High-performance operation and maintenance monitoring method for energy storage equipment

Abstract

The invention discloses a high-performance operation and maintenance monitoring method for energy storage equipment. The method comprises four steps of data acquisition, data processing, data analysis and data display. Through full-memory processing, multi-task queue and transaction batch updating mechanisms, the event processing efficiency is remarkably improved, and the database load is reduced. The data acquisition supports millisecond-level frequency and breakpoint resume; the data processing generates a structured event based on protocol analysis; data analysis dynamically marks event states and updates the event states in batches; and data display is realized through real-time visual alarm of a webpage end. The method is suitable for a large-scale energy storage system, and efficient and stable operation and maintenance monitoring is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage systems, and in particular relates to a high-performance operation and maintenance monitoring method for energy storage equipment. Background Art

[0002] With the development of energy storage technology and the widespread use of energy storage devices, the need for safety warnings and real-time performance is becoming increasingly critical. As the number of energy storage devices increases, the difficulty of operation and maintenance will also continue to increase. Raw data from edge energy storage devices is collected in real time and parsed according to the protocol to generate clearer event information. During the parsing process, database information must be frequently queried to determine the current event processing method (add, retain, or restore). After processing, the data is updated to the database.

[0003] As on-site energy storage devices are increasingly used, the number of databases will multiply every day, which will lead to two problems: 1) Frequent database operations (real-time queries and updates) occur when analyzing and determining event handling methods. This can lead to rapid degradation of database performance over time, ultimately preventing rapid warnings. 2) When the database performs a large number of data queries, the server CPU will be increased. Most of the time, the CPU exceeds 100%, which seriously affects the performance of the end server and cannot ensure the stable operation of the server.

[0004] Comprehensively evaluating the above solutions, the present invention realizes a method for rapid real-time warning, rapid operation and maintenance without increasing the burden on the server, and ensuring the normal operation of the system. Summary of the Invention

[0005] (1) Technical problems solved In response to the shortcomings of the existing technology, the present invention consists of several parts: data collection + data processing + data analysis + data display. Through full-memory processing, multi-task queues and transaction batch update mechanism, it significantly improves event processing efficiency and reduces database load.

[0006] (2) Technical solution To achieve the above objectives, the present invention provides the following technical solution: a high-performance operation and maintenance monitoring method for energy storage equipment, comprising the following steps: S1. Data collection: Real-time collection of raw data from edge energy storage devices to determine network status. If normal, data is pushed to the data processing queue. If abnormal, data is cached and an alarm is issued. S2. Data processing: Extracting raw data from the data processing queue, parsing it into structured event information based on predefined protocols, and placing the parsed results into the data analysis queue; S3, Data Analysis: Compare event information with historical event lists, dynamically mark status and batch update the database; S4. Data display: Read event information from the database in real time through the web page, and dynamically display it in combination with visualization components.

[0007] As a preferred solution, step S1 is mainly used to collect the original JSON data of the edge energy storage device, with a collection cycle of 10s; during the collection process, the network abnormality of the corresponding energy storage device is judged. If the network is abnormal, the original JSON data is retained, the breakpoint resume mechanism is enabled, and the operation and maintenance personnel are reminded in other modules that the network of a certain edge device is abnormal; if the network is normal, the data is added to the data processing queue.

[0008] As a preferred solution, the parsed protocols in step S2 include BMS and PCS protocols, the protocol information is configured into the database according to the predefined protocol, and parallel processing is achieved through multi-threaded tasks and message queues.

[0009] As a preferred solution, during the parsing process, register addresses and event names are matched through bit operations to generate a structured event list.

[0010] As a preferred solution, the event status marking in step S3 includes the following steps: a) Initialize the memory map and mark the historical event as "restored"; b) Compare the current event with the historical events and update the mapping table status to "new" or "maintain"; c) Commit or rollback database updates in batches through the transaction mechanism.

[0011] As a preferred solution, the event status mark includes a transaction batch update mechanism: Perform a database insert operation on events marked as "new"; Update the last occurrence timestamp of events marked as "keep"; Performs a status field rollback operation on events marked as "recovery".

[0012] As a preferred solution, the web page in step S4 is implemented based on the Vue framework, obtains data in real time through the HTTP interface, and dynamically displays the alarm information in combination with the visualization component.

[0013] As a preferred solution, the web page displays the alarm in real time through a bell icon, which displays event details after clicking and is automatically removed after the event ends.

[0014] (3) Beneficial effects Compared with the prior art, the present invention provides a high-performance operation and maintenance monitoring method for energy storage equipment, which has the following beneficial effects: 1. The method of the present invention includes four steps: data collection, processing, analysis, and display. Through full in-memory processing, multi-task queues, and a transaction batch update mechanism, it significantly improves event processing efficiency and reduces database load. Data collection supports millisecond-level transmission and breakpoint resumability; data processing generates structured events based on protocol parsing; data analysis dynamically marks event status and batch updates; and data display is visualized through a webpage for real-time alerting.

[0015] 2. In the present invention, all data operations are processed in memory, which speeds up event processing and improves event processing efficiency; multi-task queues and multi-task threads are used to process independent data, and data is classified and processed according to attributes, which enhances data processing capabilities. Other data attributes are added later, which facilitates program expansion. Data is updated in batches using transactions, ensuring synchronous and rapid data updates; the method of the present invention is applicable to large-scale energy storage systems and realizes efficient and stable operation and maintenance monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the monitoring method of the present invention; Figure 2 This is a schematic diagram of the network data topology structure of the present invention; Figure 3 This is a schematic diagram of the multi-tasking process of the server of the present invention; Figure 4 This is a schematic diagram of the data processing logic of the data analysis queue of the present invention; Figure 5 This is a schematic diagram of the storage method in data processing of the present invention. DETAILED DESCRIPTION

[0017] In order to better understand the purpose, structure and function of the present invention, the high-performance operation and maintenance monitoring method of energy storage equipment of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. Example 1

[0018] refer to Figure 1-5 The present invention provides a high-performance operation and maintenance monitoring method for energy storage equipment, the method comprising the following steps: S1. Data collection: Real-time collection of raw data from edge energy storage devices to determine network status. If normal, data is pushed to the data processing queue. If abnormal, data is cached and an alarm is issued. S2. Data processing: Extracting raw data from the data processing queue, parsing it into structured event information based on predefined protocols, and placing the parsed results into the data analysis queue; S3, Data Analysis: Compare event information with historical event lists, dynamically mark status and batch update the database; S4. Data display: Read event information from the database in real time through the web page, and dynamically display it in combination with visualization components.

[0019] Specifically, in step S1, data collection: real-time collection of raw data from edge devices to provide a data source for subsequent data processing tasks. This part does not undergo any processing; it is mainly used to collect raw JSON data from edge energy storage devices, with a collection cycle of 10 seconds. During the collection process, the corresponding energy storage device network anomaly is determined. If the network is abnormal, the original JSON data is retained, and the operation and maintenance personnel are reminded in other modules that a certain edge device network anomaly is currently occurring. If the network is normal, the data is added to the data processing queue. For example: Device A1 (port 8888, cabinet number 1) uploads Event: 20, and device A2 (port 8888, cabinet number 2) uploads Event: 30. After collection, they are pushed to the data processing queue.

[0020] In step S2, the protocol information is configured into the database according to the predefined protocol. For example, if it is a BMS protocol, the device type needs to be configured in the database as: [BMS], register address:

[1128] 1) Event name: [BMS-Level 1 pole overtemperature], event name corresponding value: [1] (this value is converted through bit position); 2) Event name: [BMS-Level 1 battery cell overtemperature], event name corresponding value: [2], by parsing the JSON original data, obtain the corresponding register address:

[1128] , corresponding value: [3], compare the data with the configuration information with a cyclic bitwise AND operation, for example, here the value 3&1>0 3&2>0, then the event information at this time is device type: [BMS], register address:

[1128] , specific event name [BMS-Level 1 pole overtemperature, BMS-Level 1 battery cell overtemperature].

[0021] Specifically, the A1 event is BMS-overtemperature single cut-off and PCS-temperature control protection; the A2 event is PCS-undervoltage protection and PCS-overvoltage protection. Through multi-threaded processing, structured events are generated and pushed to the analysis queue.

[0022] In step S3, data analysis: compare the parsed events with the historical event list, dynamically mark the event status (new, keep, restore) through the memory mapping table, and batch update the database. Compare the historical event list, mark A1 event as "new", A2 event as "keep", and batch update the database through transactions. Figure 4 As shown in the figure, the algorithm for comparing parsed events and historical event lists is used. During the batch update process, if the update fails, rollback is used to roll back the data; otherwise, commit is used to commit the data to the database.

[0023] In step S4, the data display reads event information from the database in real time through the web page and dynamically displays it in conjunction with visualization components. The front-end framework currently used is Vue. The web client uses HTTP post to request server-side database data. The requested data is then sent to a small bell on the web page for real-time alerting. Clicking the small bell displays the corresponding site name, event name, and event time. When the event ends, the information is automatically removed from the small bell event list, and historical event information can be queried on other pages. Example 2

[0024] The present invention provides a high-performance operation and maintenance monitoring method for energy storage equipment, which mainly consists of data acquisition, data processing, data analysis and data display.

[0025] Step S1 is primarily used to collect raw JSON data from edge energy storage devices, with a 10-second collection cycle. During the collection process, network anomalies associated with the corresponding energy storage device are detected. If so, the original JSON data is retained, a breakpoint-resume mechanism is enabled, and operations personnel are alerted in other modules of network anomalies associated with a particular edge device. If the network is normal, the data is added to the data processing queue. Step S2 parses the BMS and PCS protocols, configuring the protocol information into a database based on predefined protocols. Parallel processing is achieved through multi-threaded tasks and message queues. Bitwise operations are used to match register addresses with event names to generate a structured event list.

[0026] The event status marking in step S3 includes the following steps: a) Initialize the memory map and mark the historical event as "restored"; b) Compare the current event with the historical events and update the mapping table status to "new" or "maintain"; c) Commit or rollback database updates in batches through the transaction mechanism.

[0027] Specifically, during the data analysis process, the data analysis queue data (Event) is read in real time, and the HisEventList, a list of historical event information currently occurring on all devices, is compared with the Event (the event information currently occurring on the current device): 1) The HisEventList is looped through to find the element that matches the device port and cabinet number in the Event. The event information processing memory map DealInfoMap is initialized and the event processing mode is set to [Restore], recording the port and cabinet number. If the event information in the HisEventList matches the event content in the Event, the DealInfoMap event is updated and set to [Keep]. 2) All event information in the Event is looped through and compared with the event information in the DealInfoMap. If no identical event information is found, the event processing mode is set to [Add]. If the same event information is found, the event is skipped. 3) The database information is updated according to the corresponding event processing mode. It can be understood that the event status marking includes a transaction batch update mechanism: database insert operations are performed on events marked as "Added", the last occurrence timestamp is updated for events marked as "Keep", and the status field rollback operation is performed on events marked as "Restore".

[0028] Furthermore, in step S4, the web page is implemented based on the Vue framework, and data is obtained in real time through the HTTP interface. In combination with the visualization component, the alarm information is dynamically displayed. The web page displays the alarm in real time through the bell icon, which displays the event details after clicking. The alarm is automatically removed after the event ends. All data operations of the present invention are processed in memory, which speeds up the event processing speed and improves the event processing efficiency. In addition, multi-task queues and multi-task threads are used to classify and process data according to attributes, enhancing data processing capabilities. Other data attributes are subsequently added to facilitate program expansion. At the same time, data is updated in batches using transactions, ensuring rapid and synchronous data updates. Example 3

[0029] The present invention provides a high-performance operation and maintenance monitoring method for energy storage equipment, based on an operation and maintenance monitoring system, including the following modules: Data collection module, used to collect edge device data in real time through the Internet of Things protocol; Data processing module, used to parse raw data and generate structured event information; Data analysis module, used to compare historical events and mark status, and perform batch updates of the database; Data display module, used to visualize event information in real time; The message queue module realizes asynchronous communication between modules.

[0030] This system runs on a Linux Tencent Cloud server and consists of multiple task queues and threads to ensure orderly data flow. The task queues cover data collection, processing, and analysis. At site A, there are two energy storage cabinets (A1 and A2). A1 is currently uploading raw data: Event: 20, Port: 8888, Cabinet Number: 1; A2 is currently uploading raw data: Event: 30, Port: 8888, Cabinet Number: 2.

[0031] Specifically, a) Data collection task: the raw data of site A1 and A2 can be collected in real time, and the raw data is directly put into the data processing queue; b) Data processing task: read the data processing queue in a timed loop, and parse the detailed event information of the original data Event of A1 and A2 devices respectively according to the protocol rules. Here, the detailed event information list of A1 device is [BMS-overtemperature single cut, PCS-temperature control protection], and the detailed event information list of A2 device is [PCS-undervoltage protection, PCS-overvoltage protection]. After the parsing is completed, the event information of A1 and A2 devices are placed in the data analysis queue respectively. The specific storage method is as shown in the attached manual. Figure 5 As shown; c) Data analysis task: Read the data analysis queue in a timed loop to obtain each element event. Obtain the list of all device event information in the history that was initialized when the program started (hisEventList) and traverse each element hisEvent. When traversing the hisEventList device event information list, initialize the event processing memory map (dealInfoMap) and set all event processing methods to [Restore]. Analyze the possible scenarios: Scenario 1: If the historical event information of devices A1 and A2 does not exist in the hisEventList list, but there is event information of devices A1 and A2 in the event list, then the information of devices A1 and A2 will be added to the dealInfoMap and the event handling method will be assigned to [Add]. Scenario 2: Loop through the hisEventList to find the historical event information of device A1, hisEvent, and compare whether the event information is the same as the hisEvent information. If they are not the same, perform the following steps: 1) Set the hisEvent event information processing mode of device A1 in dealInfoMap to [Restore]; 2) Add the A1 device event information to the dealInfoMap and set the event handling method to [Add]. Scenario 3: Loop through the hisEventList to find the historical event information of device A1, and compare whether the event information is the same as the hisEvent information. If they are the same, set the processing mode of the hisEvent event information of device A1 in dealInfoMap to [Keep].

[0032] Traverse all event information in dealInfoMap and update the data into the data.

[0033] d) Data display: The web page requests event information from the server. The server program reads the event information from the database and returns it to the web page through the interface in real time for display.

[0034] The method of the present invention realizes module decoupling through three-level queues of data collection, processing and analysis, enhances scalability, and the entire data flow is based on memory, avoiding frequent database interactions and improving processing efficiency. At the same time, a transaction rollback and commit mechanism is adopted to ensure data consistency and reduce server load. It is suitable for large-scale energy storage systems and realizes efficient and stable operation and maintenance monitoring.

[0035] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. A high-performance operation and maintenance monitoring method for energy storage equipment, characterized in that: The steps include: S1. Data collection: Real-time collection of raw data from edge energy storage devices to determine network status. If normal, data is pushed to the data processing queue. If abnormal, data is cached and an alarm is issued. S2. Data processing: Extracting raw data from the data processing queue, parsing it into structured event information based on predefined protocols, and placing the parsed results into the data analysis queue; S3, Data Analysis: Compare event information with historical event lists, dynamically mark status and batch update the database; S4. Data display: Read event information from the database in real time through the web page, and dynamically display it in combination with visualization components.

2. A high-performance operation and maintenance monitoring method for energy storage equipment according to claim 1, characterized in that: Step S1 is mainly used to collect the original JSON data of the edge energy storage device, with a collection cycle of 10s; during the collection process, it is judged that the corresponding energy storage device network is abnormal. If the network is abnormal, the original JSON data is retained, the breakpoint resume mechanism is enabled, and the operation and maintenance personnel are reminded in other modules that the current edge device network is abnormal; if the network is normal, the data is added to the data processing queue.

3. A high-performance operation and maintenance monitoring method for energy storage equipment according to claim 1, characterized in that: The parsed protocols in step S2 include BMS and PCS protocols, and the protocol information is configured into the database according to the predefined protocol, and parallel processing is achieved through multi-threaded tasks and message queues.

4. A high-performance operation and maintenance monitoring method for energy storage equipment according to claim 3, characterized in that ,During the parsing process, the register address and the event name are ,matched through bit operations to generate a structured event list.

5. A high-performance operation and maintenance monitoring method for energy storage equipment according to claim 1, characterized in that The event status marking in step S3 includes the following steps: a) Initialize the memory map and mark the historical event as "restore"; b) Compare the current event with the historical events and update the mapping table status to "new" or "maintain"; c) Commit or rollback database updates in batches through the transaction mechanism.

6. A high-performance operation and maintenance monitoring method for energy storage equipment according to claim 5, characterized in that ,The event status mark includes a transaction batch update mechanism: Perform database insert operations on events marked as "new"; Update the last occurrence timestamp of events marked as "keep"; Performs a status field rollback operation on events marked as "recovery".

7. A high-performance operation and maintenance monitoring method for energy storage equipment according to claim 1, characterized in that ,In the step S4, the web page is implemented based on the Vue framework, ,which obtains data in real time through the HTTP interface, and ,dynamically displays the alarm information in combination with the visual ,component.

8. A high-performance operation and maintenance monitoring method for energy storage equipment according to claim 7, characterized in that ,The web page displays the alarm in real time through a bell icon, which ,will display the event details after clicking it, and will be automatically removed after the event ,ends.

Images