Distributed storage and processing system for power grid data

Abstract

The invention is suitable for the technical field of data storage and processing, and provides a distributed storage and processing system for power grid data, and the system comprises a distributed storage unit, a dual-heat-resistant-standby deployment unit, a central management and control unit, and a terminal. The distributed storage unit is connected with the double-heat-standby-resistant deployment unit, and the central management and control unit is connected with the double-heat-standby-resistant deployment unit and the terminal. The distributed storage unit is used for collecting power grid data, performing data preprocessing and realizing transmission and sharing of the data among distributed nodes; the dual-heat-backup-resistant deployment unit is used for performing one-to-one data backup and transmitting backup data in a wired or wireless mode; the central management and control unit is used for data extraction and fault statistics, planning a data sharing range and deploying the double-heat-standby-resistant unit according to a fault node; and the terminal is used for troubleshooting according to the alarm information sent by the central management and control unit. According to the invention, stable operation of the system is guaranteed, and the efficiency and quality of power grid data storage and processing are improved at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage and processing, and in particular to a distributed storage and processing system for power grid data. Background Art

[0002] With the rapid development of information technology, especially driven by big data, cloud computing and the Internet of Things, distributed storage systems have become a key technology for solving large-scale data storage and management problems. Distributed storage improves the reliability, availability and access efficiency of the system by storing data on multiple independent devices, and is also easy to expand.

[0003] In the prior art, the centralized data storage and processing currently used have some technical defects. In terms of data processing efficiency, with the sharp increase in the amount of data, the centralized system is prone to hardware resource limitations, which makes it difficult for the processing capacity to match the data growth rate, making the overall processing speed very slow. In terms of stability, since all data is concentrated in one place, once the storage center fails, such as hardware damage, software vulnerabilities, or encounters a network attack, the entire system may be paralyzed, and the risk of data loss and damage is very high. In addition, the scalability of the centralized system is poor. When it is necessary to expand storage capacity or processing power, hardware upgrades and architecture adjustments are complex and costly, making it difficult to flexibly respond to the data growth needs brought about by business development. Moreover, data transmission under the centralized architecture is prone to bottlenecks. The entry and exit of a large amount of data in the central node will cause network congestion, further reducing the timeliness of data processing and the operating efficiency of the system.

[0004] In view of this, a distributed storage and processing system for power grid data is needed. Summary of the invention

[0005] The embodiment of the present application provides a distributed storage and processing system for power grid data, which is used to solve the problem of low efficiency and quality of centralized data storage and processing.

[0006] A first aspect of an embodiment of the present application provides a distributed storage and processing system for power grid data, including:

[0007] Distributed storage units, dual-resistance hot standby deployment units, central control units, and terminals;

[0008] The distributed storage unit is connected to the dual-resistance hot standby deployment unit, and the central control unit is connected to the dual-resistance hot standby deployment unit and the terminal respectively;

[0009] The distributed storage unit is used to collect power grid data and perform data preprocessing, and realize data transmission and sharing among various distributed nodes;

[0010] The dual-resistance hot standby deployment unit is used to perform one-to-one data backup and transmit the backup data in a wired or wireless manner;

[0011] The central control unit is used for data extraction and fault statistics, planning data sharing scope, and deploying dual-resistance hot standby units according to faulty nodes;

[0012] The terminal is used to perform troubleshooting based on the alarm information sent by the central control unit.

[0013] Furthermore, the distributed storage unit is used to collect power grid data and perform data preprocessing, and realize data transmission and sharing among various distributed nodes, including:

[0014] The distributed storage unit includes several data storage node modules and data transmission path modules. The several data storage node modules are used to collect power grid data of equipment or substations and pre-process the data. The data transmission path module is used to realize the transmission of power grid data between nodes and data sharing.

[0015] Furthermore, the dual-resistance hot standby deployment unit is used to perform one-to-one data backup and transmit the backup data in a wired or wireless manner, including:

[0016] The installation position of the dual-resistance hot standby deployment unit is set by the central control unit according to the target number of failures of the plurality of data storage node modules, where the target number of failures is the number of failures that meet a preset threshold condition.

[0017] Furthermore, the central control unit is used for data extraction and fault statistics, planning data sharing scope, and deploying dual-resistance hot standby units according to faulty nodes, including:

[0018] The central control unit includes a data extraction module, a data sharing planning module and a dual-antibody hot standby setting module. The data extraction module is used to store the data extraction path, the data sharing planning module is used to plan the data sharing scope between the several data storage node modules, and the dual-antibody hot standby setting module is used to collect the number of failures of the several data storage node modules and deploy the dual-antibody hot standby deployment unit according to the collected number of failures.

[0019] Furthermore, the data extraction module is used to store the data extraction path, including:

[0020] The data extraction module stores data extraction paths according to a set priority, wherein the priority of the paths of the several data storage node modules themselves is greater than the path of the dual-resistance hot standby deployment unit, and the priority of the path of the dual-resistance hot standby deployment unit is greater than the path of other data storage node modules shared with the several data storage node modules.

[0021] Furthermore, the data sharing planning module is used to plan the data sharing scope between the plurality of data storage node modules, including:

[0022] Construct the objective function with cost coefficient and data security coefficient as parameters;

[0023] Determining constraints of the objective function, wherein the constraints include a minimum acceptable level of safety assurance, a maximum acceptable level of cost, and parameter constraints;

[0024] The target data sharing range among the plurality of data storage node modules is determined according to the objective function and the constraint conditions.

[0025] Furthermore, the expression of the objective function is as follows:

[0026] Minimizeh(S,C)=ω 1 ·(1―S)+ω 2 ·C

[0027] Where: h(S,C) is a comprehensive calculation function based on data security factor and cost factor, S is the normalized data security factor, C is the normalized cost factor, ω 1 and ω 2 are the corresponding weights respectively.

[0028] Furthermore, the expression of the data safety factor S is as follows:

[0029] S=k 1 Q 1 +k 2 Q 2 +k 3 Q 3

[0030] Where: Q 1 is the number of child nodes shared with the parent node, Q 2 Q is the maximum value of the edges connecting the parent node and all child nodes when the parent node and child nodes share data. 3 k is the coefficient of whether the mother node has a dual-resistance hot standby deployment unit. If it exists, it is 1; if it does not exist, it is 0. 1 , k 2 and k 3 is the corresponding weight coefficient;

[0031] The expression of the cost coefficient C is as follows:

[0032] C=ρ 1 Q 1 ·X 1 +ρ2 Q 2 ·X 2 +ρ 3 Q 3 ·X 3

[0033] Where: X 1 is the storage cost of a child node, X 2 is the cost of a data transmission path module, X 3 is the deployment cost of a dual-resistance hot standby deployment unit, ρ 1 , 2 and ρ 3 are the corresponding weight coefficients respectively.

[0034] Furthermore, the constraint conditions of the objective function are determined, wherein the constraint conditions include a minimum acceptable level of safety assurance, a maximum acceptable level of cost, and parameter constraints, including:

[0035] Minimum acceptable level of safety assurance:

[0036] S≥S min

[0037] Where: S min The minimum acceptable level of safety assurance;

[0038] Maximum acceptable level of cost:

[0039] C≤C max

[0040] Where: C max is the maximum acceptable level of cost;

[0041] Parameter constraints: Q 1 , Q 2 , Q 3 , X 1 , X 2 and X 3 Both are greater than or equal to 0.

[0042] Furthermore, the terminal is used to perform troubleshooting according to the alarm information sent by the central control unit, including:

[0043] The terminal includes an execution terminal and a management terminal. The execution terminal is used to receive the alarm information sent by the central control unit and perform troubleshooting. The management terminal is used to collect the number of faults recorded by the central control unit and the number of faults confirmed by the execution terminal, and perform focused troubleshooting based on the collected number of faults.

[0044] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0045] The system in the present invention is composed of a distributed storage unit, a dual-resistance hot standby deployment unit, a central control unit and a terminal. The distributed storage unit is connected to the dual-resistance hot standby deployment unit, and the central control unit is connected to the dual-resistance hot standby deployment unit and the terminal respectively. The distributed storage unit collects and pre-processes the power grid data to realize the sharing of data among distributed nodes, providing rich resources for data processing and application; the dual-resistance hot standby deployment unit ensures the security and reliability of data through one-to-one backup and wired or wireless transmission, and reduces the risk of data loss due to data storage node failure; the central control unit can accurately extract data, count fault conditions, reasonably plan the data sharing scope, and deploy dual-resistance hot standby units for fault-prone nodes according to the fault number threshold, effectively improving the stability of the system and data management capabilities; the terminal quickly responds to the alarm information of the central control unit to perform troubleshooting, ensuring that the system can be repaired in time when problems occur, thereby ensuring the continuous and stable operation of the entire system and improving the overall efficiency and quality of power grid data storage and processing.

[0046] Other advantages, objectives, and features of the present invention will be set forth in part in the following description, and in part will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the structure of an embodiment of a distributed storage and processing system for power grid data in the present invention;

[0048] Figure 2 The figure is a flow chart of an embodiment of a distributed storage and processing system for power grid data in the present invention. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0050] Embodiment 1

[0051] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0052] Hot standby is a high availability technology that establishes a redundant, real-time synchronized backup system between network devices to achieve seamless switching to a backup device when the primary device fails. This technology usually consists of two or more identically configured devices, one of which is active (primary) and the other is on standby (backup). The backup device synchronizes data and status information in real time so that it can immediately take over the operation of network services when the primary device fails. Hot standby is widely used in routers, switches, firewalls, security devices, servers, and storage systems to provide high availability and reliability.

[0053] The working principle of dual-machine hot standby is to establish a redundant, real-time synchronized backup system between network devices to achieve high availability technology that seamlessly switches to the backup device when the main device fails. It usually consists of two or more devices with the same configuration, one of which is active (the main device) and the other is on standby (the backup device). The backup device synchronizes data and status information in real time so that it can immediately take over the operation of network services when the main device fails. The specific workflow includes status synchronization, heartbeat detection, fault detection, fault switching, and recovery and rebalancing.

[0054] The operation and implementation of hot standby usually involves the following steps: creating a virtual router group (VRG), configuring the virtual router redundancy protocol (VRRP), starting the VRRP protocol, and ensuring that the active and standby routers can communicate normally. At the same time, it is necessary to configure fault detection mechanisms, including heartbeat detection and link detection, to ensure the reachability and connectivity between the active and standby routers. Monitoring and management are also important. The status and performance of the hot standby system are monitored through the network management system, and timely measures are taken to handle faults and abnormal situations. In Huawei equipment, the VRRP protocol can be used to achieve state synchronization and failover between devices, while in Cisco equipment, a similar technology is HSRP (Hot Standby Router Protocol). Actual examples include core routers and switches in enterprise networks, firewalls and other security devices, servers and storage systems in data centers, and critical business applications and databases.

[0055] Data denoising is a key technology in digital image processing, which aims to separate useful information from noisy signals, suppress noise, and thus improve image quality. Noise is usually randomly generated by multiple factors such as sensors, lighting conditions, and transmission channels. Denoising algorithms are usually based on statistical methods, frequency domain analysis, wavelet transforms, and other principles. Common denoising techniques include filter-based methods (such as median filters and Wiener filters), model-based methods (using image prior information to build models to describe noise and signals), and learning-based methods (learning the statistical characteristics of noise by training noisy and noise-free images to achieve noise suppression).

[0056] Data cleaning refers to the process of re-examining and verifying data, with the goal of removing duplicate information, correcting existing errors, and providing data consistency. The task of data cleaning is to filter out data that does not meet the requirements, including processing incomplete data, erroneous data, and duplicate data. Data cleaning deals with missing values, out-of-bounds values, inconsistent codes, duplicate data, and other issues from the perspectives of data accuracy, completeness, consistency, uniqueness, timeliness, and validity. The basic process of data cleaning includes cleaning missing values, removing unnecessary fields, filling in missing content, re-obtaining data, and correlation verification.

[0057] Data compression is a computer technology that reduces the size of data by reducing its redundancy. Its principle is to compress the original data into a smaller format for easy storage, transmission and printing while maintaining the integrity or recognizability of the data. Different compression algorithms use different data compression techniques, such as the LZW algorithm, PNG algorithm, JPEG algorithm and ZIP algorithm. Data compression can be divided into lossy compression and lossless compression according to whether the data can be restored before and after compression. Lossy compression will lose some information, but the compression rate is high; lossless compression can completely restore the original data, but the compression rate is relatively low.

[0058] See also Figure 1 , which is the first embodiment of the present invention, and which provides a distributed storage and processing system for power grid data, including:

[0059] Distributed storage unit 1, dual-resistance hot standby deployment unit 2, central control unit 3 and terminal 4, wherein the distributed storage unit 1 is connected to the dual-resistance hot standby deployment unit 2, and the central control unit 3 is respectively connected to the dual-resistance hot standby deployment unit 2 and the terminal 4; the distributed storage unit 1 is used to collect power grid data and perform data preprocessing, and realize data transmission and sharing among various distributed nodes; the dual-resistance hot standby deployment unit 2 is used to perform one-to-one data backup, and transmit the backup data by wired or wireless means; the central control unit 3 is used for data extraction and fault statistics, planning data sharing scope and deploying dual-resistance hot standby units according to faulty nodes; the terminal 4 is used to troubleshoot according to the alarm information sent by the central control unit 3.

[0060] Furthermore, the distributed storage unit 1 includes several data storage node modules 101 and data transmission path modules 102. Several data storage node modules 101 are used to collect the power grid data of the equipment or substation and then pre-process the data. The data transmission path module 102 is used to realize the transmission of power grid data between nodes and data sharing. Specifically, the data storage node module is a distributed module for data storage and preliminary data processing. Its role is to be distributedly arranged near each device or substation, and to collect the data of each device or substation, and to transmit it after preliminary data processing. In addition, several data storage node modules are connected by several data transmission path modules, and the data storage node modules realize data sharing through the data transmission path module. The above can be compared with the knowledge graph. The distributed storage unit is provided with several points and several edges. Each node is connected by an edge. The node is used for data storage, and data sharing between nodes is realized by an edge. The data storage node module is used to collect various data of the equipment or substation, and then pre-screen and pre-process the data. The pre-processing here includes de-noising, data cleaning, data compression, etc., which is convenient for further compression and processing of data, so as to avoid redundant data wasting data transmission resources and improve transmission efficiency.

[0061] Furthermore, the installation position of the dual-resistance hot standby deployment unit 2 is set by the central control unit 3 according to the target number of failures of several data storage node modules 1, and the target number of failures is the number of failures that meet the preset threshold conditions. Specifically, a dual-resistance hot standby deployment unit is installed in some data storage node modules. The dual-resistance hot standby deployment unit here mainly plays the role of one-to-one data backup. It is directly connected to the corresponding data storage node module and performs one-to-one data transmission and backup directly through wired or wireless means, further improving the transmission efficiency and the security of the data. An extra copy of the backup data can avoid the situation where a data storage node and a data storage node shared with it fail, and there is still backup data available.

[0062] It should be noted that the dual-resistance hot standby deployment unit is installed in some data storage node modules, but not in every data storage node. Considering the cost issue, only an additional dual-resistance hot standby deployment unit is connected to the data storage node module that often fails. The actual installation location of the dual-resistance hot standby deployment unit is given by the central control unit. The central control unit will collect the number of failures of each data storage node module (each time a data node module fails, the relevant operators will upload the failure data to the central control unit after on-site maintenance. At this time, the central control unit will grasp the failure status of each data storage node module, including the number of failures. The number of failures here is the number of failures after verification, which is different from the number of failures obtained in the data extraction module in the following step). After collecting the number of failures, when the number of failures of the data storage node module within the cycle is too high and higher than the set threshold, an additional dual-resistance hot standby deployment unit is deployed for the data storage node module. That is, it is directly connected to a backup device that realizes one-to-one transmission.

[0063] Furthermore, the central control unit 3 includes a data extraction module 301, a data sharing planning module 302 and a dual-resistance hot standby setting module 303. The data extraction module 301 is used to store the data extraction path, the data sharing planning module 302 is used to plan the data sharing range between several data storage node modules, and the dual-resistance hot standby setting module 303 is used to collect the number of failures of several data storage node modules and deploy the dual-resistance hot standby deployment unit 2 according to the collected number of failures.

[0064] Specifically, the data extraction module 301 stores the data extraction path according to the set priority, and the priority of the path of the data storage node module itself is greater than the path of the dual-resistance hot standby deployment unit, and the priority of the path of the dual-resistance hot standby deployment unit is greater than the path of other data storage node modules shared with several data storage node modules. In addition, the paths of other data storage node modules also have priority differences. The fewer data transmission path modules passed through the failed data storage node, the higher its priority. The specific priority can be adjusted according to the actual situation, and its purpose is to allow the central control unit to obtain data faster, more conveniently and with lower transmission cost. In addition, the data extraction module 301 can identify equipment failures and does not count the number of failures. It is the source of equipment failure (i.e., data storage node module failure) that fails to connect to the data supplier (such as data storage node storage module), and the data stored cannot be obtained, so it can be judged as a connection failure, and then the module failure is recorded, and an alarm signal is sent to the execution terminal.

[0065] Specifically, the data sharing planning module 302 is used to plan the data sharing range between several data storage node modules. That is to say, a group of data is first stored in the data storage node module (referred to as the parent node) with the closest distance and the lowest transmission cost, and the path planning unit selects other data storage node modules (referred to as child nodes) with which it (the parent node) realizes data sharing (data backup) based on comprehensive considerations such as cost and data security. In the data sharing planning unit, the optimal data sharing range is obtained by setting the objective function and solving it. In order to achieve the highest possible data security and the lowest possible cost.

[0066] Specifically, the dual-resistance hot standby setting module 303 will collect the number of failures of each data storage node module (each time a data node module fails, the relevant operating personnel will upload the failure data to the central control unit after going to the site for repair. At this time, the central control unit will grasp the failure conditions of each data storage node module, including the number of failures). After collecting the number of times, when the number of failures of the data storage node module within the cycle is too high and higher than the set threshold, an additional dual-resistance hot standby deployment unit will be deployed for the data storage node module. That is, a backup device that realizes one-to-one transmission is directly connected to it. The threshold value set mentioned above can be directly given by technical experts or other relevant managers based on technical experience or management experience, and can be modified according to actual conditions.

[0067] Furthermore, the terminal 4 includes an execution terminal 401 and a management terminal 402. The execution terminal 401 is used to receive the alarm information sent by the central control unit 3 and perform troubleshooting. The management terminal 402 is used to collect the number of faults recorded by the central control unit 3 and the number of faults confirmed by the execution terminal, and perform key troubleshooting based on the collected number of faults. Specifically, the execution terminal 401 and the management terminal 402 can be specifically installed on a personal mobile device. The execution terminal 401 receives the alarm signal from the central control unit, and goes to the fault site to perform troubleshooting based on the alarm signal. If the module (mother node, child node or double-click hot standby deployment unit) does fail, the number of faults is recorded (this is the number of faults confirmed by the execution module). The management terminal 402 collects the number of faults recorded by the central control unit and the number of faults confirmed by the execution module, and makes a judgment. If the two numbers are inconsistent, it may indicate that the central control unit has a network connection problem, which is convenient for troubleshooting. At this time, there is a focus on troubleshooting, and the focus is on troubleshooting the central control unit, rather than the module (mother node, child node or double-click hot standby deployment unit) that transmits data with it.

[0068] The data storage node module 1 in the above embodiment is responsible for collecting data from the equipment or substation, and performing preliminary screening and preprocessing, such as denoising, data cleaning and data compression. These processing steps help reduce unnecessary data transmission, improve transmission efficiency, and reduce storage costs. Through distributed arrangement, the data storage node module can process data nearby, reducing the transmission distance of data in the network, thereby improving the overall data transmission efficiency. The introduction of the dual-machine hot standby deployment unit 2 provides an additional data security layer for the system. Through one-to-one data backup, even if a data storage node fails, the system can restore data from the backup unit to ensure that the data is not lost. This redundancy mechanism significantly improves the reliability of data and the fault tolerance of the system. The data extraction module of the central control unit 3 can identify equipment failures and count the number of failures, providing data support for fault management and maintenance. The terminal makes the fault response faster and more accurate. The execution terminal performs on-site inspection according to the alarm signal of the central control unit, while the management terminal is responsible for collecting and comparing fault data to ensure the accuracy of fault management.

[0069] Embodiment 2

[0070] See also Figure 2 The data sharing planning module in the present invention is used to plan the data sharing scope between several data storage node modules and further comprises the following steps:

[0071] S21. construct an objective function with cost coefficient and data security coefficient as parameters;

[0072] S22. Determine the constraints of the objective function, the constraints including the minimum acceptable level of security, the maximum acceptable level of cost, and parameter constraints;

[0073] S23. Determine the target data sharing scope among several data storage node modules according to the objective function and constraint conditions.

[0074] The expression of the objective function is as follows:

[0075] Minimize h(S,C)=ω 1 ·(1―S)+ω 2 ·C

[0076] Where: h(S,C) is a comprehensive calculation function based on data security factor and cost factor, S is the normalized data security factor, C is the normalized cost factor, ω 1 and ω 2 are the corresponding weights respectively.

[0077] The expression of data safety factor S is as follows:

[0078] S=k 1 Q1 +k 2 Q 2 +k 3 Q 3

[0079] Where: Q 1 is the number of child nodes shared with the parent node, Q 2 Q is the maximum value of the edges connecting the parent node and all child nodes when the parent node and child nodes share data. 3 k is the coefficient of whether the mother node has a dual-resistance hot standby deployment unit. If it exists, it is 1; if it does not exist, it is 0. 1 , k 2 and k 3 is the corresponding weight coefficient.

[0080] The expression of cost coefficient C is as follows:

[0081] C=ρ 1 Q 1 ·X 1 +ρ 2 Q 2 ·X 2 +ρ 3 Q 3 ·X 3

[0082] Where: X 1 is the storage cost of a child node, X 2 is the cost of a data transmission path module, X 3 is the deployment cost of a dual-resistance hot standby deployment unit, ρ 1 , 2 and ρ 3 are the corresponding weight coefficients respectively.

[0083] The above costs can all be estimated by relevant personnel or determined through real-time prices in the actual market.

[0084] Minimum acceptable level of safety assurance:

[0085] S≥S min

[0086] Where: S min The minimum acceptable level of safety assurance; it can be directly given by technical experts or other relevant managers based on their technical experience or management experience, and can be modified based on actual conditions.

[0087] Maximum acceptable level of cost:

[0088] C≤C max

[0089] Where: Cmax The maximum acceptable level of cost; it can be directly given by technical experts or other relevant managers based on technical experience or management experience, and can be modified according to actual conditions.

[0090] Parameter constraints: Q 1 , Q 2 , Q 3 , X 1 , X 2 and X 3 Are both greater than or equal to 0.

[0091] By adjusting the strategy and calculating, we can find the optimal solution that satisfies the constraints and minimizes the objective function. In practical applications, we may need to use numerical optimization methods to find this optimal solution, which is the optimal data sharing range.

[0092] The above embodiment sets and solves the objective function to achieve the best balance between data security and cost. This planning takes into account data transmission cost and data security, and minimizes transmission cost while ensuring data security by optimizing the data sharing scope.

[0093] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0094] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0095] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0096] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A distributed storage and processing system for power grid data, characterized in that: include: Distributed storage units, dual-resistance hot standby deployment units, central control units, and terminals; The distributed storage unit is connected to the dual-resistance hot standby deployment unit, and the central control unit is connected to the dual-resistance hot standby deployment unit and the terminal respectively; The distributed storage unit is used to collect power grid data and perform data preprocessing, and realize data transmission and sharing among various distributed nodes; The dual-resistance hot standby deployment unit is used to perform one-to-one data backup and transmit the backup data in a wired or wireless manner; The central control unit is used for data extraction and fault statistics, planning data sharing scope, and deploying dual-resistance hot standby units according to faulty nodes; The terminal is used to perform troubleshooting based on the alarm information sent by the central control unit.

2. The distributed storage and processing system for power grid data according to claim 1, characterized in that: The distributed storage unit is used to collect power grid data and perform data preprocessing, and realize data transmission and sharing among various distributed nodes, including: The distributed storage unit includes several data storage node modules and data transmission path modules. The several data storage node modules are used to collect power grid data of equipment or substations and pre-process the data. The data transmission path module is used to realize the transmission of power grid data between nodes and data sharing.

3. The distributed storage and processing system for power grid data according to claim 2, characterized in that: The dual-resistance hot standby deployment unit is used to perform one-to-one data backup and transmit the backup data in a wired or wireless manner, including: The installation position of the dual-resistance hot standby deployment unit is set by the central control unit according to the target number of failures of the plurality of data storage node modules, where the target number of failures is the number of failures that meet a preset threshold condition.

4. The distributed storage and processing system for power grid data according to any one of claims 1 to 3, characterized in that: The central control unit is used for data extraction and fault statistics, planning data sharing scope, and deploying dual-resistance hot standby units according to faulty nodes, including: The central control unit includes a data extraction module, a data sharing planning module and a dual-antibody hot standby setting module. The data extraction module is used to store the data extraction path, the data sharing planning module is used to plan the data sharing scope between the several data storage node modules, and the dual-antibody hot standby setting module is used to collect the number of failures of the several data storage node modules and deploy the dual-antibody hot standby deployment unit according to the collected number of failures.

5. The distributed storage and processing system for power grid data according to claim 4, characterized in that: The data extraction module is used to store the data extraction path, including: The data extraction module stores data extraction paths according to a set priority, wherein the priority of the paths of the several data storage node modules themselves is greater than the path of the dual-resistance hot standby deployment unit, and the priority of the path of the dual-resistance hot standby deployment unit is greater than the path of other data storage node modules shared with the several data storage node modules.

6. The distributed storage and processing system for power grid data according to claim 4, characterized in that: The data sharing planning module is used to plan the data sharing scope between the plurality of data storage node modules, including: Construct an objective function with cost coefficient and data security coefficient as parameters; Determining constraints of the objective function, wherein the constraints include a minimum acceptable level of safety assurance, a maximum acceptable level of cost, and parameter constraints; The target data sharing range among the plurality of data storage node modules is determined according to the objective function and the constraint conditions.

7. The distributed storage and processing system for power grid data according to claim 6, characterized in that: The expression of the objective function is as follows: Minimizeh(S,C)=ω1·(1―S)+ω2·C Wherein: h(S,C) is a comprehensive calculation function based on data security factor and cost factor, S is the normalized data security factor, C is the normalized cost coefficient, ω1 and ω2 are the corresponding weights respectively.

8. The distributed storage and processing system for power grid data according to claim 7, characterized in that: The expression of the data safety factor S is as follows: S=k1·Q1+k2·Q2+k3·Q3 Among them: Q1 is the number of child nodes that share with the parent node, Q2 is the maximum value of the edges connecting the parent node and all child nodes when the parent node and the child nodes share data, Q3 is the coefficient of the parent node having a dual-resistance hot standby deployment unit, which is 1 if it exists and 0 if it does not exist, and k1, k2 and k3 are the corresponding weight coefficients; The expression of the cost coefficient C is as follows: C=ρ1·Q1·X1+ρ2·Q2·X2+ρ3·Q3·X3 Among them: X1 is the storage cost of a child node, X2 is the cost of a data transmission path module, X3 is the deployment cost of a dual-resistance hot standby deployment unit, and ρ1, ρ2 and ρ3 are the corresponding weight coefficients respectively.

9. The distributed storage and processing system for power grid data according to claim 8, characterized in that: The constraint conditions of determining the objective function, the constraint conditions including the minimum acceptable level of safety assurance, the maximum acceptable level of cost and parameter constraints, include: Minimum acceptable level of safety assurance: S≥S min Where: S min The minimum acceptable level of safety assurance; Maximum acceptable level of cost: C≤C max Where: C max is the maximum acceptable level of cost; Parameter constraints: Q1, Q2, Q3, X1, X2, and X3 are all greater than or equal to 0.

10. The distributed storage and processing system for power grid data according to claim 1, characterized in that: The terminal is used to perform troubleshooting according to the alarm information sent by the central control unit, including: The terminal includes an execution terminal and a management terminal. The execution terminal is used to receive the alarm information sent by the central control unit and perform troubleshooting. The management terminal is used to collect the number of faults recorded by the central control unit and the number of faults confirmed by the execution terminal, and perform focused troubleshooting based on the collected number of faults.

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