Data storage and backup method and system based on cloud platform

Abstract

The invention relates to the technical field of data storage, and discloses a data storage and backup method and system based on a cloud platform, and the method comprises the steps: determining a storage configuration strategy according to the importance level division condition of power data, and storing the power data through the storage configuration strategy. The stored power data is divided into a plurality of data blocks, and hash values of the data blocks are mapped to each storage node under the storage configuration strategy, so that potential faults in data storage can be effectively handled; and each data block is subjected to mirror image synchronous backup in data centers in a plurality of different geographic areas by adopting a multi-synchronization mechanism, so that quick synchronization and real-time availability of the data in a multi-node and cross-area cloud environment are ensured, and effective data guarantee is provided for stable operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage, and in particular to a data storage and backup method and system based on a cloud platform. Background Art

[0002] With the continuous growth of energy demand and the rapid development of power technology, the scale of the power system is expanding at an unprecedented rate, covering an increasingly wide area, and the types and number of equipment involved are increasing dramatically. This trend has accelerated the transformation of the power grid towards intelligence and automation. Among them, intelligent collection devices, as the core of the perception layer, are spread throughout all links of power generation, transmission, transformation, distribution and consumption. They can collect various types of data in the power system in real time and accurately, such as voltage, current, power factor and other key parameters. At the same time, the data processing device, as the brain of intelligent analysis, is responsible for converting massive raw data into valuable information, providing decision support for operation monitoring, fault warning, and optimized scheduling of the power system.

[0003] However, with the large-scale deployment of intelligent data collection devices and data processing devices, and the continuous expansion of data transmission lines, the amount of data information generated in the power grid has shown an explosive growth trend, and the traditional data processing model can no longer meet the needs of high efficiency, real-time and accuracy. In this context, cloud computing technology, with its powerful computing power, flexible resource allocation and high scalability, has gradually become a key way to solve the huge data processing problems of the power system.

[0004] It is not easy to effectively apply cloud computing technology to power systems. First of all, the servers in the power system are widely distributed, not only covering every corner of different geographical areas, but also across different geographical areas and time zones. In a multi-node and cross-regional cloud environment, the current cloud platform storage is difficult to effectively deal with potential failures in data storage, and it is difficult to ensure the rapid synchronization and real-time availability of data between different regions, and it is difficult to ensure effective data protection for the stable operation of the power system. Summary of the invention

[0005] In view of this, the present invention provides a data storage and backup method and system based on a cloud platform, which solves the technical problems that the current cloud platform storage is difficult to effectively deal with potential failures in data storage, difficult to ensure rapid synchronization and real-time availability of data between different areas, and also difficult to ensure effective data protection for the stable operation of the power system.

[0006] A first aspect of the present invention provides a data storage and backup method based on a cloud platform, comprising:

[0007] The power data to be stored uploaded to the cloud platform in real time is divided into importance levels, and a storage configuration strategy is determined according to the importance levels of the power data to be stored;

[0008] Storing the power data to be stored according to the storage configuration strategy;

[0009] Dividing the stored power data into a plurality of data blocks, and for each of the data blocks, mapping the hash value of the data block to each storage node under the storage configuration strategy;

[0010] A multiple synchronization mechanism is used to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas.

[0011] Preferably, the step of classifying the power data to be stored uploaded to the cloud platform in real time according to importance levels, and determining the storage configuration strategy according to the importance levels of the power data to be stored, includes:

[0012] Determine a data type according to the power data to be stored, and determine an importance level of the power data to be stored according to the data type, wherein the importance level includes secondary level data and important level data from low to high;

[0013] The storage configuration mode is determined according to the importance level of the power data to be stored, wherein the storage configuration mode of the secondary level data is the RAID-5 storage configuration mode, and the storage configuration mode of the important level data is the RAID-1 storage configuration mode.

[0014] Preferably, the total storage capacity space corresponding to the RAID-1 storage configuration is a multiple of the storage capacity of the importance level data, and the total storage capacity space corresponding to the RAID-5 storage configuration is the sum of the storage capacity of the secondary level data and the storage capacity of the parity data stored by all storage nodes.

[0015] Preferably, after the step of dividing the stored power data into a plurality of data blocks and, for each of the data blocks, mapping the hash value of the data block to each storage node under the storage configuration strategy, the method further comprises:

[0016] The hash values ​​of the data blocks respectively stored in the storage nodes are operated according to the data operation instructions, and the data operation instructions include adding new data and removing data.

[0017] Preferably, the step of using a multiple synchronization mechanism to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas also includes:

[0018] Taking minimizing the synchronization delay between storage nodes as the optimization goal, determine the synchronization delay optimization solution between storage nodes;

[0019] The data blocks are transmitted to data centers in various geographical areas using a synchronization delay optimization solution between storage nodes.

[0020] Preferably, before the step of classifying the power data to be stored uploaded to the cloud platform in real time according to importance levels and determining the storage configuration strategy according to the importance levels of the power data to be stored, the step further includes:

[0021] Pre-process the power data collected in real time from each node of the power grid system;

[0022] Based on the fast retransmission algorithm, the preprocessed power data is uploaded to the cloud platform through a multi-level cache strategy.

[0023] Preferably, the step of preprocessing the power data collected in real time by each node of the power grid system includes:

[0024] Performing data denoising on the power data using a wavelet transform algorithm or a Fourier transform algorithm;

[0025] The compression algorithm is used to compress the denoised power data.

[0026] In a second aspect, the present invention further provides a data storage and backup system based on a cloud platform, comprising:

[0027] A storage configuration module, used to classify the power data to be stored uploaded to the cloud platform in real time according to importance levels, and determine a storage configuration strategy according to the importance levels of the power data to be stored;

[0028] A data storage module, used for storing the power data to be stored according to the storage configuration strategy;

[0029] A data mapping module, used to divide the stored power data into a plurality of data blocks, and for each of the data blocks, map the hash value of the data block to each storage node under the storage configuration strategy;

[0030] The data backup module is used to use a multiple synchronization mechanism to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas.

[0031] In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the cloud platform-based data storage and backup method as described in the first aspect.

[0032] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the cloud platform-based data storage and backup method as described in the first aspect.

[0033] It can be seen from the above technical solutions that the present invention determines the storage configuration strategy by utilizing the division of power data according to the importance level, and stores the power data using the storage configuration strategy. It also divides the stored power data into multiple data blocks, and maps the hash value of the data block to each storage node under the storage configuration strategy, so as to effectively deal with potential failures in data storage, and uses a multiple synchronization mechanism to mirror and synchronize the backup of each data block in data centers in multiple different geographical regions, so as to ensure the rapid synchronization and real-time availability of data in a multi-node and cross-regional cloud environment, and ensure that effective data protection is provided for the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 An application environment diagram of a data storage and backup method based on a cloud platform provided by an embodiment of the present invention;

[0035] Figure 2 A flowchart of a data storage and backup method based on a cloud platform provided by an embodiment of the present invention;

[0036] Figure 3 A schematic diagram of the structure of a cloud platform-based data storage and backup system provided by an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The data storage and backup method based on the cloud platform provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, each node of the cloud platform communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0040] like Figure 2 As shown, the embodiment of the present application provides a data storage and backup method based on a cloud platform, and the method is applied to Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S1 to S4. Among them:

[0041] Step S1: The power data to be stored uploaded to the cloud platform in real time is divided according to importance levels, and a storage configuration strategy is determined according to the importance levels of the power data to be stored.

[0042] Among them, power data is the real-time collection of important power system parameter data such as voltage, current, power factor, etc. for each node of the power grid (including power generation, transmission, distribution, and power consumption). The importance level of power data can be determined based on the relative importance of the data.

[0043] The embodiment of the present application uses different storage configuration strategies to store power data of different importance levels.

[0044] Specifically, the process of classifying the power data to be stored uploaded to the cloud platform in real time according to the importance level in step S1, and determining the storage configuration strategy according to the importance level of the power data to be stored, includes:

[0045] Step S101 : determining a data type according to the power data to be stored, and determining an importance level of the power data to be stored according to the data type, wherein the importance levels include secondary level data and important level data from low to high.

[0046] Among them, for data types including but not limited to real-time operation data types, historical data types and low-frequency data types, the embodiment of the present application matches the data types according to the characteristics of the power data to be stored, and sets the corresponding importance level of each data type to ensure the efficient utilization of storage space.

[0047] Exemplarily, the real-time operation data type is important level data, and the historical data type and the low-frequency data type are secondary level data.

[0048] Step S102: Determine a storage configuration mode according to the importance level of the power data to be stored, wherein the storage configuration mode for secondary level data is a RAID-5 storage configuration mode, and the storage configuration mode for important level data is a RAID-1 storage configuration mode.

[0049] It can be understood that storing important level data through RAID-5 storage configuration can ensure real-time redundancy, and storing secondary level data through RAID-1 storage configuration can perform distributed storage and parity checking to ensure the efficiency and reliability of data storage.

[0050] Among them, RAID-5 is a redundant array of independent disks (RAID) configuration that provides data redundancy and performance improvement by striping data and adding parity information. RAID-5 requires at least three hard disks and can support more hard disks to increase storage capacity and improve performance.

[0051] In RAID-5, data is divided into small blocks (or "stripes"), and then these stripes are distributed across all the disks. Each stripe has a parity information associated with it, which is also distributed and stored on each disk, but not on the same disk as the corresponding data block. This means that if any one disk fails, the data and parity information on the other disks can be used to reconstruct the lost data.

[0052] RAID-1 is a simple way to provide data redundancy. It does this by writing the same data simultaneously to two or more hard drives, with each drive being an exact copy of the other.

[0053] Step S2: storing the power data to be stored according to the storage configuration strategy.

[0054] The total storage capacity space corresponding to the RAID-1 storage configuration is multiple times the storage capacity of the important level data. In one example, data is stored on multiple nodes, and the storage capacity of the data is D. If the RAID-1 mirror backup strategy is adopted, the total storage capacity space corresponding to the RAID-1 storage configuration is 2D.

[0055] The total storage capacity space corresponding to the RAID-5 storage configuration is the sum of the storage capacity of the secondary level data and the storage capacity of the parity data stored by all storage nodes, that is, the total storage capacity space corresponding to the RAID-5 storage configuration is:

[0056]

[0057] In the formula, The total storage capacity space corresponding to the RAID-5 storage configuration mode. is the storage capacity of secondary level data stored by storage nodes, P is the storage capacity of parity data, and N is the number of storage nodes.

[0058] Among them, the parity check P provides redundancy to ensure that when any node fails, the system can still recover the lost data through the parity check.

[0059] Step S3: Divide the stored power data into multiple data blocks, and for each data block, map the hash value of the data block to each storage node under the storage configuration strategy.

[0060] It should be noted that, in order to further optimize storage allocation and cross-region synchronization of data, the embodiment of the present application combines a distributed hash table (DHT) to perform intelligent distribution of data blocks.

[0061] Specifically, in the DHT storage configuration strategy, the cloud platform allocates each data block according to the hash value, which ensures that the data blocks are evenly distributed on different physical nodes and avoids the bottleneck problem caused by single-point storage.

[0062] Assuming that there are M data blocks that need to be stored on N storage nodes, the embodiment of the present application maps data block k to node N through the hash function H(k) i superior:

[0063]

[0064] Where k represents the hash value of the data block.

[0065] Among them, by hash mapping each data block to each storage node under the storage configuration strategy, the uniform distribution of data blocks can be ensured.

[0066] In some embodiments, the hash values ​​of the data blocks respectively stored in each storage node are operated according to data operation instructions, and the data operation instructions include adding data and removing data, so that the data of all storage nodes can be uniformly modified.

[0067] Step S4: Use a multiple synchronization mechanism to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas.

[0068] It can be understood that in a distributed cloud platform environment, in order to ensure data consistency and availability between different geographical areas, the embodiment of the present application introduces a multi-level backup strategy based on data importance. For data with high real-time requirements, the embodiment of the present application adopts a multiple synchronization mechanism to perform mirror synchronization backup in data centers in multiple different geographical areas.

[0069] Exemplarily, when backing up important-level data through RAID-1 storage configuration, mirror synchronization is performed between nodes that are geographically far apart. In order to cope with network fluctuations and delay changes, the system can also dynamically adjust the location of the backup node to ensure efficient transmission and synchronization of data between different nodes. To this end, in step S4, the process of using a multiple synchronization mechanism to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas also includes:

[0070] Step S401: Taking minimizing the synchronization delay between the storage nodes as the optimization goal, determine a synchronization delay optimization scheme between the storage nodes.

[0071] Among them, the synchronization delay optimization solution is described as:

[0072]

[0073] In the formula, is the total synchronization delay between storage nodes, N is the number of storage nodes, is the transmission delay between storage node i and storage node j. The synchronization delay optimization scheme is optimized by a mathematical solver to determine the minimum transmission delay between each storage node, thereby determining the minimum total synchronization delay between each storage node.

[0074] It can be understood that by minimizing the synchronization delay between nodes, efficient data synchronization across regions can be achieved. At the same time, in order to cope with network fluctuations and delay changes, the system can also dynamically adjust the location of backup nodes to ensure efficient transmission and synchronization of data between different nodes.

[0075] Step S402: Transmit the data blocks to the data centers in each geographical area by using the synchronization delay optimization solution between the storage nodes.

[0076] It should be noted that the embodiment of the present application determines the storage configuration strategy by utilizing the division of power data according to the importance level, and stores the power data using the storage configuration strategy. It also divides the stored power data into multiple data blocks, and maps the hash value of the data block to each storage node under the storage configuration strategy, so as to effectively deal with potential failures in data storage, and mirrors and synchronizes each data block in data centers in multiple different geographical regions by adopting a multiple synchronization mechanism, so as to ensure the rapid synchronization and real-time availability of data in a multi-node and cross-regional cloud environment, and ensure that effective data protection is provided for the stable operation of the power system.

[0077] In some embodiments, before step S1, the method further includes:

[0078] Step S11: pre-process the power data collected in real time by each node of the power grid system.

[0079] Among them, in order to improve data transmission efficiency and reduce the computing pressure of the central cloud platform, it is first necessary to pre-process these real-time collected power data through edge computing nodes to perform preliminary processing and filtering of the power data.

[0080] Specifically, step S11 includes:

[0081] Step S111: denoising the power data using a wavelet transform algorithm or a Fourier transform algorithm.

[0082] Among them, algorithms based on wavelet transform or Fourier transform can identify and separate noise components by converting signals from the time domain to the frequency domain, and then remove unnecessary noise interference through technical means such as threshold filtering. Ultimately, the data after denoising will be more accurate and can effectively reflect the actual operating status of the power system, providing a basis for subsequent data analysis and intelligent scheduling.

[0083] Step S112: compress the de-noised power data using a compression algorithm.

[0084] Among them, the embodiment of the present application introduces a compression algorithm, such as differential coding or predictive coding, and compresses the power data through the compression algorithm, thereby further reducing the data volume and optimizing the transmission efficiency.

[0085] Step S12: Based on the fast retransmission algorithm, the pre-processed power data is uploaded to the cloud platform through a multi-level cache strategy.

[0086] Among them, in some embodiments, in the process of uploading power data to the cloud platform, the pre-processed power system data needs to be transmitted in a safe and reliable manner to ensure that it is not interfered with by the outside world during the transmission process, and can reach the cloud efficiently and with low latency for storage and further processing.

[0087] Taking into account the huge amount of data in the power system and the possible instability of the network environment, the embodiment of the present application introduces a multi-level caching strategy to cope with potential network fluctuations. During the transmission process, a multi-level caching strategy is used to cope with network fluctuations to ensure that data can be transmitted to cloud storage reliably and with low latency.

[0088] Specifically, the process of uploading the pre-processed power data to the cloud platform through a multi-level cache strategy based on a fast retransmission algorithm in step S12 includes: the multi-level cache strategy is implemented through a cache mechanism at multiple levels, starting from the edge computing node cache, the power data is first temporarily stored in the edge node cache close to the acquisition source, and then gradually uploaded to the regional cache, and stored again in the regional cache for a short period of time, and finally transmitted to the cloud platform cache, and a computing task is generated according to the power data. Among them, the cache time can be set based on experience.

[0089] It is understandable that the multi-level cache strategy can ensure that even if there is a temporary interruption or congestion in the network transmission path, the data can be continuously and stably uploaded to the cloud through the support of each layer of cache. At the same time, the introduction of the multi-level cache mechanism can also significantly reduce the data loss or delay caused by sudden network jitter, ensuring the smoothness and efficiency of the entire transmission process.

[0090] In addition, in order to improve the efficiency of data transmission and reduce delays, especially in a complex and dynamically changing network environment such as the power system, the embodiment of the present application adopts a fast retransmit algorithm. Under the traditional transmission control protocol, when data packets are lost or delayed, the entire transmission process is often affected. Through the fast retransmit mechanism, when certain data packets are detected to be lost, the system will quickly resend these lost data packets, avoiding long delays caused by waiting timeouts in conventional transmissions. This optimization process can effectively improve the reliability of transmission and greatly shorten the response time of data transmission.

[0091] Based on the same inventive concept, an embodiment of the present application also provides a cloud platform-based data storage and backup system for implementing the above-mentioned cloud platform-based data storage and backup method.

[0092] The implementation solution to the problem provided by the system is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more cloud platform-based data storage and backup system embodiments provided below can be found in the above limitations on the cloud platform-based data storage and backup method, which will not be repeated here.

[0093] like Figure 3 As shown, the embodiment of the present application also provides a data storage and backup system based on a cloud platform, including:

[0094] The storage configuration module 100 is used to classify the power data to be stored uploaded to the cloud platform in real time according to the importance level, and determine the storage configuration strategy according to the importance level of the power data to be stored;

[0095] The data storage module 200 is used to store the power data to be stored according to the storage configuration strategy;

[0096] The data mapping module 300 is used to divide the stored power data into a plurality of data blocks, and for each data block, map the hash value of the data block to each storage node under the storage configuration strategy;

[0097] The data backup module 400 is used to use a multiple synchronization mechanism to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas.

[0098] In some embodiments, the storage configuration module 100 includes:

[0099] A level determination module, used to determine a data type according to the power data to be stored, and determine an importance level of the power data to be stored according to the data type, wherein the importance levels include secondary level data and important level data from low to high;

[0100] The configuration determination module is used to determine the storage configuration mode according to the importance level of the power data to be stored, wherein the storage configuration mode of the secondary level data is the RAID-5 storage configuration mode, and the storage configuration mode of the important level data is the RAID-1 storage configuration mode.

[0101] In some embodiments, the total storage capacity space corresponding to the RAID-1 storage configuration is a multiple of the storage capacity of the important level data, and the total storage capacity space corresponding to the RAID-5 storage configuration is the sum of the storage capacity of the secondary level data and the storage capacity of the parity data stored by all storage nodes.

[0102] In some embodiments, the system further comprises:

[0103] The data modification module is used to operate the hash value of the data block stored in each storage node according to the data operation instruction, and the data operation instruction includes adding data and removing data.

[0104] In some embodiments, the system further comprises:

[0105] A delay optimization module is used to determine a synchronization delay optimization scheme between storage nodes with minimization of synchronization delay between storage nodes as an optimization goal;

[0106] The transmission module is used to transmit data blocks to data centers in various geographical areas by utilizing a synchronization delay optimization solution between storage nodes.

[0107] In some embodiments, the system further comprises:

[0108] A preprocessing module is used to preprocess the power data collected in real time from each node of the power grid system;

[0109] The data upload module is used to upload the pre-processed power data to the cloud platform through a multi-level cache strategy based on a fast retransmission algorithm.

[0110] Among them, the preprocessing module includes:

[0111] A data denoising module, used for denoising power data using a wavelet transform algorithm or a Fourier transform algorithm;

[0112] The data compression module is used to compress the de-noised power data using a compression algorithm.

[0113] like Figure 4 As shown, an embodiment of the present application also provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the cloud platform-based data storage and backup method in any of the above embodiments.

[0114] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the cloud platform-based data storage and backup method in any of the above embodiments.

[0115] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, electronic device and computer storage medium can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0117] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a part of a module, a program segment or a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0118] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0121] 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 is essentially 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. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the method described in each embodiment of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0122] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data storage and backup method based on a cloud platform, characterized in that: include: The power data to be stored uploaded to the cloud platform in real time is divided into importance levels, and a storage configuration strategy is determined according to the importance levels of the power data to be stored; Storing the power data to be stored according to the storage configuration strategy; Dividing the stored power data into a plurality of data blocks, and for each of the data blocks, mapping the hash value of the data block to each storage node under the storage configuration strategy; A multiple synchronization mechanism is used to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas.

2. The data storage and backup method based on the cloud platform according to claim 1, characterized in that: The step of classifying the power data to be stored uploaded to the cloud platform in real time according to importance levels, and determining the storage configuration strategy according to the importance levels of the power data to be stored, includes: Determine a data type according to the power data to be stored, and determine an importance level of the power data to be stored according to the data type, wherein the importance level includes secondary level data and important level data from low to high; The storage configuration mode is determined according to the importance level of the power data to be stored, wherein the storage configuration mode of the secondary level data is the RAID-5 storage configuration mode, and the storage configuration mode of the important level data is the RAID-1 storage configuration mode.

3. The cloud platform-based data storage and backup method according to claim 2, characterized in that: The total storage capacity space corresponding to the RAID-1 storage configuration is a multiple of the storage capacity of the importance level data, and the total storage capacity space corresponding to the RAID-5 storage configuration is the sum of the storage capacity of the secondary level data and the storage capacity of the parity data stored by all storage nodes.

4. The data storage and backup method based on the cloud platform according to claim 1, characterized in that: After the step of dividing the stored power data into a plurality of data blocks and, for each of the data blocks, mapping the hash value of the data block to each storage node under the storage configuration strategy, the method further includes: The hash values ​​of the data blocks respectively stored in the storage nodes are operated according to the data operation instructions, and the data operation instructions include adding new data and removing data.

5. The data storage and backup method based on the cloud platform according to claim 1, characterized in that: The step of using a multiple synchronization mechanism to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas also includes: Taking minimizing the synchronization delay between storage nodes as the optimization goal, determine the synchronization delay optimization solution between storage nodes; The data blocks are transmitted to data centers in various geographical areas using a synchronization delay optimization solution between storage nodes.

6. The cloud platform-based data storage and backup method according to any one of claims 1 to 5, characterized in that: Before the step of classifying the power data to be stored uploaded to the cloud platform in real time according to importance levels and determining the storage configuration strategy according to the importance levels of the power data to be stored, the method further includes: Pre-process the power data collected in real time from each node of the power grid system; Based on the fast retransmission algorithm, the preprocessed power data is uploaded to the cloud platform through a multi-level cache strategy.

7. The cloud platform-based data storage and backup method according to claim 6, characterized in that: The step of preprocessing the power data collected in real time from each node of the power grid system includes: Performing data denoising on the power data using a wavelet transform algorithm or a Fourier transform algorithm; The compression algorithm is used to compress the denoised power data.

8. A data storage and backup system based on a cloud platform, characterized in that: include: A storage configuration module, used to classify the power data to be stored uploaded to the cloud platform in real time according to importance levels, and determine a storage configuration strategy according to the importance levels of the power data to be stored; A data storage module, used for storing the power data to be stored according to the storage configuration strategy; A data mapping module, used to divide the stored power data into a plurality of data blocks, and for each of the data blocks, map the hash value of the data block to each storage node under the storage configuration strategy; The data backup module is used to use a multiple synchronization mechanism to perform mirror synchronization backup of each data block in data centers in multiple different geographical areas.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the cloud platform-based data storage and backup method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the cloud platform-based data storage and backup method as described in any one of claims 1 to 7 are implemented.

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