Energy storage power station cell data shunting acquisition processing method and system

Abstract

The invention provides an energy storage power station cell data shunting acquisition processing method and system, and belongs to the technical field of operation and dispatching automation of a power system. The method comprises the following steps: automatically distinguishing real-time monitoring data and statistical analysis data according to a specific marking rule, carrying out shunting processing on different types of data through a front acquisition shunting technology, uploading the real-time monitoring data to an SCADA (Supervisory Control and Data Acquisition) for processing, storing the real-time monitoring data into a real-time library for real-time monitoring control, and directly storing the statistical analysis data by adopting a time sequence library. By constructing a distributed time sequence library based on virtual nodes, uniform fragmentation storage of data is ensured. Different applications call different data sets for display or analysis through interface configuration, it is ensured that the energy storage energy management system can access mass battery data, the platform data processing pressure is reduced, and the stability of the system is ensured.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and dispatch automation technology, and more specifically, relates to a data diversion, acquisition and processing method for large-scale energy storage power stations. Background Technology

[0002] In electrochemical energy storage systems, each individual cell contains data such as voltage, temperature, and state of charge (SOC). A battery cluster typically contains 200-400 individual cells, and a 200MWh energy storage power station generates over 1 million data points. The primary use of cell data is for battery optimization analysis based on historical data, without needing to handle standard SCADA functions such as limit exceedances and alarms. Furthermore, during battery charging and discharging, the rate of change at these measurement points is extremely high, placing immense pressure on the system's processing capabilities.

[0003] Existing technology document 1 (CN120186215A) discloses a data acquisition method and energy storage system for energy storage systems. Its shortcoming lies in its focus on the field of energy storage system data information technology, achieving decoupling between the acquisition task and the acquisition equipment, but failing to address the issues of big data acquisition and storage in real-time energy storage monitoring systems. Compared to existing technology document 1, this invention differs significantly in that it is proposed within the scope of real-time monitoring systems, belonging to the field of power system operation and dispatch automation technology. The proposed data diversion concept primarily addresses the performance issues of real-time data acquisition and processing. Based on this significant difference, the actual technical effects achieved by this invention include at least the following: Based on the timeliness of data, this invention uses pre-acquisition diversion technology to process different types of data separately; simultaneously, it stores the acquired data in a real-time database or a time-series database, reducing the pressure on real-time data processing and increasing the data access volume of the energy storage monitoring system without changing the hardware configuration.

[0004] Prior art document 2 (CN118673085A) discloses a task execution method, equipment, energy storage equipment analysis system, medium, and product. Its shortcoming lies in that it only stores data in different databases, falling within the scope of data warehouse design, and is not integrated with actual application systems. Compared to prior art document 2, the significant difference of this invention is that it is proposed for the scope of real-time monitoring systems, belonging to the field of power system operation and dispatch automation technology. The proposed data diversion concept mainly addresses the performance issues of real-time data acquisition and processing. Based on this significant difference, the actual technical effects achieved by this invention include at least the following: Based on the timeliness of data, this invention stores data in either a real-time database or a time-series database, reducing the pressure on real-time data processing and increasing the data access volume of the energy storage monitoring system without changing the hardware configuration. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for the offloading and processing of battery cell data in large-scale energy storage power stations. This method automatically distinguishes between real-time monitoring data and statistical analysis data according to specific labeling rules. Through pre-collection and offloading technology, different types of data are processed separately. Real-time monitoring data is sent to SCADA for processing and stored in a real-time database for real-time monitoring and control. Statistical analysis data is directly stored in a time-series database. By constructing a distributed time-series database based on virtual nodes, uniform data sharding is ensured. Different applications can access different datasets for display or analysis through interface configuration, ensuring that the energy storage management system can access massive amounts of battery data, reducing the platform's data processing pressure and ensuring system stability.

[0006] The present invention adopts the following technical solution.

[0007] The first aspect of the present invention provides a method for data diversion, acquisition, and processing of battery cells in an energy storage power station, comprising: The battery cell data is automatically categorized into real-time monitoring data and statistical analysis data according to the set data classification rules. Based on the original communication point tables of various energy storage devices, a configurable point table template is generated through hierarchical configuration and protocol mapping parsing. Based on the relationship between equipment containers, equipment groups are formed, and a preliminary point table and SCADA point table are generated using the point table template. Based on the point table markings, the received real-time data stream is judged and split; Statistical analysis data is stored directly in a time-series library. The split data is hashed according to the configured sharding model to determine its shard. Data from different shards is distributed to the corresponding virtual nodes for concurrent writing, thus building a distributed time-series library based on virtual nodes.

[0008] Preferably, generating a configurable point table template includes: Import the original communication point table templates for various energy storage devices. In the configuration tool, select the hierarchical configuration items according to the point table template. The hierarchical configuration items include: substation, container type, container, channel, and terminal type. Based on the selected container type and container, load the corresponding energy storage device's communication attributes and point table framework; Match the corresponding communication protocol according to the selected channel, map and parse the point table framework of the corresponding energy storage device according to the communication protocol, and extract the unified field information required by the system. Generate a configurable point table template based on the unified field information required by the system.

[0009] Preferably, the generated front point table and SCADA point table include: Based on the relationship between the equipment and containers, energy storage equipment groups are formed; The configurable point table template is applied in batches to the energy storage equipment group, and corresponding data points are created in the energy storage monitoring system. Based on the data classification rules, real-time monitoring data and statistical analysis data are marked in the electricity meter to generate a front-end point table and SCADA point table with tags.

[0010] Preferably, the process of judging and splitting the received real-time data stream includes: The real-time data stream from devices such as PCS and BMS is received by the front-end node of the communication layer. The communication layer queries the tagged front-end table associated with the front-end in the system real-time library and queries the SCADA point tag corresponding to each front-end node. The traffic splitting method is determined based on the SCADA point markers corresponding to each front-end node, and the traffic splitting data queue and front-end change messages are identified. The offloaded data queue and the preceding change message are offloaded to the corresponding location.

[0011] Preferably, determining the diversion data queue and the preceding change message includes: The energy storage monitoring system's front-end protocol receives real-time monitoring data. It determines whether the corresponding data is statistical analysis data by checking if the SCADA point marked by the front-end point in the real-time database can be found. If it is statistical analysis data, it is written into the time-series database submission queue. If the mark indicates that it is real-time monitoring data, the data is encapsulated into a front-end change message.

[0012] Preferably, diverting the offloaded data queue and the preceding change message to the corresponding location includes: After the preceding change message is sent to the processing node corresponding to the SCADA point for SCADA processing, the SCADA processing result is published to the high-speed message bus to form the final real-time database data. The backend service directly submits the split data queue to the distributed time-series library for storage, forming a split data stream.

[0013] Preferably, constructing a distributed time-series library based on virtual nodes includes: Configure virtual nodes, and configure services and assign port numbers, sharding models and storage paths to each virtual node; Statistical analysis data is stored directly in a time series library. When storing split data streams, the time series library client performs hash calculations on the split data according to the configured split model to determine the split to which it belongs, and distributes the data of different splits to the corresponding virtual nodes for concurrent writing. Each virtual node stores the distributed sharded data into a disk array, forming a distributed time-series library based on virtual nodes.

[0014] Preferably, distributing data from different shards to corresponding virtual nodes for concurrent writing includes: Configure the hash function, the number of shards, and the number of virtual nodes.

[0015] The split data stream is stored using a time-series library. When storing split data, all measurement points are processed by a set hash function and modulo operation to calculate the data fragment to which the data belongs, thus obtaining the fragmented data. Different virtual nodes are then called to match the fragmented data. Based on the hash function and modulo operation, the obtained data is assigned to different fragments. The sharded data is submitted concurrently to the matching virtual nodes.

[0016] Preferably, multiple virtual nodes are deployed on physical nodes. When a new physical node is added, the configuration of the virtual nodes and storage paths is modified to migrate the virtual nodes to the new physical nodes.

[0017] A second aspect of the present invention provides a data offloading and processing system for battery cells in an energy storage power station, comprising, according to a method for data offloading and processing for battery cells in an energy storage power station as described in the first aspect: The data differentiation module is used to automatically classify cell data into real-time monitoring data and statistical analysis data according to the set data classification rules. Point table templates are used to generate configurable point table templates based on the original communication point tables of various energy storage devices through hierarchical configuration and protocol mapping parsing. The point table generation module is used to form device groups based on device container relationships, and to generate a front point table and SCADA point table using the point table template. The splitting module is used to judge and split the received real-time data stream based on the point table markings; The output module is used to directly store statistical analysis data in a time series library. It performs hash calculations on the split data according to the configured sharding model to determine the shard to which it belongs, and distributes the data of different shards to the corresponding virtual nodes for concurrent writing, thus building a distributed time series library based on virtual nodes. Compared with the prior art, the beneficial effects of the present invention include at least the following: This invention proposes a method for the offloading and processing of battery cell data in large-scale energy storage power stations. It automatically distinguishes between real-time monitoring data and statistical analysis data according to specific labeling rules. Through pre-collection and offloading technology, different types of data are processed separately. Real-time monitoring data is sent to SCADA for processing and stored in a real-time database for real-time monitoring and control. Statistical analysis data is directly stored in a time-series database. By constructing a distributed time-series database based on virtual nodes, uniform data sharding is ensured. Different applications can access different datasets for display or analysis through interface configuration, ensuring that the energy storage management system can access massive amounts of battery data, reducing the platform's data processing pressure and ensuring system stability. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the implementation process according to the embodiments of the present invention; Figure 2 This is a schematic diagram of the guide point tool provided according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating key information markings provided in accordance with embodiments of the present invention; Figure 4 This is a schematic diagram of front-end data splitting provided according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a virtual node provided according to an embodiment of the present invention; Figure 6 This is a schematic diagram of a distributed time-series library sharding strategy provided according to an embodiment of the present invention; Figure 7 This is a schematic diagram of cell consistency analysis provided according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0020] like Figure 1 As shown, Embodiment 1 of the present invention provides a method for data offloading, acquisition, and processing of battery cells in a large-scale energy storage power station, comprising the following steps: A method for data offloading and processing of battery cells in large-scale energy storage power stations automatically distinguishes between real-time monitoring data and statistical analysis data according to specific labeling rules. Through front-end acquisition and offloading technology, different types of data are processed separately. Real-time monitoring data is sent to SCADA for processing and stored in a real-time database for real-time monitoring and control. Statistical analysis data is directly stored in a time-series database. By constructing a distributed time-series database based on virtual nodes, uniform data sharding is ensured. Different applications access different datasets for display or analysis through interface configuration, ensuring that the energy storage management system can access massive amounts of battery data, reducing the platform's data processing pressure and ensuring system stability. The specific implementation steps and process are as follows: Step 1: Automatically classify battery cell data into real-time monitoring data and statistical analysis data according to the set data classification rules.

[0021] Real-time monitoring data includes: PCS start / stop alarms, AC overvoltage and undervoltage of the power grid, total BMS alarms, total faults, and operating status; statistical analysis data includes: cell temperature and cell voltage.

[0022] Step 2: Obtain point tables for various energy storage devices, including PCS, BMS, transformer substations, auxiliary control equipment, etc. During the import process, the import tool extracts the necessary system information from the energy storage device point tables based on the communication attributes of different types of energy storage devices and the communication protocol point tables. This information includes device type, device number of the same type, description, measurement type, point number, coefficient, naming rules, unit, sequence, JSON_STR, remote adjustment point number, etc., forming a configurable point table template, such as... Figure 1 As shown.

[0023] In a preferred but non-limiting embodiment of the present invention, step 1 includes: Step 2.1: Import the original communication point table templates for various energy storage devices (PCS, BMS, transformer substations, etc.). In the configuration tool, select the hierarchical configuration items according to the point table template, such as substation, container type, container, channel, and terminal type. These selections are hierarchically related; for example, the container type option under a substation will only be updated after a substation is selected, in order to filter and locate the target device.

[0024] Step 2.2: Based on the container type and container selected in Step 2.1, load the communication attributes and point table framework of the corresponding energy storage device.

[0025] Step 2.3: Based on the channel selected in Step 2.1, match the corresponding communication protocol, and map and parse the point table framework of the corresponding energy storage device according to the communication protocol to extract the unified field information required by the system, such as device type, point number, description, coefficient, and measurement type. [1] Step 2.4: Generate a configurable point table template based on the unified field information required by the system obtained in Step 2.3.

[0026] Terminal point establishment involves creating corresponding point tables based on different terminal devices (PCS, BMS, etc.). Substations contain different containers, such as PCS, BMS, and transformer substations, and these containers contain different channels, such as PCS channels and BMS channels.

[0027] Step 3: Form device groups based on device container relationships, and apply the point table template to generate the pre-point table and SCADA point table.

[0028] In a preferred but non-limiting embodiment of the present invention, step 3 includes: Step 3.1: Based on the relationships between energy storage subsystems, bays, and other equipment containers, form an energy storage equipment group, such as, but not limited to, an energy storage equipment group containing 1 PCS, 1 BMS, and 1 transformer substation.

[0029] Step 3.2: Apply the configurable point table template in batches to the energy storage device group, create corresponding data points in the energy storage monitoring system, and mark real-time monitoring data and statistical analysis data according to data classification rules, such as device type, measurement type, SCADA point number, etc. For example, cell data is generally analytical data, so the SCADA point number is set to -1 in the front-end point table to mark it as statistical analysis data for differentiation. Step 3: Associate various energy storage devices and their corresponding point tables, mark key information, and generate marked front-end point tables and SCADA point tables. The front-end point table is configured for the communication layer and loaded into the communication node, while the SCADA point table is configured for the processing layer and loaded into the processing node. Step 4: During real-time communication, data is collected and distributed according to the information processed in Step 3.

[0030] Step 4: Using the pre-acquisition and diversion technology, the real-time monitoring data and statistical analysis data from Step 1 are diverted during the real-time communication process of the energy storage monitoring system to obtain diverted data.

[0031] In a preferred but non-limiting embodiment of the present invention, step 4 includes: Step 4.1: Input real-time data streams from devices such as PCS and BMS. These streams are received by the front-end nodes of the communication layer. The communication layer queries the system's real-time database for the tagged front-end table associated with the front-end node and retrieves the SCADA point tag corresponding to each front-end node.

[0032] Step 4.2: Determine the traffic splitting method based on the SCADA point marker corresponding to each front-end node to obtain the traffic splitting data queue for analysis and the front-end change message for monitoring.

[0033] Step 4.3: Distribute the diversion data queue and the preceding change message to the corresponding location.

[0034] If the SCADA point markers found are real-time monitoring data, they are sent as front-end change messages to the processing layer (SCADA) nodes for real-time calculation, alarm judgment, and other processing. After processing, they are published to the high-speed message bus to form real-time database data. If they are statistical analysis data of the markers, they are directly placed into the time series database submission queue to form a split data stream.

[0035] The energy storage monitoring system's front-end protocol receives real-time monitoring data. It checks if the SCADA point marked with a front-end point in the real-time database can be found to determine if the corresponding data is statistical analysis data. If it is statistical analysis data, it is written to the time-series database submission queue. If the marker indicates it is real-time monitoring data, the data is encapsulated into a front-end change message. The time-series database submission queue is a non-blocking interface. If the data is not subject to offloading, it is processed in the original way, and a front-end change message is sent to SCADA for processing.

[0036] The process of diverting the diverted data queue and the preceding change message to the corresponding location includes: sending the preceding change message to the processing node corresponding to the SCADA point for SCADA processing, and then publishing the SCADA processing result to the high-speed message bus to form the final real-time database data; and having the background service directly submit the diverted data queue to the distributed time-series database for storage to form the diverted data stream.

[0037] Meanwhile, to improve retrieval efficiency, a field `container_id` is added to the column information table of the front-end table. This field is used to fill in the container to which the battery belongs, such as the battery cluster ID to which the battery cell belongs. The `container_id` field is configured as an index field in the real-time database, which can significantly improve the efficiency of conditional queries based on this field. The front-end table also exists in the real-time database. The previously described "finding the SCADA point corresponding to the front-end point" actually involves finding the front-end table in the real-time database and then finding the front-end point.

[0038] Step 5: Statistical analysis data is directly stored in a time-series library. When storing split data streams, the time-series library client performs hash calculations on the split data according to the configured sharding model to determine its sharding. Data from different shards is then distributed to the corresponding virtual nodes for concurrent writing, constructing a distributed time-series library based on virtual nodes. Here, one virtual node is equivalent to "service + sharding + storage," and the relationship between virtual nodes and physical nodes (real hosts or virtual machines) is many-to-one.

[0039] In a preferred but non-limiting embodiment of the present invention, step 5 includes: Step 5.1: Configure virtual nodes. Configure services and assign port numbers, sharding models and storage paths to each virtual node.

[0040] Step 5.2: The statistical analysis data from Step 1 is directly stored in the time series library. When storing split data, the split data is managed by sharding and storing in separate nodes according to the virtual nodes configured in Step 5.1.

[0041] More preferably, step 5.2 includes: Step 5.2.1: Set the hash function, the number of shards, and the number of virtual nodes.

[0042] Step 5.2.2: The statistical analysis data (splitting data) from Step 1 is directly stored in a time-series library. When storing splitting data, all measurement points are processed using a set hash function and modulo operation to calculate the data shards to which the data belongs, resulting in sharded data. Different virtual nodes are then called to match the sharded data. The number of shards is 4, which is manually specified. Then, based on the hash function and modulo operation, the obtained data is assigned to different shards. 'Node' refers to a virtual node.

[0043] Step 5.2.3: Concurrently submit the sharded data to the matching virtual nodes, and then store it in the disk array according to the configured storage path.

[0044] Taking into account the characteristics of time-series libraries, a hash-based partitioning method is adopted. Sharding configuration information is extremely important for the overall algorithm of the distributed time-series library; inconsistencies in configuration across nodes will lead to systemic failures. The background data submission service of the time-series library (located on the client side) employs a multi-process concurrent processing mode. Each process is responsible for submitting data for one shard of the time-series library. All sharded data is submitted concurrently, thus ensuring the overall efficiency of data submission.

[0045] Step 5.3: Deploy multiple virtual nodes on the physical node. When a new physical node is added, migrate the virtual node to the new physical node by modifying the virtual node and storage path configuration.

[0046] More preferably, step 5.3 includes: Multiple virtual nodes can be deployed on a single physical machine. When more physical nodes are added, virtual nodes can be migrated to new nodes by modifying their configurations and storage paths, thereby reducing the performance load on the original nodes. Virtual nodes are independent of each other, each with its own model (sharding), storage path, and port number.

[0047] Different applications access different datasets through interface configurations for display or analysis, enabling the offloading and processing of cell data from energy storage power stations. For monitoring and controlling energy storage devices, real-time database data interfaces are called to ensure the timeliness of monitoring and control applications. For analyzing energy storage devices, time-series database data is used to construct analytical datasets. For example, the battery warning module relies on cell data for cell consistency analysis, and applications directly retrieve historical data from the time-series database for analysis and display.

[0048] like Figure 2 As shown, Embodiment 2 of the present invention provides a battery cell data diversion and acquisition processing system for an energy storage power station, which runs the battery cell data diversion and acquisition processing method for an energy storage power station described in Embodiment 1, including: a data differentiation module, used to automatically differentiate battery cell data into real-time monitoring data and statistical analysis data according to a set data classification rule; Point table templates are used to generate configurable point table templates based on the original communication point tables of various energy storage devices through hierarchical configuration and protocol mapping parsing. The point table generation module is used to form device groups based on device container relationships, and to generate a front point table and SCADA point table using the point table template. The splitting module is used to judge and split the received real-time data stream based on the point table markings; The output module is used to directly store statistical analysis data in a time series library. It performs hash calculations on the split data according to the configured sharding model to determine the shard to which it belongs, and distributes the data of different shards to the corresponding virtual nodes for concurrent writing, thus building a distributed time series library based on virtual nodes.

[0049] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for data diversion, acquisition, and processing of battery cells in an energy storage power station, characterized in that: The battery cell data is automatically categorized into real-time monitoring data and statistical analysis data according to the set data classification rules. Based on the original communication point tables of various energy storage devices, a configurable point table template is generated through hierarchical configuration and protocol mapping parsing. Based on the relationship between equipment containers, equipment groups are formed, and a preliminary point table and SCADA point table are generated using the point table template. Based on the point table markings, the received real-time data stream is judged and split; Statistical analysis data is stored directly in a time-series library. The split data is hashed according to the configured sharding model to determine its shard. Data from different shards is distributed to the corresponding virtual nodes for concurrent writing, thus building a distributed time-series library based on virtual nodes.

2. The method for data diversion, acquisition, and processing of battery cells in an energy storage power station according to claim 1, characterized in that: Generating configurable point table templates includes: Import the original communication point table templates for various energy storage devices. In the configuration tool, select the hierarchical configuration items according to the point table template. The hierarchical configuration items include: substation, container type, container, channel, and terminal type. Based on the selected container type and container, load the corresponding energy storage device's communication attributes and point table framework; Match the corresponding communication protocol according to the selected channel, map and parse the point table framework of the corresponding energy storage device according to the communication protocol, and extract the unified field information required by the system. Generate a configurable point table template based on the unified field information required by the system.

3. The method for data diversion, acquisition, and processing of battery cells in an energy storage power station according to claim 1, characterized in that: The generated front point table and SCADA point table include: Based on the relationship between the equipment and containers, energy storage equipment groups are formed; The configurable point table template is applied in batches to the energy storage equipment group, and corresponding data points are created in the energy storage monitoring system. Based on the data classification rules, real-time monitoring data and statistical analysis data are marked in the electricity meter to generate a front-end point table and SCADA point table with tags.

4. The method for data diversion, acquisition, and processing of battery cells in an energy storage power station according to claim 1, characterized in that: The process of judging and splitting the received real-time data stream includes: The real-time data stream from devices such as PCS and BMS is received by the front-end node of the communication layer. The communication layer queries the tagged front-end table associated with the front-end in the system real-time library and queries the SCADA point tag corresponding to each front-end node. The traffic splitting method is determined based on the SCADA point markers corresponding to each front-end node, and the traffic splitting data queue and front-end change messages are identified. The offloaded data queue and the preceding change message are offloaded to the corresponding location.

5. The method for data diversion, acquisition, and processing of battery cells in an energy storage power station according to claim 4, characterized in that: The determination of the offloaded data queue and the preceding change message includes: The energy storage monitoring system's front-end protocol receives real-time monitoring data. It determines whether the corresponding data is statistical analysis data by checking if the SCADA point marked by the front-end point in the real-time database can be found. If it is statistical analysis data, it is written into the time-series database submission queue. If the mark indicates that it is real-time monitoring data, the data is encapsulated into a front-end change message.

6. The method for data diversion, acquisition, and processing of battery cells in an energy storage power station according to claim 4, characterized in that: Distributing the off-flow data queue and pre-change messages to the corresponding locations includes: After the preceding change message is sent to the processing node corresponding to the SCADA point for SCADA processing, the SCADA processing result is published to the high-speed message bus to form the final real-time database data. The backend service directly submits the split data queue to the distributed time-series library for storage, forming a split data stream.

7. The method for data diversion, acquisition, and processing of battery cells in an energy storage power station according to claim 1, characterized in that: Building a distributed time-series library based on virtual nodes includes: Configure virtual nodes, and configure services and assign port numbers, sharding models and storage paths to each virtual node; Statistical analysis data is stored directly in a time series library. When storing split data streams, the time series library client performs hash calculations on the split data according to the configured split model to determine the split to which it belongs, and distributes the data of different splits to the corresponding virtual nodes for concurrent writing. Each virtual node stores the distributed sharded data into a disk array, forming a distributed time-series library based on virtual nodes.

8. The method for data diversion and acquisition processing of battery cells in an energy storage power station according to claim 7, characterized in that: Distributing data from different shards to corresponding virtual nodes for concurrent writing includes: Configure the hash function, the number of shards, and the number of virtual nodes. The split data stream is stored using a time-series library. When storing split data, all measurement points are processed by a set hash function and modulo operation to calculate the data fragment to which the data belongs, thus obtaining the fragmented data. Different virtual nodes are then called to match the fragmented data. Based on the hash function and modulo operation, the obtained data is assigned to different fragments. The sharded data is submitted concurrently to the matching virtual nodes.

9. The method for data diversion, acquisition, and processing of battery cells in an energy storage power station according to claim 8, characterized in that: Deploy multiple virtual nodes on physical nodes. When a new physical node is added, modify the configuration of the virtual nodes and storage paths to migrate the virtual nodes to the new physical nodes.

10. A data offloading and processing system for battery cells in an energy storage power station, and a data offloading and processing method for battery cells in an energy storage power station according to any one of claims 1-9, characterized in that: The data differentiation module is used to automatically classify cell data into real-time monitoring data and statistical analysis data according to the set data classification rules. Point table templates are used to generate configurable point table templates based on the original communication point tables of various energy storage devices through hierarchical configuration and protocol mapping parsing. The point table generation module is used to form device groups based on device container relationships, and to generate a front point table and SCADA point table using the point table template. The splitting module is used to judge and split the received real-time data stream based on the point table markings; The output module is used to directly store statistical analysis data in a time series library. It performs hash calculations on the split data according to the configured sharding model to determine the shard to which it belongs, and distributes the data of different shards to the corresponding virtual nodes for concurrent writing, thus building a distributed time series library based on virtual nodes.

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