Field control and charging equipment distributed data storage method, system and device based on electric ion system and storage medium

Abstract

The application relates to the technical field of data storage, and discloses a field control and charging equipment distributed data storage method, system and device based on an electric Hong system and a storage medium. The method stores structured operation data of charging equipment in a local main storage node, and stores core data in a physically isolated and independently powered core backup node after encryption; through an electric Hong software bus, non-real-time summary data of the equipment is synchronized to a regional storage cluster with redundant nodes at the field control end; regional level data such as station load, equipment online state and charging orders are uniformly stored in the cluster; time slice data is batch uploaded to a cloud storage cluster through wireless communication; and relying on a distributed storage management system, each level storage node is monitored and linked in real time. The application builds a hierarchical distributed storage architecture, and comprehensively improves the safety, reliability and access efficiency of data storage.

Description

Technical Field

[0001] This invention relates to the field of data storage technology, and in particular to a distributed data storage method, system, device, and storage medium for field control and charging equipment based on a power supply system. Background Technology

[0002] Against the backdrop of the large-scale development of new energy charging stations, charging equipment continuously generates massive amounts of structured operational data, covering key information such as charging voltage, current, power, equipment status, fault information, and user orders. This data plays a decisive role in the safe operation of charging stations, user service guarantees, and rapid fault response. However, existing data storage solutions have systemic defects, making it difficult to meet the comprehensive needs of modern charging stations for data storage security, efficiency, collaboration, and scalability. Traditional centralized storage architectures rely excessively on single server nodes and lack effective emergency recovery mechanisms. Once a server encounters hardware failure or software anomalies, it is highly susceptible to data loss across the entire station, causing operational paralysis and charging interruptions for users. While some solutions employ a single local or cloud storage model, they lack a multi-layered backup system. When charging equipment experiences power outages or network interruptions, core data such as fault records and charging start / stop logs cannot be recovered in a timely manner, posing a severe challenge to data reliability. In the data processing stage, existing technologies generally use generic lossless compression algorithms to process charging data, failing to optimize for the characteristics of charging data, such as high structure, high repetition rate of fixed fields (e.g., pile number, parameter units), and strong regularity of numerical sequence changes (e.g., voltage and current fluctuations). This results in low compression efficiency, local storage space being occupied by a large amount of redundant data, excessive network resource consumption during cloud synchronization, and prolonged data transmission cycles. Furthermore, inconsistent compression methods between different storage nodes necessitate frequent transcoding during data exchange, significantly increasing the risk to data integrity. At the system architecture level, existing solutions fail to integrate the low-latency communication advantages of the E-Hong soft bus to build a collaborative storage network. Charging device data is stored in isolation locally, requiring the site control terminal to operate across multiple independent systems when accessing aggregated data from multiple devices. The lack of a regional-level site control storage node as a data aggregation layer leads to slow data scheduling response and low efficiency. The entire system also lacks a unified distributed management mechanism, resulting in data fragmentation between the device and site control ends, making it impossible to dynamically monitor node load and implement data migration, and hindering resource balancing when local nodes are overloaded. Furthermore, the storage architecture suffers from severe scalability issues. Adding new charging equipment requires manual intervention to adjust storage configurations, and the system cannot automatically adapt to dynamic changes in the scale of charging stations, hindering the rapid deployment and technological iteration of charging infrastructure. These problems collectively prevent existing technologies from effectively guaranteeing the robustness, timeliness, and scalability of data storage at charging stations. Summary of the Invention

[0003] The purpose of this invention is to provide a distributed data storage method, system, device, and storage medium for field control and charging equipment based on the Dianhong system, which can improve data storage security, reliability, and access efficiency.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a distributed data storage method for field control and charging equipment based on a power supply system, comprising the following steps: The structured operational data of the charging equipment is stored on the main storage node of the charging equipment, and the core data of the charging equipment is encrypted and stored on the core backup node of the charging equipment. The main storage node and the core backup node are physically isolated and independently powered. The non-real-time aggregated data of the charging equipment is synchronized to the regional storage node cluster of the field control terminal through the Dianhong soft bus. The regional storage node cluster includes 2-4 redundant storage nodes. The regional-level data of the site control terminal is stored in the regional storage node cluster. The regional-level data includes site load, equipment online status and user charging orders. Time-sliced ​​data from the regional storage node cluster is uploaded in batches to the cloud storage cluster via wireless communication; The distributed storage management system monitors the status of each storage node and interacts in real time with the storage nodes of the charging equipment, the regional storage node cluster, and the cloud storage cluster.

[0005] Furthermore, the primary storage node stores compressed real-time operating data, including charging voltage, charging current, charging power, equipment status, fault information, pile number, and parameter units; the core backup node stores encrypted fault records and charging start / stop logs; the primary storage node and the core backup node achieve data synchronization through a built-in protocol.

[0006] Furthermore, the distributed storage management system adopts a two-dimensional data sharding strategy based on device number and time slice. The entire data is divided into device shards according to device number, and each device shard is further divided into time slices in one-hour units. The storage nodes of the charging devices store their own device shards as well as randomly assigned shards of other devices. The randomly assigned shards of other devices are used for disaster recovery. Each node in the regional storage node cluster stores the device shards it is responsible for and the index of the entire time slice. Data scheduling follows the principle of proximity priority. When calling the real-time data of a single charging device, it reads from the local storage node of the charging device. When calling the aggregated data of multiple devices, it reads from the regional storage node cluster.

[0007] Furthermore, non-real-time aggregated data and time-sliced ​​data are compressed using an improved DEFLATE lossless compression algorithm. The compression process includes: establishing a global dictionary for fixed fields in the data, mapping pile number, parameter unit, and equipment model to short codes of 1-2 bytes; using difference encoding for numerical data, taking the first data in the time period as the baseline value, and storing only the difference between the subsequent data and the baseline value; and carrying a CRC32 checksum at the end of the compressed data.

[0008] Furthermore, the difference encoding includes: taking the first charging current data or charging voltage data in the time period as the reference value, and storing only the deviation value relative to the reference value in subsequent charging current data or charging voltage data; the deviation value carries a positive or negative sign to indicate the direction of increase or decrease.

[0009] Furthermore, the compression process also includes: summing and averaging repeated parameters within a preset threshold range for a single registration, storing the average value and data volume; performing first-order and second-order fitting on the charging curve, storing only the fitting function parameters, where the first-order fitting is a linear fitting and the second-order fitting is a polynomial fitting, and the fitting function parameters include the slope and intercept of the first-order fitting and the coefficients of the quadratic term, the coefficients of the first term, and the constant term of the second-order fitting.

[0010] Furthermore, it also includes fault-tolerant recovery steps: when the primary storage node fails, it automatically switches to the core backup node; when both the primary storage node and the core backup node fail, it reads backup data from the backup node of the regional storage node cluster via the Elec-Tech soft bus; when the primary node in the regional storage node cluster fails, the backup node automatically takes over storage and synchronization work; when both the primary node and the backup node in the regional storage node cluster fail, it downloads regional data within a preset time range from the cloud storage cluster and rebuilds the field control storage by combining it with the local data of each charging device; after the node reconnects, it supplements the missing data through differential synchronization, and the recovery process supports breakpoint resume.

[0011] Furthermore, the distributed storage management system monitors the storage occupancy, CPU load, and network bandwidth of each storage node in real time. When the load of the charging device's storage node exceeds a preset threshold, the system migrates the non-real-time data of the charging device to a storage node with a lower load via the Elec-Tech soft bus to balance the storage pressure.

[0012] Furthermore, the charging equipment synchronizes non-real-time aggregated data to the regional storage node cluster in real time via the E-Hong soft bus. The synchronization frequency is dynamically adjusted according to the equipment load. When the equipment load exceeds a preset ratio, the synchronization frequency is reduced to one minute. The regional storage node cluster uploads the full-time time-segmented data to the cloud storage cluster in batches every hour. The cloud storage cluster performs CRC32 verification on the received data. After the verification is successful, redundant data is removed, and bidirectional synchronization is maintained with the regional storage node cluster.

[0013] This application also proposes a distributed data storage system for field control and charging equipment based on an electric power system, comprising: The charging device has a local distributed storage module, which includes a main storage node and a core backup node. The main storage node is used to store compressed real-time running data, and the core backup node is used to store encrypted core data. The main storage node and the core backup node are physically isolated and powered independently. The site control terminal regional storage node cluster includes 2-4 redundant storage nodes, which are used to store regional data and back up the core data of each charging device. Regional data includes site load, equipment online status and user charging orders. The cloud storage cluster is used to receive time-sliced ​​data uploaded in batches from the regional storage node cluster, perform CRC32 verification and remove redundant data, and form a third set of backup storage. The Elec-Power Soft Bus Communication Module is used to establish a low-latency connection between the local distributed storage module on the charging device side and the regional storage node cluster on the field control side, and supports heartbeat detection and breakpoint resume mechanism. The distributed storage management system module is used to interact with each storage node in real time, and to perform node management, data sharding scheduling and load balancing.

[0014] Furthermore, it also includes an extreme compression processing module, which adopts an improved DEFLATE lossless compression algorithm, including a scenario-based optimization layer and a data preprocessing unit. The scenario-based optimization layer establishes a global dictionary for fixed fields, mapping the pile number, parameter unit, and equipment model to 1-2 byte short codes, and uses difference encoding for numerical data. The data preprocessing unit sums and averages repeated parameters within a preset threshold range and registers them individually, and performs primary and secondary fitting on the charging curve. The extreme compression processing module establishes a unified compression standard for the entire link, and the compressed data carries a CRC32 checksum at the end.

[0015] This application also proposes a distributed data storage device for field control and charging equipment based on an electric system, comprising: At least one processor; At least one memory for storing at least one program; When at least one program is executed by at least one processor, the at least one processor implements the above-described distributed data storage method for field control and charging equipment based on the electric power system.

[0016] This application also proposes a computer-readable storage medium storing processor-executable instructions, which, when executed by a processor, are used to perform the aforementioned distributed data storage method for field control and charging devices based on an electric system.

[0017] Compared with existing technologies, the present invention provides a distributed data storage method, system, device and storage medium for field control and charging equipment based on the Dianhong system. Through distributed storage and monitoring mechanisms, it solves the problems of data loss and inefficiency in existing technologies, and can improve data storage security, reliability and access efficiency.

[0018] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more easily understood, specific embodiments of the present invention are described below. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments will be briefly introduced below. Obviously, the drawings described below only relate to some embodiments of the present invention and are not intended to limit the present invention.

[0020] Figure 1 This is a schematic flowchart of a distributed data storage method for field control and charging equipment based on an electric power system, provided as an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of a distributed data storage system for field control and charging equipment based on the Dianhong system, provided as an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of this invention. The components of this invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0023] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] Existing data storage solutions for new energy charging stations suffer from problems such as poor data security, low storage efficiency, insufficient collaboration, and poor scalability when scaled up. For example, centralized storage is prone to single points of failure and lacks multi-layer backup; general compression algorithms are inefficient and data interaction carries transcoding risks; and the failure to fully utilize the eHong soft bus to build collaborative storage results in slow data scheduling response and a lack of unified management and load balancing capabilities.

[0025] In this regard, such as Figure 1 As shown, this embodiment proposes a distributed data storage method for field control and charging equipment based on an electric power system. This method aims to solve the aforementioned technical problems and is implemented through the following steps: The structured operational data of the charging device is stored in the main storage node of the charging device, and the core data of the charging device is encrypted and stored in the core backup node of the charging device. The main storage node and the core backup node are physically isolated and independently powered. The non-real-time aggregated data of the charging device is synchronized to the regional storage node cluster of the field control terminal via the Dianhong soft bus. The regional storage node cluster includes 2-4 redundant storage nodes. The regional-level data of the site control terminal is stored in the regional storage node cluster. This regional-level data includes site load, equipment online status and user charging orders. The time-sliced ​​data in the storage node cluster of this region is uploaded in batches to the cloud storage cluster via wireless communication; The status of each storage node is monitored by a distributed storage management system, which interacts in real time with the storage nodes of the charging device, the storage node cluster in the region, and the cloud storage cluster.

[0026] First, the structured operational data generated by the charging device is stored on the local primary storage node. This data can include raw operational logs or sensor readings. Simultaneously, critical data from the charging device, such as configuration parameters or security credentials, is encrypted and stored on the local core backup node. To ensure data security and system stability, the primary storage node and the core backup node are designed to be physically isolated and each has its own independent power supply system. For example, the primary storage node can use a high-speed solid-state drive, while the core backup node can use an independent embedded storage module. Both are powered by separate power lines to prevent a single point of failure from affecting all local storage.

[0027] Optionally, to achieve data collaboration between the charging equipment and the site control center, the non-real-time summary data of the charging equipment is synchronized to the site control center's regional storage node cluster via the Elec-Tech soft bus. The Elec-Tech soft bus provides a high-efficiency, low-latency communication channel. The non-real-time summary data may include statistical data of the charging equipment over a period of time, such as total daily charging volume and average power. The regional storage node cluster consists of multiple redundant storage nodes, for example, it can be configured with 2, 3, or 4 nodes to provide data redundancy and high availability. For example, the charging equipment can periodically package the summary data and send it to any available node in the regional storage node cluster via the Elec-Tech soft bus.

[0028] Building upon this, regional-level data from the site control system is also stored in the regional storage node cluster. This regional-level data encompasses key information about the site's operation, such as the overall load status of the site, the online status of all charging equipment, and details of user-submitted charging orders. This data plays a crucial role in the daily management and scheduling of the site. For example, the site control system can periodically write the current site load data, the online equipment list, and newly generated user order information to the regional storage node cluster.

[0029] Furthermore, to achieve remote backup and long-term archiving of data, time-sharded data in the regional storage node cluster is uploaded in batches to the cloud storage cluster via wireless communication. Time-sharded data refers to data blocks divided according to a specific time granularity, such as hourly or daily data sets. Wireless communication methods can include cellular networks or Wi-Fi. The cloud storage cluster, acting as a remote storage center, receives and stores this uploaded data. For example, the regional storage node cluster can be configured to package all time-sharded data from the previous day at midnight each day and upload it to the cloud storage cluster via a wireless network connection.

[0030] Finally, to effectively manage the entire distributed storage system, a distributed storage management system is deployed to monitor the status of each storage node. This system maintains real-time interaction with the storage nodes of the charging equipment, the regional storage node cluster, and the cloud storage cluster. This real-time interaction allows the management system to obtain information on the operational status of each node, storage capacity usage, and data synchronization status. For example, the distributed storage management system can periodically send heartbeat packets to all storage nodes and receive their status reports, thereby gaining a comprehensive understanding of the entire storage network's operation.

[0031] In some possible embodiments, the main storage node stores compressed real-time operating data, including charging voltage, charging current, charging power, equipment status, fault information, pile number, and parameter units; the core backup node stores encrypted fault records and charging start / stop logs; the main storage node and the core backup node achieve data synchronization through a built-in protocol.

[0032] Specifically, the primary storage node is configured to store real-time operational data of the charging equipment. This real-time operational data, such as charging voltage, charging current, charging power, equipment status, fault information, charging station number, and parameter units, is critical information that is continuously generated and frequently recorded during the operation of the charging equipment. To improve storage efficiency and reduce data transmission bandwidth consumption, this real-time operational data is compressed before being stored on the primary storage node. Compression can employ various lossless compression algorithms, such as dictionary-based compression, differential coding, or run-length encoding, which can significantly reduce data volume without sacrificing data accuracy. By compressing the real-time operational data, the lifespan of the primary storage node can be effectively extended, and the performance of subsequent data synchronization and queries can be optimized.

[0033] The core backup node is dedicated to storing encrypted fault records and charging start / stop logs. Fault records provide detailed descriptions of equipment malfunctions, crucial for fault diagnosis and maintenance; charging start / stop logs record the start and end times of each charging service, serving as foundational data for billing and operational analysis. Due to the high sensitivity and importance of this data, it is encrypted before being stored on the core backup node. Encryption can employ symmetric encryption algorithms (such as AES) or asymmetric encryption algorithms (such as RSA) to ensure that even if the core backup node is illegally accessed, the data cannot be easily deciphered, thus guaranteeing data confidentiality and integrity.

[0034] To ensure data consistency and reliability between the primary storage node and the core backup node, data synchronization is achieved through a built-in protocol. This built-in protocol is a communication mechanism specifically designed for these two physically isolated and independently powered nodes. It defines the data transmission format, timing, error checking, and retransmission mechanisms. The protocol can be implemented based on a serial communication interface (such as SPI, I2C, or UART) or a dedicated internal bus, and may include a heartbeat mechanism to monitor the status of the other node. Data synchronization through this built-in protocol ensures that the core backup node can provide the latest and complete critical backup data in the event of a primary storage node failure, thereby improving the fault tolerance and data reliability of the entire system.

[0035] In some possible embodiments, a distributed storage management system is proposed to adopt a two-dimensional data sharding strategy based on device number and time slice. The entire data is divided into device shards according to device number, and each device shard is further divided into time slices in 1-hour units. The storage nodes of the charging devices store their own device shards as well as randomly assigned shards of other devices. The randomly assigned shards of other devices are used for disaster recovery. Each node in the regional storage node cluster stores the device shards it is responsible for and the index of the entire time slice. Data scheduling follows the principle of proximity priority. When calling real-time data of a single charging device, it is read from the local storage node of the charging device. When calling aggregated data of multiple devices, it is read from the regional storage node cluster.

[0036] Specifically, the distributed storage management system employs a two-dimensional data sharding strategy based on device IDs and time slices, aiming to achieve refined management and efficient organization of massive amounts of data. This strategy first logically divides the entire site's data into independent device shards based on the unique device IDs of the charging equipment, ensuring logical continuity of all data related to a specific device. Furthermore, the data within each device shard is further divided into time shards, using one-hour units and the timestamps generated by the data. This two-dimensional sharding method provides a clear structure for data storage, facilitating subsequent querying and management.

[0037] In terms of data storage, the storage nodes of charging devices are configured to store not only the real-time data generated by the device itself (i.e., its own device fragments), but also, according to a preset disaster recovery strategy, randomly store a portion of device fragment data from other charging devices. These randomly allocated fragments from other devices serve as backup data, primarily for disaster recovery purposes. When the local storage node of a charging device fails, its data can be recovered from the storage nodes of other devices, thereby significantly improving the availability of local data and the overall resilience of the system.

[0038] Meanwhile, each node in the regional storage node cluster is designed to store the device shard data it is responsible for, in order to process device data within its jurisdiction. More importantly, each regional storage node also stores a global time-shard index. This index records the data distribution of all devices across various time shards; for example, which devices have data in which time shards, and in which specific device shard that data is stored. This enables the regional storage node cluster to efficiently support aggregated queries across devices and time periods without traversing the local storage of all charging devices.

[0039] In terms of data scheduling and access, this embodiment follows the principle of proximity priority. This means that when reading data, the system will prioritize retrieving data from the storage node closest to the request source to minimize network latency and improve data access speed. Specifically, when real-time data of a single charging device needs to be retrieved, such as the current charging voltage and current, the system will directly read from the local storage node of that charging device, ensuring a fast response to real-time data. When aggregated data from multiple devices needs to be retrieved, such as querying the load status of the entire site, the online status of all devices, or user charging orders, the system will read from the regional storage node cluster. Since the regional storage node cluster stores the full-site time-sharded index and some device shards, and typically has stronger processing capabilities and network bandwidth, it can efficiently complete aggregated queries, avoiding frequent access to a large number of local device storage nodes.

[0040] This embodiment proposes a distributed data storage method for field control and charging equipment based on the Elec-Hong system. This method stores the structured operational data of the charging equipment on the local main storage node of the charging equipment, encrypts the core data, and stores it on a core backup node, ensuring that the main storage node and the core backup node are physically isolated and independently powered. Simultaneously, non-real-time aggregated data from the charging equipment is synchronized to a regional storage node cluster at the field control end via the Elec-Hong soft bus. This cluster contains 2-4 redundant storage nodes. Regional-level data from the field control end, including site load, equipment online status, and user charging orders, is also stored in this regional storage node cluster. Subsequently, time-sliced ​​data from the regional storage node cluster is uploaded in batches to a cloud storage cluster via wireless communication. The entire process is monitored by a distributed storage management system, which interacts in real time with the storage nodes of the charging equipment, the regional storage node cluster, and the cloud storage cluster.

[0041] However, in the aforementioned distributed data storage methods, the amount of non-real-time aggregated data and time-sliced ​​data will grow exponentially with the increase in the number of charging devices and the extension of operating time. This unoptimized raw data will consume a large amount of storage space and network bandwidth during storage and transmission, not only increasing the system's operating costs but also potentially leading to low data synchronization efficiency, affecting the response speed and data availability of the entire distributed storage system. Especially at resource-constrained charging device terminals and in wireless communication links with limited network bandwidth, the surge in data volume will become a bottleneck for system performance.

[0042] To effectively address the aforementioned issues, this embodiment proposes using an improved DEFLATE lossless compression algorithm to compress non-real-time aggregated data and time-sliced ​​data. This compression process includes: establishing a global dictionary for fixed fields in the data, mapping pile numbers, parameter units, and equipment models to short codes of 1-2 bytes; employing difference encoding for numerical data, using the first data point within a time period as the baseline value, with subsequent data only storing the difference from the baseline value; and adding a CRC32 checksum to the end of the compressed data.

[0043] Specifically, the improved DEFLATE lossless compression algorithm is a compression method optimized for the characteristics of charging device data based on the standard DEFLATE algorithm. The DEFLATE algorithm combines the LZ77 algorithm (for finding and replacing duplicate strings) and Huffman coding (for variable-length encoding of symbols) to achieve efficient lossless compression. The term "improved" here means that preprocessing or optimization strategies targeting specific data patterns are introduced before or during the application of the DEFLATE algorithm to further improve the compression ratio. For example, customized dictionary construction or encoding strategy adjustments can be made for common repetition patterns and numerical trends in charging data, thereby minimizing data volume without losing any original information.

[0044] During the compression process, a global dictionary is first established for fixed fields in the data. Fixed fields refer to fields in charging device data that have a limited range of values ​​and appear frequently, such as the charging pile number, parameter units, and equipment model. By pre-analyzing the possible values ​​of these fields, a globally shared dictionary is established, mapping each unique fixed field value to a short code of 1-2 bytes. For example, "charging pile A001" can be mapped to "01", "V" (volts) to "0A", and "AC charging pile" to "1B", etc. When these fixed fields appear in the original data, their original strings are no longer stored; instead, the corresponding short codes are stored. This method greatly reduces the storage space occupied by these highly repetitive but long-length fields. This global dictionary can be loaded during system initialization or dynamically built and updated based on data characteristics to adapt to different data scenarios.

[0045] Secondly, differential encoding is used for numerical data. In the operational data of charging equipment, such as charging voltage, charging current, and charging power, the values ​​often exhibit continuous or slightly fluctuating characteristics over a short period. Differential encoding utilizes this characteristic, no longer storing the absolute value of each value, but instead using the first data point within a time period as a baseline value, and storing only the difference between the baseline value and subsequent data. For example, if the first charging current value in a time period is 100A, and subsequent current values ​​are 101A, 99A, and 102A, then 100 (baseline value), +1, -1, and +2 will be stored. Since these differences are usually much smaller than the original values, they can be represented with fewer bits, thus achieving data compression. This encoding method is particularly suitable for time series data and can significantly reduce data redundancy.

[0046] Finally, the compressed data is appended with a CRC32 checksum. CRC32 (Cyclic Redundancy Check 32-bit) is an error detection code widely used in data communication and storage. Before data is compressed and prepared for transmission or storage, a 32-bit checksum is calculated based on the compressed data content and appended to the end of the data block. When data is received or read, the receiver or reader performs another CRC32 calculation on the data content and compares the result with the received checksum. If they match, it is assumed that no errors occurred during transmission or storage; if they do not match, it indicates that the data may have been corrupted or tampered with. This provides a reliable guarantee for data integrity in distributed storage systems.

[0047] In some possible embodiments, the difference encoding is proposed to include: using the first charging current data or charging voltage data in the time period as a reference value, and storing only the deviation value relative to the reference value in subsequent charging current data or charging voltage data; the deviation value carries a positive or negative sign to indicate the direction of increase or decrease.

[0048] Specifically, this scheme aims to provide a unified reference point for subsequent differential encoding. Within a specific time slice or data acquisition period, the system selects the first charging current or charging voltage data point within that period as the baseline value to ensure that all subsequent related data within that period are encoded relative to a common starting point. For example, within a preset time slice, the system records the charging current value at the beginning of that slice as the baseline, and all subsequent charging current values ​​acquired within that slice are compared to this baseline value. This technique is the core of differential encoding; it utilizes the characteristic that charging current and charging voltage typically have a strong correlation or relatively small variation over a short period. By storing the difference between each data point and the baseline value instead of the absolute value, the amount of data that needs to be stored can be significantly reduced. For example, if the baseline value is 20A, and subsequent data are 20.1A, 19.9A, and 20.2A, the stored deviation values ​​are +0.1A, -0.1A, and +0.2A, respectively. These deviation values ​​typically have a smaller numerical range than the original absolute values, thus allowing for representation with fewer bits and achieving data compression. Meanwhile, this scheme ensures the integrity and reversibility of the difference encoding. By including a positive or negative sign in the deviation value, it can accurately indicate whether subsequent data increases or decreases relative to the baseline value. For example, +0.1A represents an increase of 0.1A compared to the baseline value, while -0.1A represents a decrease of 0.1A compared to the baseline value. This is crucial for accurately reconstructing the original data during decompression, avoiding information loss and ensuring data integrity. Furthermore, by encoding the sign bit of the deviation value, storage space can be further optimized, for example, by using two's complement to represent negative numbers or employing variable-length encoding, resulting in higher storage efficiency.

[0049] In some possible embodiments, the compression process is proposed to further include: summing and averaging the parameters repeatedly within a time period where the parameter changes are within a preset threshold range, storing the average value and the amount of data; performing a first-order and a second-order fitting on the charging curve, storing only the fitting function parameters, where the first-order fitting is a linear fitting and the second-order fitting is a polynomial fitting, and the fitting function parameters include the slope and intercept of the first-order fitting and the coefficients of the quadratic term, the coefficients of the first term, and the constant term of the second-order fitting.

[0050] Specifically, for repeated parameters whose changes are within a preset threshold range, the system performs summation and averaging, recording and storing the average value and data volume. This means that when monitoring the operating parameters of the charging equipment, the system continuously evaluates the fluctuations of specific parameters (e.g., voltage, current, temperature, etc. during stable charging) over a period of time. If the magnitude of these parameter changes does not exceed a preset allowable threshold (e.g., ±0.5% or ±1 unit), these parameters are considered relatively stable or repetitive within that period. In this case, the system no longer records all the original data points for that period one by one, but instead sums these data points and calculates their average value, then only stores the average value and the number of original data points it represents. For example, if the charging voltage fluctuates within the range of 220V ± 0.5V for a 10-minute period, the system can record it as an average voltage of 220V for 10 minutes, thus significantly reducing the amount of data stored.

[0051] Simultaneously, the system performs first-order and second-order fitting on the charging curve, storing only the fitting function parameters. First-order fitting is a linear fit, and second-order fitting is a polynomial fit. The fitting function parameters include the slope and intercept of the first-order fit, and the coefficients of the quadratic term, the coefficients of the linear term, and the constant term of the second-order fit. Charging curves, such as curves showing the change of charging current or voltage over time, typically contain a large number of continuous sampling points. Directly storing these points would consume a significant amount of storage space. This embodiment uses mathematical fitting to efficiently represent the entire curve using only a few parameters. When the charging curve exhibits an approximately linear trend (e.g., during the constant current charging stage), the system uses linear fitting (first-order fitting), calculating the slope and intercept of the straight line that best represents the curve using algorithms such as least squares, and storing only these two parameters. For example, for the change of current I over time t, the fitting is I = k... If t+b, then only k and b are stored. When the charging curve exhibits a more complex nonlinear trend (e.g., during the constant voltage charging stage or at the end of charging), the system uses polynomial fitting (quadratic fitting) to calculate the quadratic coefficients, linear coefficients, and constant term of the quadratic polynomial that best represents the curve, and only these three parameters are stored. For example, for the change of current I with time t, the fitting is I=a t^2+b For t+c, only a, b, and c are stored. In this way, a curve that originally required storing a large number of data points to describe can now be described by storing only a few key fitting parameters, greatly improving the data compression ratio.

[0052] In the distributed data storage method for field control and charging equipment based on the Dianhong system, although the data is distributed and stored in the main storage node, core backup node, regional storage node cluster at the field control end, and cloud storage cluster on the local charging equipment, and synchronized and interacted through Dianhong soft bus and wireless communication, if there is a lack of a sound fault tolerance and recovery mechanism, once one or more storage nodes fail, it may lead to data loss, service interruption or reduced system availability, thereby affecting the continuity of charging services and data integrity.

[0053] To address this, this embodiment proposes fault-tolerant recovery steps, aiming to ensure that in a distributed data storage system, when any storage node fails, the system can automatically or semi-automatically perform data recovery and business takeover, thereby guaranteeing data integrity, availability, and service continuity. Its core lies in achieving rapid fault detection, isolation, and recovery through multi-level, multi-path redundancy design.

[0054] Specifically, when the primary storage node fails, the system can automatically switch to the core backup node. Primary storage node failures can be detected through heartbeat mechanisms, periodic health checks, or abnormal data read / write operations. Once a failure is detected, the system immediately triggers an automatic switchover process, redirecting data read / write operations to the core backup node, which pre-stores core data. Because the primary storage node and the core backup node are physically isolated and powered independently, this switchover effectively addresses single points of failure, ensuring the continuous availability of core data.

[0055] In some possible implementations, when both the primary storage node and the core backup node fail, the system will read backup data from the backup nodes of the regional storage node cluster via the Elec-Tech soft bus. In the event of local dual redundancy failure, the system will utilize the efficient and reliable communication mechanism of the Elec-Tech soft bus to request backup data from the regional storage node cluster at the field control center. The backup nodes in the regional storage node cluster typically store aggregated and backup data from multiple charging devices, serving as a secondary recovery source for local storage. Data reading via the Elec-Tech soft bus ensures low latency and high reliability in data transmission.

[0056] Meanwhile, when the primary node in a regional storage node cluster fails, the backup node can automatically take over storage and synchronization tasks. Regional storage node clusters typically employ a primary-backup or multi-active architecture. When the primary node in the cluster fails, the cluster's internal monitoring mechanism detects the primary node's inactivity and automatically elects or designates a backup node to immediately take over the storage services and data synchronization tasks of the original primary node. This automatic takeover mechanism ensures the continuity of regional data storage and processing, avoiding regional service interruptions due to the failure of a single node.

[0057] Based on this, when both the primary and backup nodes in the regional storage node cluster fail, the system downloads regional data within a preset time range from the cloud storage cluster and combines it with local data from each charging device to rebuild the field control storage. This is the highest level of fault tolerance and recovery strategy. When the regional storage node cluster completely fails, the system downloads regional data from the cloud storage cluster for a recent period (preset time range). Simultaneously, it integrates and verifies the data from the local storage nodes of the charging devices that have not yet completely failed (if present) to rebuild the field control storage. This layered recovery strategy leverages the eventual consistency and high availability of cloud storage to ensure data recoverability under extreme conditions.

[0058] Furthermore, after a node reconnects, the system can retransmit missing data via differential synchronization, and the recovery process supports resuming interrupted transmissions. When a faulty node is repaired and reconnected, the system does not perform a full data synchronization. Instead, it uses a differential synchronization mechanism to identify and transmit only the data that was missing or changed during the fault. Differential synchronization can be achieved by comparing the timestamp or version number of the node's local data with the latest global data. Simultaneously, the recovery process supports resuming interrupted transmissions, meaning that after data transmission is interrupted, it can resume transmission from the point of interruption without starting from the beginning, thereby improving recovery efficiency and reliability, especially suitable for scenarios with unstable network environments.

[0059] In some possible embodiments, a distributed storage management system is proposed to monitor the storage occupancy, CPU load, and network bandwidth of each storage node in real time. When the load of the charging device's storage node exceeds a preset threshold, the non-real-time data of the charging device is migrated to a storage node with a lower load via the Elec-Tech soft bus to balance the storage pressure.

[0060] Specifically, the distributed storage management system is configured to continuously collect and analyze operational metrics from all storage nodes in the system (including the primary storage node on the charging device, core backup nodes, regional storage node clusters, and cloud storage clusters). Storage utilization refers to the usage of storage space, CPU load refers to the processor's workload, and network bandwidth refers to data transmission capacity. These metrics are key parameters for evaluating the health and performance of storage nodes. Real-time monitoring can be achieved through various methods such as periodic queries, event-driven reporting, or agent-based collection, ensuring that system administrators or automated mechanisms can understand the operational status of each node in a timely and accurate manner.

[0061] The preset thresholds are upper limits set for metrics such as storage utilization, CPU load, and network bandwidth, based on factors such as system design requirements, hardware performance, expected data volume, and Quality of Service (QoS) standards. When the distributed storage management system detects that any one or more of these metrics for any charging device's storage node exceed these preset thresholds during real-time monitoring, it determines that the node is in a high-load state. The threshold settings need to comprehensively consider system stability and efficiency, avoiding excessive sensitivity leading to frequent migrations, or excessive sluggishness causing performance bottlenecks.

[0062] Data migration refers to transferring a portion of data, particularly non-real-time data, from the charging device's storage nodes to nodes with relatively low load within the regional storage node cluster. Non-real-time data typically refers to data that doesn't require immediate access but still needs long-term storage, such as historical operation logs and statistical reports. The Elec-Tech Softbus, as an efficient and reliable communication mechanism, is responsible for establishing a data transmission channel between the charging device and the regional storage node cluster, ensuring the stability and integrity of the data migration process. The migration process can employ incremental synchronization, full synchronization, or block-based transmission methods and supports breakpoint resumption to cope with network fluctuations and other situations.

[0063] Balancing storage load is the ultimate goal of data migration. By distributing data from high-load nodes to low-load nodes, the storage, computation, and network pressure on individual nodes can be effectively alleviated, preventing system performance degradation, response latency, or even service interruptions caused by localized overload. This load balancing strategy helps optimize the resource utilization of the entire distributed storage system, improves the overall throughput and stability of the system, and ensures the continuous availability of data storage services.

[0064] In some possible embodiments, the charging device is proposed to synchronize non-real-time aggregated data to the regional storage node cluster in real time via the E-Hong soft bus. The synchronization frequency is dynamically adjusted according to the device load. When the device load exceeds a preset ratio, the synchronization frequency is reduced to one minute. The regional storage node cluster uploads the full-time sharded data to the cloud storage cluster in batches every hour. The cloud storage cluster performs CRC32 verification on the received data. After the verification is successful, redundant data is removed, and bidirectional synchronization is maintained with the regional storage node cluster.

[0065] Specifically, non-real-time aggregated data generated locally by the charging equipment, such as daily, weekly, or monthly statistics on charging volume, equipment operating time, and fault statistics, needs to be periodically synchronized to the regional storage node cluster at the site control center for centralized management and analysis. This synchronization is performed via the Elec-Tech soft bus, ensuring the reliability and efficiency of data transmission. The Elec-Tech soft bus can be implemented using message queues, RPC (Remote Procedure Call), or customized communication services based on protocols such as HTTP (HyperTextTransfer Protocol) / MQTT (Message Queuing Telemetry Transport). The data synchronization module at the charging equipment end periodically collects and packages the non-real-time aggregated data and sends it to the regional storage node cluster via the soft bus interface. The regional storage node cluster has a corresponding receiving service to parse and store the received data.

[0066] To optimize network resource utilization and ensure system responsiveness, the data synchronization frequency should not be fixed but dynamically adjusted based on the actual operating load of the charging equipment. When the equipment load is high, system resources (such as CPU and network bandwidth) may be strained. In this case, reducing the synchronization frequency can prevent impact on core charging services. The charging equipment itself or its connected edge computing units continuously monitor the equipment's load, such as the number of active connections to the charging pile, data processing volume, and network traffic. The preset load threshold can be determined based on empirical values ​​or through training a machine learning model. When the monitored load indicators exceed the preset threshold, the synchronization module receives an instruction and automatically extends the data synchronization cycle, for example, from once per minute to once every five minutes, or from once every five minutes to once every ten minutes. When the equipment load exceeds a preset percentage, such as CPU utilization exceeding 80% or the number of concurrent charging sessions exceeding 70% of the maximum capacity, the synchronization frequency adjustment logic is triggered, reducing the synchronization frequency to once per minute to effectively alleviate resource pressure on the system under high load.

[0067] Furthermore, as the aggregation point for field-level data, the regional storage node cluster needs to periodically upload the collected time-sharded data from the entire field to the cloud storage cluster to achieve higher levels of backup, long-term storage, and big data analysis. Choosing "hourly" as the upload cycle balances real-time requirements and network transmission efficiency. The regional storage node cluster has a scheduled task that triggers a data upload operation once per hour. This operation collects all time-sharded data generated or updated in the past hour and packages it into one or more data packets. These data packets can be uploaded to the cloud storage cluster via secure file transfer protocols such as SFTP (SSH File Transfer Protocol) or HTTPS (Hypertext Transfer Protocol Secure) or the cloud service provider's API interface. Batch uploads reduce the overhead of connection establishment and disconnection, improving transmission efficiency.

[0068] To ensure data integrity during transmission, the cloud storage cluster immediately performs a CRC32 (Cyclic Redundancy Check) verification on the data upon receiving it from the regional storage node cluster. CRC32 is a commonly used data integrity verification algorithm that effectively detects data transmission errors. Before uploading data, the regional storage node cluster calculates a CRC32 checksum for the data to be uploaded and sends this checksum along with the data. Upon receiving the data, the cloud storage cluster recalculates the checksum using the same CRC32 algorithm and compares it with the checksum sent with the data. If the two checksums match, the data is considered intact; otherwise, it indicates that an error may have occurred during transmission, requiring retransmission or error handling.

[0069] In cloud storage clusters, to save storage space and improve data retrieval efficiency, received data needs to be deduplicated. After verification, the system identifies and deletes duplicate data records, retaining only unique data instances. Redundant data removal can be achieved in several ways. A common method is to generate a unique hash value (e.g., MD5 or SHA256) for each data record and maintain an index of the hash values ​​of the stored data. When new data is received, its hash value is first calculated, and then the index is checked to see if a duplicate hash value exists. If it exists, the data is considered redundant and is either not stored or the old duplicate data is deleted; if it does not exist, the new data is stored and the hash value index is updated.

[0070] The relationship between the cloud storage cluster and the regional storage node cluster is not merely a one-way data upload; it also maintains bidirectional synchronization. This means that data updates or management operations (such as data cleanup, archiving, and repair) on the cloud storage cluster may also need to be synchronized back to the regional storage node cluster to ensure data consistency. Bidirectional synchronization can be achieved through an incremental synchronization mechanism. The cloud storage cluster records its data change log and periodically synchronizes these changes back to the regional storage node cluster. The regional storage node cluster also maintains a synchronization state, receiving and applying changes from the cloud. This may involve version control and conflict resolution mechanisms to ensure data consistency in both directions.

[0071] During the large-scale development of new energy charging stations, the amount of structured data generated by charging equipment continues to surge, placing higher demands on the security, efficiency, collaboration, and scalability of data storage. Traditional charging stations mostly adopt centralized storage architectures or single storage modes, which suffer from problems such as poor data security, low storage efficiency, insufficient collaboration, and poor scalability. Specifically, this manifests as: server failure leading to the loss of all data at the station, inefficient general compression algorithms and transcoding risks in data interaction, slow data scheduling response, lack of a unified load balancing mechanism, and the need for manual configuration adjustments when adding new equipment.

[0072] In this regard, such as Figure 2 As shown, this embodiment proposes a distributed data storage system for field control and charging equipment based on the Dianhong system. The system includes a local distributed storage module for the charging equipment, a regional storage node cluster for the field control, a cloud storage cluster, a Dianhong soft bus communication module, and a distributed storage management system module. The local distributed storage module for the charging equipment includes a primary storage node and a core backup node. The primary storage node stores compressed real-time operating data, while the core backup node stores encrypted core data. The primary storage node and the core backup node are physically isolated and independently powered. This physical isolation and independent power supply design effectively avoids the risk of single point of failure. When the primary storage node malfunctions, the core backup node can immediately take over critical data services, ensuring the continuous availability of local data on the charging equipment.

[0073] The site control area storage node cluster comprises 2-4 redundant storage nodes for storing area-level data and backing up core data from each charging device. This area-level data includes site load, device online status, and user charging orders. The redundant storage node configuration enables multi-copy data storage; if any node fails, other nodes can automatically take over data services, significantly improving the reliability and access efficiency of area-level data. The cloud storage cluster receives time-sharded data uploaded in batches from the area storage node cluster, performs CRC32 verification and redundant data removal, forming a third set of backup storage. Through the CRC32 verification mechanism, the system verifies data integrity in real time during data upload, removing redundant data and retaining only valid information, thus saving cloud storage space and ensuring the accuracy of remote backup data.

[0074] The Elec-Tech Softbus communication module establishes a low-latency connection between the local distributed storage module on the charging device and the regional storage node cluster on the site control side, supporting heartbeat detection and breakpoint resume mechanisms. This module fully utilizes the low-latency characteristics of the Elec-Tech Softbus, controlling the data synchronization latency between the charging device and the site control side to the millisecond level. The heartbeat detection mechanism can promptly detect communication interruptions, and the breakpoint resume function ensures the continuity of data synchronization during network fluctuations. The distributed storage management system module interacts with each storage node in real time, performing node management, data sharding scheduling, and load balancing. This module continuously monitors the storage occupancy, CPU load, and network status of each node. When it detects that the load of the charging device's storage node exceeds a preset threshold, it automatically migrates non-real-time data to regional storage nodes with lower loads, achieving dynamic optimization of system resources.

[0075] In some possible embodiments, it is proposed that the system also includes an extreme compression processing module. This extreme compression processing module is configured to perform efficient lossless compression processing on various types of data in the system. Its core function is to reduce data storage space occupation, reduce network transmission bandwidth requirements, and improve data processing efficiency by reducing data redundancy and optimizing data representation. This module can be deployed as a standalone software component in the data processing link of each storage node, or implemented as a hardware acceleration unit in specific scenarios.

[0076] The extreme compression processing module employs an improved DEFLATE lossless compression algorithm. The improved DEFLATE lossless compression algorithm used in this embodiment is a version optimized for the characteristics of charging device data, based on the standard DEFLATE algorithm (combining the LZ77 algorithm and Huffman coding). This algorithm aims to achieve a higher compression ratio while ensuring no information is lost during compression. Its improvements may be reflected in customized dictionary construction, encoding strategies more adapted to specific data patterns, and optimized Huffman tree generation.

[0077] The extreme compression processing module comprises a scenario-based optimization layer and a data preprocessing unit. As a component of this module, the scenario-based optimization layer is specifically responsible for applying customized compression strategies based on the specific data type and business scenario. This layer can identify specific fields in the data and optimize them based on their characteristics to achieve compression results superior to general compression algorithms. The data preprocessing unit performs preprocessing operations on the data before it enters the core compression algorithm. This aims to eliminate inherent redundancy in the original data, making it easier for subsequent compression algorithms to process efficiently. Preprocessing operations include aggregating repetitive parameters and fitting curve data.

[0078] The scenario-based optimization layer establishes a global dictionary for fixed fields, mapping the pile number, parameter unit, and equipment model to 1-2 byte short codes. For frequently occurring identifying fields in charging data with fixed value ranges, such as pile number, parameter unit, and equipment model, the system pre-creates or dynamically establishes a global dictionary. Each unique field value in the dictionary is assigned a short code of 1 to 2 bytes. During data storage or transmission, the original string or longer identifier is replaced by its corresponding short code, significantly reducing the data volume. Simultaneously, the scenario-based optimization layer employs difference encoding for numerical data. For continuously changing numerical data, such as charging voltage and charging current, this embodiment uses difference encoding technology. Specifically, the first data point within a time period is used as the baseline value, and subsequent data points only store the difference between them and the baseline value. Since the changes in continuous values ​​are usually small, these differences can be represented with fewer bits, thus achieving effective data compression.

[0079] The data preprocessing unit sums and averages repeated parameters within a preset threshold range for a given period, storing the average value and the amount of data. When a parameter's value remains consistently within the preset threshold range over a period, the system no longer records each data point individually. Instead, it sums and averages all data points within that period, storing only the average value and the number of data points within that period. This effectively reduces redundant recording of stable parameters. Furthermore, the data preprocessing unit performs first-order and second-order fitting of the charging curve. For time-varying curves of voltage, current, etc., during charging, the system does not directly store large amounts of discrete data points. Instead, it approximates these curves using mathematical fitting methods, such as first-order (linear) or second-order (polynomial) fitting. Once the fitting is complete, the system stores only the fitting function parameters, not the original data points, thus accurately reconstructing the charging curve with minimal storage overhead. First-order fitting is linear fitting, and second-order fitting is polynomial fitting. The fitting function parameters include the slope and intercept of the first-order fitting, and the coefficients of the quadratic term, the coefficients of the first term, and the constant term of the second-order fitting.

[0080] The extreme compression processing module establishes a unified compression standard across the entire chain. To ensure compatibility and consistency throughout the distributed storage system (including charging equipment, field control, and cloud) during data compression and decompression, this embodiment establishes a unified compression standard. This standard specifies the compression algorithm, dictionary content, encoding rules, and data format that all nodes must follow, thereby avoiding data parsing problems caused by inconsistent compression standards and simplifying system data flow management. A CRC32 checksum is appended to the compressed data. After data compression, the system appends a CRC32 (Cyclic Redundancy Check) checksum to the end of the compressed data. This checksum is used for integrity checks after data transmission or storage. The receiver or reader can recalculate the CRC32 and compare it with the appended checksum to quickly detect whether errors or corruption occurred during data transmission or storage, thus ensuring data reliability.

[0081] By introducing an extreme compression processing module, this system can efficiently and losslessly compress the massive amounts of data generated by charging equipment. The scenario-based optimization layer significantly reduces the storage space for identifying information such as charging pile numbers and parameter units, as well as numerical data, by establishing a global dictionary of fixed fields and employing differential encoding. The data preprocessing unit further simplifies the data volume by summing and averaging stable parameters and fitting charging curves, avoiding the storage and transmission of redundant data. The establishment of a unified compression standard across the entire chain ensures consistency and interoperability of data formats during transmission and storage between charging equipment, the field control terminal, and the cloud, reducing the complexity of system integration and maintenance. Simultaneously, the CRC32 checksum carried at the end of the compressed data effectively guarantees the integrity and reliability of the data during transmission and storage. These measures work together to significantly reduce the storage pressure and network bandwidth consumption of the distributed storage system, improve data transmission efficiency, and its advantages are even more pronounced in environments with limited wireless communication and edge device resources, thereby optimizing the overall operational performance of the Dianhong system.

[0082] Existing data storage solutions for new energy charging stations suffer from problems such as poor data security, low storage efficiency, insufficient collaboration, and poor scalability when scaled up. For example, centralized storage is prone to single points of failure and lacks multi-layer backup; general compression algorithms are inefficient and data interaction carries transcoding risks; and the failure to fully utilize the eHong soft bus to build collaborative storage results in slow data scheduling response and a lack of unified management and load balancing capabilities.

[0083] To address this, this embodiment proposes a distributed data storage device for field control and charging equipment based on the Elec-Hong system. The core innovation of this device lies in implementing the aforementioned distributed storage method, achieving physical isolation and independent power supply between the local primary storage node and the core backup node of the charging equipment. It utilizes the Elec-Hong soft bus to construct a low-latency collaborative storage network and adopts a redundant architecture of regional storage node clusters. This effectively avoids single-point-of-failure risks, improves data compression efficiency, and achieves dynamic load balancing, thereby ensuring data security, optimizing storage resource utilization, and enhancing system scalability.

[0084] Specifically, the device includes: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the distributed data storage method for field control and charging equipment based on the above-described electric system.

[0085] The embodiments and examples presented herein are provided to best illustrate embodiments of the invention and its particular applications, thereby enabling those skilled in the art to practice and use the invention. However, those skilled in the art will understand that the above description and examples are provided merely for ease of illustration and example. The descriptions presented are not intended to cover all aspects of the invention or to limit the invention to the precise forms disclosed.

[0086] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed data storage method for field control and charging equipment based on an electric system, characterized in that, Includes the following steps: The structured operational data of the charging device is stored in the main storage node of the charging device, and the core data of the charging device is encrypted and stored in the core backup node of the charging device. The main storage node and the core backup node are physically isolated and independently powered. The non-real-time aggregated data of the charging device is synchronized to the regional storage node cluster of the field control terminal via the Elecsoft bus. The regional storage node cluster includes multiple redundant storage nodes. The regional-level data of the site control terminal is stored in the regional storage node cluster. The regional-level data includes site load, equipment online status and user charging orders. The time-sliced ​​data in the regional storage node cluster is uploaded in batches to the cloud storage cluster via wireless communication; The status of each storage node is monitored by a distributed storage management system, which interacts in real time with the storage nodes of the charging device, the regional storage node cluster, and the cloud storage cluster.

2. The distributed data storage method for field control and charging equipment based on the electric power system according to claim 1, characterized in that, The main storage node stores compressed real-time operating data, which includes charging voltage, charging current, charging power, equipment status, fault information, pile number, and parameter units. The core backup node stores encrypted fault records and charging start / stop logs. The primary storage node and the core backup node synchronize data through a built-in protocol.

3. The distributed data storage method for field control and charging equipment based on the electric power system according to claim 1, characterized in that, The distributed storage management system adopts a two-dimensional data sharding strategy of device number and time slice, dividing the entire data into device shards according to device number, and dividing each device shard into time slices in units of 1 hour. The storage nodes of the charging device store its own device fragments and randomly allocated fragments of other devices, and the randomly allocated fragments of other devices are used for disaster recovery. Each node in the regional storage node cluster stores the device shards it is responsible for and the overall time shard index. Data scheduling follows the principle of proximity priority. When calling real-time data of a single charging device, it reads from the local storage node of the charging device. When calling aggregated data of multiple devices, it reads from the regional storage node cluster.

4. The distributed data storage method for field control and charging equipment based on the electric power system according to claim 1, characterized in that, The non-real-time aggregated data and the time-sliced ​​data are compressed using the improved DEFLATE lossless compression algorithm. The compression process includes: establishing a global dictionary for fixed fields in the data, mapping pile number, parameter unit and equipment model to short codes; using difference encoding for numerical data, taking the first data in the time period as the base value, and storing only the difference between the data and the base value in subsequent data; and carrying a CRC32 check code at the end of the compressed data.

5. The distributed data storage method for field control and charging equipment based on the electric power system according to claim 4, characterized in that, The difference encoding includes: The first charging current data or charging voltage data within a time period is used as the reference value, and subsequent charging current data or charging voltage data only store the deviation value relative to the reference value. The deviation value carries a positive or negative sign to indicate the direction of increase or decrease; The compression process also includes: For repeated parameters within a preset threshold range, sum and average the data, record the average value and the amount of data, and store the data. The charging curve is fitted once and twice, and only the fitting function parameters are stored. The first fitting is a linear fitting, and the second fitting is a polynomial fitting. The fitting function parameters include the slope and intercept of the first fitting, and the coefficients of the quadratic term, the coefficients of the first term, and the constant term of the second fitting.

6. The distributed data storage method for field control and charging equipment based on the electric power system according to claim 1, characterized in that, The method also includes a fault-tolerant recovery step: When the primary storage node fails, the system automatically switches to the core backup node. When both the primary storage node and the core backup node fail, backup data is read from the backup node of the regional storage node cluster via the Elec-Tech soft bus. When the primary node in the regional storage node cluster fails, the backup node automatically takes over the storage and synchronization work. When both the primary node and the backup node in the regional storage node cluster fail, regional data within a preset time range is downloaded from the cloud storage cluster and combined with the local data of each charging device to rebuild the field control storage. After the node reconnects, it retransmits the missing data through differential synchronization, and the recovery process supports resuming interrupted transmissions.

7. A distributed data storage system for field control and charging equipment based on an electric system, characterized in that, include: The charging device has a local distributed storage module, which includes a main storage node and a core backup node. The main storage node is used to store compressed real-time running data, and the core backup node is used to store encrypted core data. The main storage node and the core backup node are physically isolated and powered independently. The site control terminal regional storage node cluster includes 2-4 redundant storage nodes, used to store regional data and back up the core data of each charging device. The regional data includes site load, equipment online status and user charging orders. The cloud storage cluster is used to receive time-sliced ​​data uploaded in batches from the regional storage node cluster, perform CRC32 verification and redundant data removal, and form a third set of backup storage. The Elecsoft bus communication module is used to establish a low-latency connection between the local distributed storage module on the charging device side and the regional storage node cluster on the field control side, and supports heartbeat detection and breakpoint resume mechanism. The distributed storage management system module is used to interact with each storage node in real time, and to perform node management, data sharding scheduling and load balancing.

8. The distributed data storage system for field control and charging equipment based on the electric power system according to claim 7, characterized in that, The system also includes an extreme compression processing module, which employs an improved DEFLATE lossless compression algorithm and includes a scenario-based optimization layer and a data preprocessing unit. The scenario-based optimization layer establishes a global dictionary for fixed fields, mapping pile number, parameter unit and equipment model to 1-2 byte short codes, and using difference encoding for numerical data; The data preprocessing unit sums and averages the repeated parameters within a preset threshold range for a single registration, and performs a first and second fitting on the charging curve; the extreme compression processing module formulates a unified compression standard for the entire link, and the compressed data carries a CRC32 check code at the end.

9. A distributed data storage device for field control and charging equipment based on an electric system, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the distributed data storage method for field control and charging equipment based on the electric power system as described in any one of claims 1-6.

10. A computer-readable storage medium storing processor-executable instructions, characterized in that, The processor-executable instructions, when executed by the processor, are used to perform the distributed data storage method for field control and charging equipment based on the electric power system as described in any one of claims 1-6.

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