Power grid data security management method and system based on block chain technology

Abstract

The invention relates to the technical field of power grid data processing, and particularly discloses a power grid data security management method and system based on a block chain technology, and the method comprises the steps: carrying out the statistics of each storage stack point of a block chain, analyzing the security state evaluation index of each storage stack point, screening a real-time storage stack point and a backup storage stack point, according to the method, the real-time storage stack points and the backup storage stack points can be accurately screened according to the safety parameters and the state indexes of the storage stack points, resources are intelligently allocated according to the operation risk in combination with a dynamic adjustment mechanism, the real-time storage stack points and the backup storage stack points can be accurately screened according to the real-time storage stack points and the backup storage stack points, and the real-time storage stack points and the backup storage stack points can be accurately screened according to the real-time storage stack points and the backup storage stack points. When risks occur, early warning response is conducted in time, the risk of data loss and leakage is reduced, meanwhile, according to the storage priority index, real-time and backup storage requirements are accurately distinguished, storage stack point resources are reasonably allocated, the resources are flexibly adjusted according to storage stack point performance parameters, resource dynamic balance is achieved, and the storage efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid data processing, and specifically to a power grid data security management method and system based on blockchain technology. Background Art

[0002] In the modern power grid environment, with the rapid development of smart grids, power grid data has exploded. The traditional centralized data storage architecture has the risk of single point failure. Once the central server suffers from unexpected events such as network attacks, hardware failures or natural disasters, a large amount of power grid data may be lost or unavailable, which will seriously affect the safe and stable operation of the power grid. The distributed storage characteristics of blockchain enable power grid data to be stored in multiple nodes in a dispersed manner, avoiding the problem of single point failure and significantly improving the availability and reliability of data.

[0003] For example, the invention patent with announcement number CN111046411B announces a method and system for secure storage of power grid data. The method includes: the power grid terminal generates power grid data and constructs encrypted power grid data, and sends the encrypted power grid data to the slave blockchain node; the slave blockchain node receives the encrypted power grid data, and performs a decryption operation on the encrypted power grid data. If the decryption from the slave blockchain node is successful, a distributed aggregate signature operation is performed on the power grid data. If the distributed aggregate signature is successful, the power grid data is sent to the main blockchain node; the main blockchain node stores the power grid data.

[0004] For example, the invention patent with announcement number CN117932673B announces a power grid data management system based on privacy computing, including a data acquisition module, a data analysis module, a node adoption coefficient acquisition module, a node call coefficient acquisition module, an abnormal node marking module and a display module.

[0005] However, in the process of implementing the embodiments of the present application, the present application found that the above technology has at least the following technical problems: The current power grid data management system is not capable of coping with complex data conditions during the operation of the power grid in the actual complex power grid operation environment. Power grid data management faces the challenges of complex data types, huge data volumes, and extremely high requirements for data real-time and reliability. The traditional relatively single and rigid management method is difficult to achieve ideal results in data storage resource optimization, data security, and dynamic data allocation, which may lead to low data storage efficiency, increased risk of data loss or damage, and the system’s inability to adjust data management strategies in a timely manner according to the power grid operation status. These problems reduce the effectiveness and stability of power grid data management. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a power grid data security management method and system based on blockchain technology, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a power grid data security management method based on blockchain technology, comprising: S1: Count the storage points of the blockchain, obtain the security parameters of each storage point, and analyze the security status evaluation indicators of each storage point S2: synchronously collect the status parameters of each storage stack point, combine the security status evaluation indicators of each storage stack point, and screen the real-time storage stack point and the backup storage stack point. S3, obtain the characteristic parameters of each type of power grid data and the historical parameters of each type of power grid data, analyze the data storage priority index of each type of power grid, and then store each type of power grid data in real time through the real-time storage stack point S4, based on the storage priority index of each type of power grid data, determine the power grid data backup tag, thereby performing backup storage of each type of power grid data through the backup storage stack point.

[0008] The second aspect of the present invention provides a power grid data security management system based on blockchain technology, comprising: The storage stack point security assessment module is used to count the storage stack points of the blockchain, obtain the security parameters of each storage stack point, and analyze the security status assessment indicators of each storage stack point.

[0009] The storage stack point screening module is used to synchronously collect the status parameters of each storage stack point, and screen the real-time storage stack points and backup storage stack points in combination with the safety status evaluation indicators of each storage stack point.

[0010] The real-time storage module for power grid data is used to obtain characteristic parameters and historical parameters of various types of power grid data, analyze the storage priority index of various types of power grid data, and thus store various types of power grid data in real time through real-time storage stack points.

[0011] The power grid data backup storage module is used to determine the power grid data backup label based on the storage priority index of each type of power grid data, thereby performing backup storage of various types of power grid data through the backup storage stack point.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides a power grid data security management method and system based on blockchain technology. In terms of data security, the distributed storage characteristics of blockchain are used to avoid single point failures through multi-node backup. Real-time storage stacks and backup storage stacks are accurately screened according to the security parameters and status indicators of storage stacks. In combination with a dynamic adjustment mechanism, resources are intelligently allocated according to operational risks, and early warning responses are given in time when risks occur, thereby reducing the risk of data loss and leakage. In terms of resource optimization, the real-time and backup storage requirements are accurately distinguished according to the storage priority index, storage stack resources are reasonably allocated, and resources are flexibly adjusted according to the performance parameters of the storage stacks to achieve dynamic resource balance and improve storage efficiency.

[0013] (2) The present invention screens real-time storage stack points and backup storage stack points, accurately identifies storage stack points with safe and reliable performance as real-time storage stack points, ensures that high-frequency generated key power grid data can be processed and stored in a low-latency, high-bandwidth environment, and guarantees the timeliness and accuracy of power grid data storage. The screened backup storage stack points provide safe redundancy for massive power grid data, and can quickly restore data in the event of system failure or human error, thereby reducing data loss. At the same time, storage resources are allocated according to data characteristics and importance, avoiding excessive concentration or idle waste of resources, thereby improving the overall storage resource utilization efficiency.

[0014] (3) The present invention performs real-time storage of various types of power grid data and stores them in order according to the storage priority index, ensuring that key information is stored in the appropriate storage stack point in real time, thereby improving the accuracy and response agility of power grid control and effectively reducing fault processing delays. At the same time, the efficient storage mechanism avoids data congestion and loss and ensures data continuity and integrity.

[0015] (4) The present invention performs backup storage of various types of power grid data, sets adaptive backup execution parameters based on the storage priority index, and reduces the risk of data loss and fills the data loss loophole through multi-node and periodic backup. At the same time, it reasonably distinguishes backup requirements, accurately allocates resources, avoids resource waste and excessive redundancy, and improves storage efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0018] Figure 2 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 As shown, the first aspect of the present invention provides a power grid data security management method based on blockchain technology, comprising: S1, count the storage stack points of the blockchain, obtain the security parameters of each storage stack point, and analyze the security status evaluation indicators of each storage stack point.

[0021] In this embodiment, the security status evaluation index of each storage stack point is analyzed, and the specific analysis process is as follows: Obtain the security parameters of each storage stack point, including the total number of historical vulnerabilities, historical vulnerability repair frequency, historical attack times, and key update frequency of each storage stack point.

[0022] It should be noted that the security parameters of each storage stack point are obtained from the program log records of each storage stack point.

[0023] The safety status evaluation index of each storage stack point is obtained according to the analysis and processing of the safety parameters of each storage stack point, and the safety status evaluation index of each storage stack point is used to characterize the safety status of each storage stack point.

[0024] In a specific embodiment, the security status evaluation index of each storage stack point is obtained in the following specific method: in, is the security status evaluation index of the i-th storage stack point, is the total number of historical vulnerabilities at the i-th storage stack point, is the historical vulnerability repair frequency of the i-th storage stack point, is the number of historical attacks on the i-th storage stack point, is the key update frequency of the i-th storage stack point, is the weight of the total number of historical vulnerabilities, is the weight of the historical vulnerability repair frequency, is the weight of the number of historical attacks, is the key update frequency weight, i is the number of each storage stack point, , m is the number of storage stack points, and e is a natural constant.

[0025] It should be noted that the weight of the total number of historical vulnerabilities, the weight of the frequency of historical vulnerability repairs, the weight of the number of historical attacks, and the weight of the key update frequency are all in the range of 0-1. When used, the pre-set values ​​can be directly extracted from the database. In actual application, the pre-set values ​​can be directly retrieved from the database. Taking a certain storage stack point as an example, the total number of historical vulnerabilities, the frequency of historical vulnerability repairs, the number of historical attacks, and the key update frequency of the storage stack point are first collected, and a mapping set is constructed with the corresponding weight of the total number of historical vulnerabilities, the weight of the frequency of historical vulnerability repairs, the weight of the number of historical attacks, and the weight of the key update frequency. During use, it is only necessary to input the total number of historical vulnerabilities, the frequency of historical vulnerability repairs, the number of historical attacks, and the key update frequency of the storage stack point obtained in real time into the pre-set mapping set to extract the corresponding weights.

[0026] It should also be noted that the security status assessment indicators of each storage stack point are obtained based on the analysis and processing of the security parameters of each storage stack point, taking into account the mutual influence between these parameters. For example, when the total number of historical vulnerabilities is large, a higher frequency of historical vulnerability repairs is usually required to reduce security risks. A large total number of historical vulnerabilities will increase the possibility of storage stack points being attacked, thereby increasing the number of historical attacks. A large total number of historical vulnerabilities may indicate that keys need to be updated more frequently. Because vulnerabilities may increase the risk of key leakage, in order to ensure data security, it is necessary to reduce this risk by updating keys. A high frequency of historical vulnerability repairs can reduce the number of historical attacks. A high number of historical attacks usually leads to an increase in the frequency of key updates. Because each attack may pose a risk of key leakage, in order to prevent attackers from using the obtained keys to further access data, keys need to be updated more frequently.

[0027] S2, synchronously collect the status parameters of each storage stack point, combine the safety status evaluation index of each storage stack point, and screen the real-time storage stack point and the backup storage stack point.

[0028] In this embodiment, the real-time storage stack points and the backup storage stack points are screened, and the specific analysis process is as follows: During the preset monitoring period, the status parameters of each storage stack point are collected, including the total number of CPU floating-point operations, the total number of memory reads and writes, the average consumption rate of disk storage capacity, and the maximum network delay of each storage stack point.

[0029] Extract the storage stack point reference status parameters stored in the database, including the reference total number of CPU floating point operations, the reference total number of memory reads and writes, the reference average consumption rate of disk storage capacity, and the reference maximum network delay.

[0030] It should be noted that the total number of CPU floating-point operations refers to the cumulative number of floating-point operations performed by the central processing unit (CPU) during the preset monitoring period. Floating-point operations refer to mathematical operations performed by computers on real numbers with decimal points (i.e. floating-point numbers), including addition, subtraction, multiplication, division, and more complex mathematical function calculations (such as trigonometric functions, logarithmic functions, etc.).

[0031] It should also be noted that the status parameters of each storage stack point can be directly extracted from the system's own performance monitoring tool such as the Sar tool (System Activity Reporter).

[0032] According to the state parameters of each storage stack point and the safety state evaluation index of each storage stack point, the state index of each storage stack point is obtained through analysis and processing, and the state index of each storage stack point is used to characterize the storage performance of each storage stack point.

[0033] In a specific embodiment, the state indicator of each storage stack point is obtained in the following manner: , in, is the state indicator of the i-th storage stack point, is the security status evaluation index of the i-th storage stack point, is the total number of CPU floating-point operations for the i-th storage stack point, is the total number of memory reads and writes at the i-th storage stack point, is the average consumption rate of disk storage capacity at the i-th storage stack point, is the highest network delay of the i-th storage stack point, The total number of CPU floating point operations reference, is the total number of memory read and write references, is the average consumption rate of disk storage capacity. is the highest network reference delay, i is the number of each storage stack point, , m is the number of storage stack points, and e is a natural constant.

[0034] It should be noted that the state indicators of each storage stack point are obtained by analyzing and processing the state parameters of each storage stack point, taking into account the mutual influence between these parameters. For example, a high total number of CPU floating-point operations means that there are many complex computing tasks. In order to support the operation, the intermediate data is frequently read and the results are written, which increases the total number of memory reads and writes. At the same time, the large number of computing result storage requirements and data cache occupancy accelerate the average consumption rate of disk storage capacity. If the disk I / O is busy, the feedback affects the computing efficiency, causing the CPU to wait for data, further increasing the total number of CPU floating-point operations. When the memory is read and written frequently, the memory bandwidth competition is fierce, and the data transmission is delayed due to congestion, which causes the maximum network delay to increase. Conversely, high network delay hinders computing tasks that rely on network data, and the CPU idles or repeatedly waits for data, increasing the computing time and number. The total number of memory reads and writes also increases due to data buffer accumulation and frequent refreshes, and the large backlog of pending data will accelerate disk storage consumption.

[0035] In a specific embodiment, by analyzing the status indicators of each storage stack point, it is possible to accurately determine the performance and safety status of the storage stack point, reasonably screen the real-time and backup storage stack points, optimize the storage resource allocation, improve storage efficiency and management effectiveness, and avoid the risk of data loss or damage due to performance limitations or safety hazards. At the same time, combined with the safety status evaluation indicators, the judgment results can be made more accurate. If the stack point performance is relatively suitable for real-time storage, but the safety status is poor, the stack point will be recorded as backup storage, thereby reducing the risk of data leakage and ensuring the integrity of power grid data.

[0036] Extract the storage stack point status verification indicators preset in the database.

[0037] It should be noted that the storage stack point status verification index is a critical index pre-set in the database and used to determine the storage function of the storage stack point.

[0038] If the status indicator of a storage stack point is greater than the storage stack point status verification indicator, the storage stack point is recorded as a real-time storage stack point. If the status indicator of a storage stack point is less than or equal to the storage stack point status verification indicator, the storage stack point is recorded as a backup storage stack point. The real-time storage stack points and backup storage stack points are thus screened out.

[0039] If the status index of a storage stack point is greater than the storage stack point status verification index, it means that the storage stack point has strong comprehensive performance and can handle complex power grid data calculation tasks, ensure the efficiency of real-time data processing, and ensure that data can be transmitted and interacted in a timely manner. Therefore, such a storage stack point is suitable as a real-time storage stack point and can support the real-time storage and rapid processing requirements of power grid data.

[0040] If the status indicator of a storage stack point is less than or equal to the storage stack point status verification indicator, it means that the storage stack point is relatively weak in comprehensive performance and cannot bear the complex calculation of large-scale real-time data. The data interaction speed is slow and the efficiency is poor when dealing with high-frequency and large-scale real-time data writing. In order to avoid waste of resources, such stack points are used for backup storage, so that they can play a role in data backup scenarios, safely save copies of power grid data, provide protection for data integrity, realize data recovery when system failure or data loss risk occurs, and ensure the security and integrity of power grid data.

[0041] S3, acquiring characteristic parameters of various types of power grid data and historical parameters of various types of power grid data, analyzing storage priority indexes of various types of power grid data, and thereby performing real-time storage of various types of power grid data through real-time storage stack points.

[0042] In this embodiment, characteristic parameters of various types of power grid data and historical parameters of various types of power grid data are obtained, where: The characteristic parameters of each type of power grid data include the average data generation frequency, the total number of data bytes, the average data update period, and the data fluctuation frequency of each type of power grid data.

[0043] The historical parameters of each type of power grid data include the historical access frequency, historical modification times and historical data completeness rate of each type of power grid data.

[0044] It should be noted that the characteristic parameters of various types of power grid data can be directly extracted from the system's own performance monitoring tools such as the Sar tool (System Activity Reporter).

[0045] The historical parameters of various types of power grid data are obtained from the program log records of each storage stack point.

[0046] In this embodiment, real-time storage of various types of power grid data is performed through real-time storage stack points, and the specific analysis process is as follows: Extract reference data characteristic parameters and reference data historical parameters stored in the database.

[0047] The reference data characteristic parameters include the average generation frequency of the reference data, the total number of bytes of the reference data, the average update period of the reference data, and the fluctuation frequency of the reference data.

[0048] The historical parameters of reference data include the historical access frequency of reference data, the historical modification times of reference data and the historical data completeness rate of reference data.

[0049] According to characteristic parameters of each type of power grid data, historical parameters of each type of power grid data, characteristic parameters of reference data and historical parameters of reference data, analysis and processing are performed to obtain storage priority indexes of each type of power grid data, wherein the storage priority indexes of each type of power grid data are used to characterize the storage priority of each type of power grid data.

[0050] In a specific embodiment, the storage priority index of each type of power grid data is obtained by the following method: , in, is the storage priority index of the j-th type of power grid data, is the average generation frequency of the j-th type of power grid data, is the total number of bytes of data of the j-th type of power grid data, is the average update period of the j-th type of power grid data, is the data fluctuation frequency of the j-th type of power grid data, is the historical frequency of visits to the j-th type of power grid data, is the number of historical modifications of the j-th type of power grid data, is the historical data completeness rate of the j-th type of power grid data, is the average generation frequency of the reference data, is the total number of bytes of reference data, is the average update period of reference data, is the reference data fluctuation frequency, For reference data, historical interview frequency, is the number of historical modifications of the reference data, is the completeness rate of historical data of reference data, j is the number of power grid data type, , n is the number of power grid data types, and e is a natural constant.

[0051] It should be noted that the priority index of data storage for each type of power grid is obtained by analyzing and processing the characteristic parameters of each type of power grid data and the historical parameters of each type of power grid data. This is based on the mutual influence between these parameters. For example, if the average data generation frequency increases, the total number of data bytes will accumulate rapidly over time, and the average data update cycle will be shortened accordingly. In order to maintain the timeliness of the data, new data quickly replaces old data. High-frequency generation is easy to capture more details of power grid operation changes, resulting in frequent data fluctuations. The increase in the total number of data bytes affects the reading and updating speed to a certain extent. When the average data update cycle is extended, the average data generation frequency is relatively reduced, which means that less new data is generated per unit time, and the growth of the total number of data bytes slows down. The frequency of historical interviews increases, and the number of historical modifications increases due to interviewees discovering data errors, deficiencies, or improvements based on feedback. For example, users frequently question the electricity bill data, prompting correction of data errors, optimization of billing algorithms, and increase in the number of modifications. An increase in the number of historical modifications may increase the total number of data bytes due to modification operation records, modified data version management, or supplementary correction of data content. The historical access frequency may attract more attention and visits due to the improvement of data quality after modification. The high integrity rate of historical data is the basis of data reliability, which is conducive to maintaining the stability of the average data generation frequency. The historical access frequency is positively affected by the integrity rate. Complete and reliable data attracts more user departments to visit and query.

[0052] Based on the storage priority index of each type of power grid data, each type of power grid data is stored in the real-time storage stack in order from high to low storage priority index.

[0053] In a power grid data management scenario, there are many types of power grid data. For example, the voltage and current data monitored in real time have a very high average data generation frequency, with a large amount of data generated every second and a fast data fluctuation frequency, reflecting changes in the power grid operation status at any time. There is also historical fault data. Although the total number of data bytes is large, the average data update cycle is long and is only updated after a fault occurs or is repaired.

[0054] Storage arrangements are made based on the storage priority index of various types of power grid data. For example, the real-time monitored voltage and current data are crucial to the real-time regulation of the power grid, so their storage priority index is greater than the historical fault data storage priority index. When storing, the real-time monitored voltage and current data are preferentially transmitted to the real-time storage stack. Its high-speed CPU operation and frequent memory reading and writing ensure that the data can be received, processed and stored instantly, and then the historical fault data is transmitted. This transmission method can ensure that important real-time data always occupies a priority storage position, ensure orderly and efficient power grid data storage management, and optimize data resource allocation and utilization efficiency.

[0055] It should be noted that, in a specific embodiment, the status indicator corresponding to each real-time storage stack point is extracted and recorded as the status indicator of each real-time storage stack point, and the status indicators of each real-time storage stack point are sorted in sequence according to their sizes, thereby determining the storage priority of the real-time storage stack point.

[0056] In a specific embodiment, when a certain type of power grid data is stored in the real-time storage stack, the power grid data of this type needs to be stored in the real-time storage stack in sequence according to the storage priority of the real-time storage stack.

[0057] S4, based on the storage priority index of each type of power grid data, determine the power grid data backup tag, thereby performing backup storage of each type of power grid data through the backup storage stack point.

[0058] In this embodiment, backup storage of various types of power grid data is performed through the backup storage stack, and the specific analysis process is as follows: Extract the power grid data backup storage verification indicators preset in the database.

[0059] If a certain type of power grid data storage priority index is less than the power grid data backup storage verification index, the power grid data backup tag of this type of power grid data is recorded as no need for backup.

[0060] If a certain type of power grid data storage priority index is greater than or equal to the power grid data backup storage verification index, the power grid data backup tag of this type of power grid data is recorded as demand backup.

[0061] If the storage priority index of a certain type of power grid data is less than the power grid data backup storage verification index, it means that the importance, timeliness or criticality of this type of power grid data in power grid operation analysis and fault response is relatively low. The data may be updated slowly and have little impact on the current operation of the power grid, such as some long-standing and rarely consulted historical draft data of power grid planning, or the accuracy and integrity of the data have been fully guaranteed through other channels or methods, and no additional backup resources are required. Backing up such data will not significantly improve the overall value of power grid data management, but may consume unnecessary storage costs and management efforts. Therefore, it is marked as not requiring backup, so as to accurately configure backup resources, focus on core key data, and improve the effectiveness and efficiency of power grid data backup management.

[0062] If the storage priority index of a certain type of power grid data is greater than or equal to the power grid data backup storage verification index, it means that this type of power grid data is highly critical in the sustained stability of power grid operation, fault diagnosis, performance optimization and other aspects. Such data needs to be invested in backup resources to prevent data risks and ensure the security and effectiveness of key power grid data.

[0063] Extract and count various types of power grid data with the power grid data backup label as demand backup, record them as demand backup power grid data, and simultaneously extract the storage priority index corresponding to each demand backup power grid data, record them as each demand backup power grid data storage priority index.

[0064] The backup execution parameters corresponding to each power grid data storage priority index interval stored in the database are extracted, and the backup execution parameters corresponding to the interval where each required backup power grid data storage priority index is located are mapped and extracted, and recorded as the backup execution parameters of each required backup power grid data.

[0065] It should be noted that the backup execution parameters refer to the backup period and the number of backup nodes required to back up the power grid data.

[0066] The larger the storage priority index of the power grid data to be backed up, the more important the data is, and the more short-cycle multi-node backup is needed. By determining the backup execution parameters according to the size of the storage priority index of the power grid data to be backed up, the risk of data loss can be greatly reduced and the reliability and stability of the power grid data security management system can be improved.

[0067] The backup execution parameters of the power grid data are backed up according to various requirements, and backup storage of various types of power grid data is performed through the backup storage stack.

[0068] In a specific embodiment, taking a large regional power grid as an example, the storage priority index of the real-time monitored operating parameters of the key equipment of the substation is in the high index range, and the backup execution parameters of the corresponding database for this interval are set to backup every 15 minutes, and the backup storage stacks are deployed in 5 different locations. Such short-cycle multi-node backup ensures that data can still be quickly restored in extreme cases (such as local network failures, natural disasters that cause damage to some stacks), ensuring that power grid dispatchers can grasp the status of equipment in real time and avoid the expansion of power grid failures due to data loss. For the annual power grid electricity statistics and analysis data, the storage priority index is in the low index range, the backup cycle is set to once a month, and the backup nodes are 2. This backup strategy allocates resources according to the importance of data, which not only reduces the risk of key data loss, improves the reliability and stability of the overall system, but also saves storage resources and achieves a balance between power grid data security and benefits.

[0069] In this embodiment, the power grid data change parameters and network performance parameters of each storage stack point are obtained, the adjustment requirement label of each storage stack point is determined, and corresponding data security management is performed on each storage stack point. The specific analysis process of determining the adjustment requirement label of each storage stack point is as follows: The adjustment demand labels of each storage stack point include no adjustment required, dynamic demand adjustment and early warning demand adjustment.

[0070] In a preset monitoring cycle, the power grid data change parameters and network performance parameters of each storage stack point are obtained, wherein: the power grid data change parameters of each storage stack point include the data capacity extreme value difference and the data read and write frequency change of each storage stack point.

[0071] Network performance parameters include storage capacity utilization and network bandwidth occupancy of each storage stack point.

[0072] The storage stack point reference data stored in the database is extracted, and the storage stack point reference data includes reference data capacity extreme value difference, reference data read and write frequency change, reference storage capacity utilization rate and reference network bandwidth occupancy rate.

[0073] According to the power grid data change parameters and network performance parameters of each storage stack point, the regulation demand indicator parameters of each storage stack point are analyzed and processed, and the regulation demand indicator parameters of each storage stack point are used to characterize the demand regulation degree of each storage stack point.

[0074] In a specific embodiment, the adjustment demand indicator parameters of each storage stack point are obtained in the following manner: , in, is the parameter indicating the adjustment requirement of the i-th storage stack point, is the data capacity extreme value difference of the i-th storage stack point, is the change in the data read and write frequency of the i-th storage stack point, is the storage capacity utilization of the i-th storage stack point, is the network bandwidth occupancy rate of the i-th storage stack point, is the reference data capacity extreme value difference, is the change in the reference data reading and writing frequency, For reference storage capacity utilization, For reference network bandwidth utilization, , m is the number of storage stack points, and e is a natural constant.

[0075] It is important to understand that the switch function is a built-in function in Python. .

[0076] It should be noted that the adjustment demand indicator parameters of each storage stack point are obtained by analyzing and processing according to the power grid data change parameters and network performance parameters of each storage stack point, taking into account the mutual influence between these parameters. For example, when the data capacity extreme value difference increases, that is, the storage volume fluctuation intensifies, if it approaches the storage upper limit, it will trigger frequent data migration and sorting operations, resulting in an increase in the change in data read and write frequency, a large number of read and write requests emerge in a concentrated manner, causing a surge in network bandwidth occupancy, and causing transmission congestion and increased delay. At the same time, excessive storage capacity utilization further compresses storage stack point performance redundancy and slows down read and write responses. Conversely, when the data read and write frequency change increases dramatically, the rapid read and write demand causes frequent cache replacement and data fragmentation, expands the range of data capacity extreme value difference, and continuously consumes network bandwidth, making the occupancy rate high. The saturation of network bandwidth occupancy will greatly prolong the data transmission time, block data inflow and outflow, cause abnormal fluctuations in storage capacity utilization, and increase the data capacity extreme value difference.

[0077] Extract the first threshold value of the adjustment requirement and the second threshold value of the adjustment requirement preset in the database.

[0078] If the adjustment requirement indicator parameter of a storage stack point is less than the first adjustment requirement threshold, the adjustment requirement label of the storage stack point is recorded as no adjustment is required.

[0079] If the regulation demand indicator parameter of a storage stack point is less than the first regulation demand threshold, it means that the performance indicators of the storage stack point are stable under the current power grid data operation environment, the data capacity extreme value difference is kept in a very small range, the data read and write frequency changes smoothly, the storage capacity utilization is reasonable, and the network bandwidth occupancy is moderate. Its resource allocation is highly adapted to the task load, and the system runs smoothly without optimization.

[0080] If the adjustment requirement indicator parameter of a storage stack point is greater than or equal to the first adjustment requirement threshold and less than or equal to the second adjustment requirement threshold, the adjustment requirement tag of the storage stack point is recorded as demand dynamic adjustment.

[0081] If the regulation demand indicator parameter of a storage stack point is greater than or equal to the first regulation demand threshold and less than or equal to the second regulation demand threshold, it means that this storage stack point has shown signs of performance fluctuation. Although it can maintain basic functions, it needs dynamic adjustment to achieve accurate adaptation of resources to dynamic loads, ensure the continuous and stable flow and efficient processing of power grid data, enhance the system's adaptability to gradual changes in the operating environment, and improve the dynamic balance capability of power grid data management.

[0082] If the adjustment demand indicator parameter of a storage stack point is greater than the second adjustment demand threshold, the adjustment demand label of the storage stack point is recorded as demand warning adjustment.

[0083] If the regulation demand indicator parameter of a storage stack point is greater than the second threshold of regulation demand, it means that the storage stack point may face a greater operational risk. Due to major power grid failures, large-scale system upgrades or extreme business peaks, the data capacity extreme value difference may expand sharply, the read and write frequency may suddenly become out of control, the storage capacity may be full, and the network bandwidth may be seriously blocked. A high-intensity early warning is required to prompt operators to carry out subsequent processing to ensure the stable operation of the power grid data security management system.

[0084] In this embodiment, data security management is performed on each storage stack point accordingly, and the specific analysis process is as follows: The storage stack points for dynamic demand adjustment are extracted and counted, recorded as demand adjustment storage stack points, and the adjustment demand indicator parameters corresponding to the demand adjustment storage stack points are simultaneously extracted, recorded as the adjustment demand index of the demand adjustment storage stack points.

[0085] The number of additional demand stack points corresponding to each adjustment demand index preset in the database is extracted, and the number of additional demand stack points corresponding to the interval in which the adjustment demand index of each demand adjustment storage stack point is located is mapped and extracted, and recorded as the number of additional demand stack points of each demand adjustment storage stack point.

[0086] The greater the adjustment demand index of the demand adjustment storage stack point, the higher the demand adjustment degree of the storage stack point, and the more corresponding demand-added stack points are required. By determining the number of demand-added stack points for each demand adjustment storage stack point according to the size of the adjustment demand index of each demand adjustment storage stack point, accurate configuration of power grid data storage resources can be achieved, waste of resources can be avoided, and the scientificity and effectiveness of power grid data management can be improved.

[0087] Based on the demand of each demand-adjusted storage stack point, the number of stack points is increased to increase the number of storage stack points of each demand-adjusted storage stack point, thereby achieving the adjustment of each demand-adjusted storage stack point.

[0088] In a specific embodiment, the adjustment demand index of a demand adjustment storage stack point corresponds to the number of stack points required to be increased in the database is 3. Based on this, the system automatically adds 3 stack points to continue real-time data storage, ensuring efficient processing of real-time monitoring data. This avoids the risk of data delay and loss caused by storage bottlenecks and improves the stability of the system.

[0089] The storage stack points of demand warning adjustment are extracted and counted, recorded as demand warning adjustment storage stack points, thereby generating warning information for warning processing.

[0090] In a specific embodiment, for example, storage stack point X is determined as a demand warning adjustment storage stack point, and the corresponding warning information generated may be "Storage stack point X has a high risk of data overload, and data loss may occur, please pay attention."

[0091] Reference Figure 2As shown, the second aspect of the present invention provides a power grid data security management system based on blockchain technology, including: The storage stack point security assessment module is used to count the storage stack points of the blockchain, obtain the security parameters of each storage stack point, and analyze the security status assessment indicators of each storage stack point.

[0092] The storage stack point screening module is used to synchronously collect the status parameters of each storage stack point, and screen the real-time storage stack points and backup storage stack points in combination with the safety status evaluation indicators of each storage stack point.

[0093] The real-time storage module for power grid data is used to obtain characteristic parameters and historical parameters of various types of power grid data, analyze the storage priority index of various types of power grid data, and thus store various types of power grid data in real time through real-time storage stack points.

[0094] The power grid data backup storage module is used to determine the power grid data backup label based on the storage priority index of each type of power grid data, thereby performing backup storage of various types of power grid data through the backup storage stack point.

[0095] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A power grid data security management method based on blockchain technology, characterized in that: include: S1, count the storage points of the blockchain, obtain the security parameters of each storage point, and analyze the security status evaluation indicators of each storage point; S2, synchronously collect the status parameters of each storage stack point, combine the security status evaluation index of each storage stack point, and screen the real-time storage stack point and the backup storage stack point; S3, acquiring characteristic parameters of various types of power grid data and historical parameters of various types of power grid data, analyzing data storage priority indexes of various types of power grids, and thereby performing real-time storage of various types of power grid data through real-time storage stack points; S4, based on the storage priority index of each type of power grid data, determine the power grid data backup tag, thereby performing backup storage of each type of power grid data through the backup storage stack point.

2. The power grid data security management method based on blockchain technology according to claim 1 is characterized by: The specific analysis process of analyzing the security status evaluation index of each storage stack point is as follows: Obtain security parameters of each storage stack point, including the total number of historical vulnerabilities, historical vulnerability repair frequency, historical attack times, and key update frequency of each storage stack point; The safety status evaluation index of each storage stack point is obtained according to the analysis and processing of the safety parameters of each storage stack point, and the safety status evaluation index of each storage stack point is used to characterize the safety status of each storage stack point.

3. The power grid data security management method based on blockchain technology according to claim 2 is characterized by: The specific analysis process of screening the real-time storage stack points and the backup storage stack points is as follows: During the preset monitoring period, the status parameters of each storage stack point are collected, including the total number of CPU floating-point operations, the total number of memory reads and writes, the average consumption rate of disk storage capacity, and the maximum network delay of each storage stack point; Analyze and process the state parameters of each storage stack point and the safety state evaluation index of each storage stack point to obtain the state index of each storage stack point, wherein the state index of each storage stack point is used to characterize the storage performance of each storage stack point; Extracting the storage stack point status verification index preset in the database; If the status indicator of a storage stack point is greater than the storage stack point status verification indicator, the storage stack point is recorded as a real-time storage stack point. If the status indicator of a storage stack point is less than or equal to the storage stack point status verification indicator, the storage stack point is recorded as a backup storage stack point. The real-time storage stack points and backup storage stack points are thus screened out.

4. The power grid data security management method based on blockchain technology according to claim 1 is characterized by: The real-time storage of various types of power grid data is performed through the real-time storage stack, and the specific analysis process is as follows: Obtain characteristic parameters of various types of power grid data and historical parameters of various types of power grid data; Based on characteristic parameters of each type of power grid data and historical parameters of each type of power grid data, analyzing and processing to obtain a data storage priority index for each type of power grid, wherein the data storage priority index for each type of power grid is used to characterize the storage priority of each type of power grid data; Based on the storage priority index of each type of power grid data, each type of power grid data is stored in the real-time storage stack in order from high to low storage priority index.

5. The method for secure management of power grid data based on blockchain technology according to claim 4 is characterized in that: The specific analysis process of the data storage priority index of each type of power grid is as follows: The characteristic parameters of each type of power grid data include the average data generation frequency, total number of data bytes, average data update cycle, and data fluctuation frequency of each type of power grid data; The historical parameters of each type of power grid data include the historical access frequency, historical modification times and historical data integrity rate of each type of power grid data; Extracting reference data characteristic parameters and reference data historical parameters stored in a database; The reference data characteristic parameters include the average generation frequency of reference data, the total number of reference data bytes, the average update period of reference data, and the fluctuation frequency of reference data; The historical parameters of the reference data include the historical access frequency of the reference data, the historical modification times of the reference data, and the historical data completeness rate of the reference data; According to the characteristic parameters of each type of power grid data, the historical parameters of each type of power grid data, the characteristic parameters of the reference data and the historical parameters of the reference data, the storage priority index of each type of power grid data is obtained through analysis and processing.

6. The method for secure management of power grid data based on blockchain technology according to claim 5 is characterized in that: The backup storage of various types of power grid data is performed through the backup storage stack, and the specific analysis process is as follows: Extract the power grid data backup storage verification indicators preset in the database; If a certain type of power grid data storage priority index is less than the power grid data backup storage verification index, the power grid data backup tag of this type of power grid data is recorded as no need for backup; If a certain type of power grid data storage priority index is greater than or equal to the power grid data backup storage verification index, the power grid data backup tag of this type of power grid data is recorded as demand backup; Extract and count various types of power grid data with a power grid data backup tag of demand backup, record them as each demand backup power grid data, and simultaneously extract the storage priority index corresponding to each demand backup power grid data, record them as each demand backup power grid data storage priority index; Extract the backup execution parameters corresponding to the intervals of the priority indexes of each power grid data storage stored in the database, and map and extract the backup execution parameters corresponding to the intervals of the priority indexes of each required backup power grid data storage, and record them as the backup execution parameters of each required backup power grid data; The backup execution parameters of the power grid data are backed up according to various requirements, and backup storage of various types of power grid data is performed through the backup storage stack.

7. The power grid data security management method based on blockchain technology according to claim 5 is characterized by: The specific method for obtaining the data storage priority index of each type of power grid is as follows: ， in, is the storage priority index of the j-th type of power grid data, is the average generation frequency of the j-th type of power grid data, is the total number of bytes of data of the j-th type of power grid data, is the average update period of the j-th type of power grid data, is the data fluctuation frequency of the j-th type of power grid data, is the historical frequency of visits to the j-th type of power grid data, is the number of historical modifications of the j-th type of power grid data, is the historical data completeness rate of the j-th type of power grid data, is the average generation frequency of the reference data, is the total number of bytes of reference data, is the average update period of reference data, is the reference data fluctuation frequency, For reference data, historical interview frequency, is the number of historical modifications of the reference data, is the completeness rate of historical data of reference data, j is the number of power grid data type, , n is the number of power grid data types, and e is a natural constant.

8. The power grid data security management method based on blockchain technology according to claim 1 is characterized by: It also includes obtaining the power grid data change parameters and network performance parameters of each storage stack point, determining the adjustment requirement label of each storage stack point, and thus performing corresponding data security management on each storage stack point. Among them, the specific analysis process of determining the adjustment requirement label of each storage stack point is as follows: The adjustment demand labels of each storage stack point include no adjustment, dynamic demand adjustment and early warning demand adjustment; According to the power grid data change parameters and network performance parameters of each storage stack point, the regulation demand indicator parameters of each storage stack point are analyzed and processed, and the regulation demand indicator parameters of each storage stack point are used to characterize the demand regulation degree of each storage stack point; Extracting a first threshold value of adjustment demand and a second threshold value of adjustment demand preset in a database; If the adjustment requirement indicator parameter of a storage stack point is less than the first adjustment requirement threshold, the adjustment requirement label of the storage stack point is recorded as no adjustment is required; If the regulation requirement indicator parameter of a storage stack point is greater than or equal to the first regulation requirement threshold and less than or equal to the second regulation requirement threshold, the regulation requirement tag of the storage stack point is recorded as demand dynamic regulation; If the adjustment demand indicator parameter of a storage stack point is greater than the second adjustment demand threshold, the adjustment demand label of the storage stack point is recorded as demand warning adjustment.

9. According to the blockchain technology-based power grid data security management system of claim 1, it is characterized by: The data security management is performed accordingly on each storage stack point, and the specific analysis process is as follows: Extract and count the storage stack points of dynamic demand adjustment, record them as demand adjustment storage stack points, and simultaneously extract the adjustment demand indicator parameters corresponding to each demand adjustment storage stack point, record them as the adjustment demand index of each demand adjustment storage stack point; Extract the number of additional demand stack points corresponding to each adjustment demand index preset in the database, and map and extract the number of additional demand stack points corresponding to the interval where the adjustment demand index of each demand adjustment storage stack point is located, and record it as the number of additional demand stack points of each demand adjustment storage stack point; Based on the demand of each demand-adjusting storage stack point, the number of stack points is increased to increase the number of storage stack points of each demand-adjusting storage stack point, thereby realizing the adjustment of each demand-adjusting storage stack point; The storage stack points of demand warning adjustment are extracted and counted, recorded as demand warning adjustment storage stack points, thereby generating warning information for warning processing.

10. A system using the power grid data security management method based on blockchain technology as described in any one of claims 1 to 9, characterized in that: include: The storage stack point security assessment module is used to count the storage stack points of the blockchain, obtain the security parameters of each storage stack point, and analyze the security status assessment indicators of each storage stack point; The storage stack point screening module is used to synchronously collect the status parameters of each storage stack point, and screen the real-time storage stack points and backup storage stack points in combination with the safety status evaluation indicators of each storage stack point; A real-time power grid data storage module, which is used to obtain characteristic parameters of various types of power grid data and historical parameters of various types of power grid data, analyze the storage priority index of various types of power grid data, and thus perform real-time storage of various types of power grid data through real-time storage stack points; The power grid data backup storage module is used to determine the power grid data backup label based on the storage priority index of each type of power grid data, thereby performing backup storage of various types of power grid data through the backup storage stack point.

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