 1; judging whether the number of process tasks is greater than the preset parallel number, if the number of process tasks is less than or equal to the preset parallel number, using the power grid real-time data parallel processing engine to parallel process the process tasks; if the number of process tasks is greater than the preset parallel number, using the power grid real-time data parallel processing engine to parallel process the M process tasks, and queuing the remaining process tasks for parallel processing.

[0099] Through parallel processing of real-time data of the power grid, multiple data blocks can be processed at the same time, significantly improving the speed and efficiency of data processing, completing the analysis and processing of large-scale data more quickly, and reducing processing time. Through parallel processing, computing resources can be used more effectively and idle resources can be avoided.

[0100] Engine build optimization modules, including:

[0101] The engine building module is used to build multiple cores based on the Serverless protocol, build a hash map structure in each core, and use the hash map structure to build a parallel processing engine for real-time data in the power grid;

[0102] Performance evaluation module, used to evaluate the performance of the constructed power grid real-time data parallel processing engine, including throughput, response time, resource utilization and other indicators;

[0103] The optimization module is used to optimize the power grid real-time data parallel processing engine according to the performance evaluation result of the power grid real-time data parallel processing engine.

[0104] A cloud-native-based parallel processing method for real-time data of a power grid includes the following steps:

[0105] Step 1: Establish a data storage database based on the cloud native architecture, then collect real-time power grid data and pre-process the collected real-time power grid data;

[0106] Obtain the parallel computing task DAG calculation graph and computing resource node topology graph, extract their features and construct a graph search space. Then, merge the heterogeneous computing resource nodes, and merge the computing resource nodes based on the operator's sensitivity to the heterogeneity of computing power to reduce the search space. Finally, it is proposed to achieve optimal scheduling of critical paths based on path cost prediction, and use technologies such as containers and Server l ess to achieve subtask operation management, reduce the execution time of the DAG calculation graph, and improve the efficiency of parallel strategies;

[0107] Step 2: Transmit the pre-processed real-time power grid data. During the transmission process, homomorphic encryption is used to perform key generation and data encryption on the transmitted real-time power grid data, generate chain encryption access logs and dynamic permission configuration files, and generate a real-time power grid data backup database;

[0108] Step 3: storing the received real-time grid data and comparing the received real-time grid data with the data in the backup database. When the comparison result passes, the real-time grid data is sent to the first buffer pool to receive the next set of real-time grid data.

[0109] Step 4: When the comparison result fails, an analysis report corresponding to the comparison result is determined based on the comparison result to determine whether the transmitted real-time power grid data is tampered with or lost, and the data is transmitted to the second cache pool, and then the abnormal point of the transmitted real-time power grid data is determined, and the corresponding data is retrieved from the backup data according to the abnormal point for correction and completion;

[0110] Step 5: Build a parallel processing engine for real-time power grid data, group the data volume of real-time power grid data, and use the parallel processing engine for real-time power grid data to process multiple groups of real-time power grid data in parallel.

[0111] In summary, a cloud-native-based parallel processing system and method for real-time data of a power grid of the present invention uses cloud-native to build a data storage database, can dynamically adjust resources according to actual needs, avoid the performance bottleneck of traditional databases when facing peak loads, ensure the availability and integrity of data in the event of a fault, and greatly reduce the operation and maintenance costs. In the process of real-time data transmission of the power grid, the real-time data of the power grid is encrypted, which can effectively prevent the real-time data of the power grid from being accessed by unauthorized persons during the transmission process, reduce the risk of leakage of real-time data of the power grid, protect the confidentiality and integrity of the real-time data of the power grid, and protect the privacy information of individuals and institutions from being leaked, ensure that the privacy rights and interests of users are protected, prevent personal privacy information from being maliciously used, ensure that the real-time data of the power grid is not tampered with or stolen during the transmission process, improve the security and reliability of real-time data transmission of the power grid, and effectively prevent malicious attackers from stealing, tampering with or destroying the real-time data of the power grid, improve data security, and protect data from malicious attacks. By processing the real-time data of the power grid in parallel, multiple data blocks can be processed at the same time, significantly improving the speed and efficiency of data processing, and can complete the analysis and processing of large-scale data faster, reducing the processing time. By parallel processing, computing resources can be more effectively utilized to avoid idle resources.

[0112] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0113] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, supplements, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention.

Claims

1. A cloud-native-based real-time data parallel processing system for power grids, characterized in that: include: The data acquisition module is used to collect real-time data of the power grid, remove interference and enhance the collected real-time data of the power grid, and clean the real-time data of the power grid; A data transmission module, used to perform key generation and data encryption on the transmitted real-time power grid data using homomorphic encryption, generate chain encryption access logs and dynamic permission configuration files, and generate a backup database for real-time power grid data; Cloud-native distributed database, which is used to establish a data storage database based on the cloud-native architecture, store the received real-time power grid data, and correct and complete abnormal real-time power grid data; The parallel processing module is used to construct a parallel processing engine for real-time power grid data, group the data volume of real-time power grid data, and use the parallel processing engine for real-time power grid data to process multiple groups of real-time power grid data in parallel.

2. The cloud-native-based real-time data parallel processing system for power grids according to claim 1, characterized in that: The data acquisition module comprises: Data collection module, used to collect real-time data of power grid; The preprocessing module is used to remove interference and enhance the collected real-time data of the power grid, and to clean the real-time data of the power grid.

3. The cloud-native-based real-time data parallel processing system for power grids according to claim 2, characterized in that: The preprocessing module comprises: Remove communication interference, white noise interference and random pulse interference from the collected real-time data of the power grid; Performing data enhancement on the data after interference removal so that the characteristic parameters in the data after interference removal are enhanced; If there are two column names with the same column name or the same meaning in the real-time data column of the power grid, rename the column name of one of the data columns; If there are duplicate data values ​​in the real-time data column of the power grid, the duplicate data values ​​will be deleted, and the first record of the duplicate data will be retained during the deletion; If there are missing values ​​in the data of the real-time data column of the power grid, statistical methods are used to fill in the missing values ​​in the data; If there are inconsistent data or naming rules in the data column, use the split function to split the data values ​​in the inconsistent data column; Filter and sort the data in the data column, and identify and process outliers in the data.

4. The cloud-native-based real-time data parallel processing system for power grids according to claim 1, characterized in that: The data transmission module comprises: A transmission encryption module is used to encrypt the transmitted real-time data of the power grid using homomorphic encryption and generate chained encryption access logs and dynamic permission profiles; A decryption module, used to obtain a secret key corresponding to the dynamic rights configuration file based on the dynamic rights configuration file, and decrypt the dynamic rights configuration file according to the corresponding secret key; The data backup module is used to back up the uploaded real-time data of the power grid according to the time point of long transmission during the real-time data transmission of the power grid, and generate a backup database.

5. The cloud-native-based real-time data parallel processing system for power grids according to claim 1, characterized in that: The cloud-native distributed database includes: The storage module is used to establish a data storage database based on the cloud-native architecture to store the received real-time power grid data; The safety protection module is used to compare the real-time data of the power grid transmitted to the storage module, and to correct and complete the abnormal real-time data of the power grid according to the comparison result.

6. The cloud-native-based real-time data parallel processing system for power grids according to claim 5, characterized in that: The data defense module includes: A data comparison module is used to compare the real-time data of the power grid transmitted to the storage module with the real-time data of the power grid in the backup database; A data partitioning module, used to transmit the real-time data of the power grid with consistent comparison results to the first buffer pool for storage, and transmit the real-time data of the power grid with inconsistent comparison results to the second buffer pool; The data compensation module is used to correct and complete abnormal values ​​of the real-time data of the power grid in the second cache database.

7. The cloud-native-based real-time data parallel processing system for power grids according to claim 6, characterized in that: The workflow of the data defense module includes: Compare the real-time data of the power grid monitored by the data monitoring module with the real-time data of the power grid in the backup database; If the comparison result is inconsistent, it proves that the real-time data of the power grid has been tampered with or lost, and the data is stored in the second cache pool, and the time point when the real-time data of the power grid has been tampered with or lost is marked; If the results are consistent, it proves that the real-time database of the power grid has not been tampered with or lost, and the data is stored in the first cache pool; Determine the abnormal time point of the abnormal real-time data of the power grid in the second buffer pool, retrieve the corresponding data from the backup data according to the abnormal time point for correction and completion, and transmit the data to the first buffer pool for storage after the correction and completion are completed.

8. The cloud-native-based real-time power grid data parallel processing system according to claim 1, characterized in that: The parallel processing module comprises: The engine construction optimization module is used to construct a power grid real-time data parallel processing engine, and to evaluate and optimize the performance of the constructed power grid real-time data parallel processing engine; A grouping module, used to judge the real-time data of the power grid in the first buffer pool, and group the real-time data of the power grid in the first buffer pool according to the judgment result; The processing module is used to perform parallel processing on the grouped real-time power grid data using the parallel processing engine for real-time power grid data.

9. The cloud-native-based real-time data parallel processing system for power grids according to claim 8, characterized in that: The engine construction optimization module includes: The engine building module is used to build multiple cores based on the Serverless protocol, build a hash map structure in each core, and use the hash map structure to build a real-time data parallel processing engine for the power grid; Performance evaluation module, used to evaluate the performance of the constructed power grid real-time data parallel processing engine, including throughput, response time, and resource utilization; The optimization module is used to optimize the power grid real-time data parallel processing engine according to the performance evaluation result of the power grid real-time data parallel processing engine.

10. A cloud-native-based real-time power grid data parallel processing method, applied in the cloud-native-based real-time power grid data parallel processing system as claimed in claim 9, characterized in that: The following steps are involved: Step 1: Establish a data storage database based on the cloud native architecture, then collect real-time power grid data and pre-process the collected real-time power grid data; Step 2: Transmit the pre-processed real-time power grid data. During the transmission process, homomorphic encryption is used to perform key generation and data encryption on the transmitted real-time power grid data, generate chain encryption access logs and dynamic permission configuration files, and generate a real-time power grid data backup database; Step 3: storing the received real-time grid data and comparing the received real-time grid data with the data in the backup database. When the comparison result passes, the real-time grid data is sent to the first buffer pool to receive the next set of real-time grid data. Step 4: When the comparison result fails, an analysis report corresponding to the comparison result is determined based on the comparison result to determine whether the transmitted real-time power grid data is tampered with or lost, and the data is transmitted to the second cache pool, and then the abnormal point of the transmitted real-time power grid data is determined, and the corresponding data is retrieved from the backup data according to the abnormal point for correction and completion; Step 5: Build a parallel processing engine for real-time power grid data, group the data volume of real-time power grid data, and use the parallel processing engine for real-time power grid data to process multiple groups of real-time power grid data in parallel.

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