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CN114915033B - Large-scale energy storage power station black box system based on cloud edge cooperation - Google Patents

Large-scale energy storage power station black box system based on cloud edge cooperation Download PDF

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CN114915033B
CN114915033B CN202210673927.6A CN202210673927A CN114915033B CN 114915033 B CN114915033 B CN 114915033B CN 202210673927 A CN202210673927 A CN 202210673927A CN 114915033 B CN114915033 B CN 114915033B
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CN114915033A (en
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张建兴
陈世锋
马帅
丁辉
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Suzhou Yunneng Cube Energy Technology Co ltd
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Abstract

本发明公开了一种基于云边协同的大型储能电站黑匣子系统，属于运行数据记录信息管理系统技术领域，包括黑匣子硬件终端、黑匣子预警录波系统，将黑匣子硬件终端置于单个电池集装箱内部，用于采集电池集装箱内电池管理系统BMS、空调设备、消防设备、视频监控设备、声音采集设备的数据；将黑匣子预警录波系统置于储能电站云平台端或储能电站本地站控平台端，黑匣子预警录波系统为B/S或C/S架构。本发明研制的基于云边协同的大型储能电站黑匣子系统，采用云边协同的系统架构、功能划分和交互机制，具备预警录波一体化的功能，实现了故障录波系统和视频安防写的一体化设计与智能化升级。

The invention discloses a large-scale energy storage power station black box system based on cloud-edge collaboration, which belongs to the technical field of operating data recording information management systems. It includes a black box hardware terminal and a black box early warning wave recording system. The black box hardware terminal is placed inside a single battery container. It is used to collect data from the battery management system BMS, air conditioning equipment, fire protection equipment, video surveillance equipment, and sound collection equipment in the battery container; the black box early warning wave recording system is placed on the cloud platform of the energy storage power station or the local station control platform of the energy storage power station. , The black box early warning wave recording system is B/S or C/S architecture. The large-scale energy storage power station black box system based on cloud-side collaboration developed by this invention adopts the system architecture, functional division and interaction mechanism of cloud-side collaboration, has the function of integrating early warning wave recording, and realizes the integration of fault wave recording system and video security recording. Integrated design and intelligent upgrade.

Description

Large-scale energy storage power station black box system based on cloud edge cooperation
Technical Field
The invention belongs to the technical field of operation data record information management systems, and particularly relates to a cloud-edge cooperation-based large energy storage power station black box system.
Background
In recent years, along with the continuous improvement of the economy of energy storage technologies, the energy storage technologies have increasingly outstanding roles in renewable energy power generation, smart grids and energy internet construction, and policies are continuously put out in China to encourage the construction and application of the energy storage technologies. The large lithium battery energy storage power station for centralized application has the characteristics of high power (several megawatts to hundred megawatts), long continuous discharge time (minutes to hours), multiple functions and the like, and is rapidly developed and applied.
The lithium battery energy storage power station is used as novel electric equipment, a large number of lithium iron phosphate batteries are installed in the lithium battery energy storage power station, complete equipment is burnt after a fire event occurs, and great risks exist in the production and operation processes.
The current lithium battery energy storage power station lacks a strong black box system (Event Data Recorder, EDR for short), only has a separated video security system and a fault recording system, and the existing video security system and fault recording system are independent systems and cannot realize complete early warning, fault recording and accident recall functions. The existing video security system does not have an active early warning function, and intelligent early warning protection cannot be performed in the early stage of an accident; the video security system can only record video data, and lacks synchronous recording of the electrical operation data in the single equipment box body, so that data association analysis cannot be performed after an accident occurs; the traditional electric wave recording equipment has shorter wave recording length, thinner covering granularity and is not suitable for lithium battery energy storage power station equipment with long fault period; the Battery Management System (BMS) in the equipment box body has a partial wave recording function, but the wave recording length is shorter, equipment is burnt after a fire event occurs, and the accident recall analysis function cannot be realized.
The current large lithium battery energy storage power station is generally composed of a battery container, a boosting converter container and station control equipment (according to the condition of being in the container or indoor); the main problems in early warning, fault recording and accident recall are as follows:
1. the electrical quantity data, video data, sound data and other data are not synchronously collected, and data association analysis cannot be realized: the current large lithium battery energy storage power station adopts traditional centralized wave recording system equipment of an electric power system, is independently arranged and installed with equipment such as a station controller and the like, is separately arranged with an energy storage container, records waves of electric and partial battery data of the whole station system, can only record waves of faults of the electric quantity of the whole station, cannot record waves of external environment data such as images, sounds and the like in the single battery container, and cannot realize complete accident recall linkage analysis.
2. Aiming at traditional electrical equipment, the fault wave recording system has short wave recording period before fault and insufficient granularity of covering wave recording data during fault, and can not meet the fault wave recording requirement of a lithium battery energy storage power station; the current large-scale lithium battery energy storage power station adopts traditional centralized wave recording system equipment of an electric power system, is based on a large amount of operation data of a lithium battery, has shorter wave recording wavelength, has thinner wave recording data coverage granularity, and cannot meet the actual application requirement on the data volume provided by accident recall linkage analysis.
3. The most easily problematic of current large-scale lithium battery energy storage power station is the battery container, but the battery container does not install the black box, can't realize the comprehensive record of the inside electric quantity of container, environmental data such as video, sound.
4. The existing large lithium battery energy storage power station battery container is internally provided with a shooting and recording all-in-one machine device, and based on the hidden danger of heating and firing of batteries, cables and the like in the battery container, complete equipment is burnt out if a firing event occurs, so that accident recall of the battery container cannot be realized; the network camera NVR is partially adopted, but only has the functions of storing and forwarding data, and cannot realize the acquisition linkage with the data such as electric quantity and the like; and the system also has no AI analysis and early warning function.
5. The lithium battery management system BMS installed in the current part of battery containers has a simple wave recording function, data only exist in the energy storage battery management system BMS, the wave recording length is short, and the data volume provided for accident recall linkage analysis cannot meet the actual application requirements; and if the BMS equipment is burnt after a fire event occurs, accident recall analysis cannot be realized.
6. Based on the scope of non-traditional electric protection actions such as battery faults, connection line faults and the like in the battery container, the time of occurrence is long, the device has the capability of active early warning through internet of things sensing and intelligent analysis, and current wave recording equipment in the industry does not have the function.
7. The fault wave recording system and the video security system in the industry are independent systems at present, and part of video security system equipment has certain internet-of-things sensing and intelligent analysis functions, but no mature scheme technology exists in the industry aiming at battery container industry products.
8. The existing equipment system does not realize decoupling of software and hardware systems, the existing fault recording system is a closed system, and the product upgrading needs to depend on suppliers completely; the cloud edge architecture system is not adopted, or the adopted cloud edge architecture is only data monitoring, and the reasonable division implementation of the machine learning function and the local real-time reasoning function based on the cloud edge architecture is not realized; the system structure lacks applicability and has weaker functions in early warning analysis.
Therefore, a new black box system is needed to solve the defects that the current large lithium battery energy storage power station is limited by the traditional power equipment fault recording system scheme and the video security system scheme.
Disclosure of Invention
The invention provides a cloud-edge-collaboration-based large energy storage power station black box system, which solves the problem that EDR cannot realize complete early-stage early warning, fault wave recording and accident recall functions at present. The cloud-edge-cooperative-based large energy storage power station black box system disclosed by the invention adopts a cloud-edge-cooperative system architecture, a function division and an interaction mechanism, has the function of early warning and wave recording integration, and realizes the integrated design and intelligent upgrading of a fault wave recording system and video security and writing.
The invention provides the following technical scheme:
a black box system of a large energy storage power station based on cloud edge cooperation comprises a black box hardware terminal and a black box early warning and wave recording system, wherein the black box hardware terminal is arranged in a single battery container and is used for collecting data of a battery management system BMS, air conditioning equipment, fire protection equipment, video monitoring equipment and sound collecting equipment in the battery container; and placing the black box early-warning wave-recording system at the cloud platform end of the energy storage power station or the local station control platform end of the energy storage power station, wherein the black box early-warning wave-recording system is in a B/S or C/S architecture.
Further, when the black box early warning and wave recording system is arranged at the cloud platform end of the energy storage power station, the black box hardware terminal is connected with the black box early warning and wave recording system through 4G; when the black box early-warning wave recording system is arranged at the local station control platform end of the energy storage power station, the black box hardware terminal is connected with the black box early-warning wave recording system through a LAN.
Further, the black box hardware terminal is connected with the battery management system BMS through a LAN network cable; the black box hardware terminal is connected with air conditioning equipment and fire fighting equipment through an RS485 communication line; and the black box hardware terminal is connected with the video monitoring equipment and the sound acquisition equipment through the LAN network cable.
Further, the functional module of the black box hardware terminal comprises an electric quantity acquisition and processing module, a fire-fighting air conditioner acquisition and processing module, a video sound acquisition and processing module, a data filtering and cleaning module, a data storage module, a data forwarding module, an AI analysis and early warning module and a fault wave recording module.
Furthermore, the black box hardware terminal adopts an open architecture on a software architecture, and comprises bottom hardware, a system kernel, an edge computing SDK and a container; the container comprises a plurality of APP, including collection APP, forwarding APP, early warning APP.
Furthermore, the black box hardware terminal adopts a mode of combining high-frequency data acquisition, long-period low-frequency storage and short-period high-frequency storage aiming at battery management system BMS, air conditioning equipment and fire fighting equipment data on a wave recording mechanism; when no fault exists, the long period low frequency storage is that the time is longer than 60 minutes, 1 data is stored every 5 minutes, and when the time is not longer than 60 minutes, the short period high frequency storage is that 1 data is stored every 5 seconds; when faults occur, a short-period high-frequency acquisition and storage mode is started, and high-frequency data acquisition is performed in real time (1 time per 1 second).
Further, the black box hardware terminal aims at the data of the battery management system BMS, the air conditioning equipment and the fire fighting equipment on the wave recording mechanism: when no fault exists, the black box hardware uploads 1 time long period low frequency storage data or short period high frequency storage data to the black box early warning wave recording system every 5 minutes, the black box early warning wave recording system stores the long period low frequency storage data, and the short period high frequency storage data only keeps the data in the last short period time; when faults occur, the black box hardware terminal uploads short-period high-frequency acquisition data to the black box early-warning wave recording system in real time, and the black box early-warning wave recording system synchronously stores the short-period high-frequency acquisition data until the accident ends.
Further, the black box hardware terminal aims at the data of the video monitoring equipment and the sound equipment on a wave recording mechanism: when no fault exists, the black box hardware terminal collects video and sound data in real time, 1 data is uploaded to the black box early warning and recording system every 5 minutes, and the machine only stores data in 60 minutes in a short period; when faults occur, the black box hardware terminal uploads video and sound collected data to the black box early-warning wave recording system in real time, and the black box early-warning wave recording system synchronously stores short-period high-frequency collected data until accidents are over.
Furthermore, the black box early warning and wave recording system adopts an AI machine learning algorithm to carry out linkage analysis and algorithm iteration on all uploaded data according to different functional module requirements of the active early warning, and transmits the learning result to a black box hardware terminal in real time through high-speed communication, the AI analysis and early warning module of the black box hardware terminal receives the learning result, calculates a corresponding active early warning result, and the active early warning result can be output to a monitoring and energy management system EMS or related control protection device in a related container to be used as a system-level backup protection.
Furthermore, the black box early warning and wave recording system can continuously introduce a new trained early warning model aiming at fragmented potential safety hazard scenes according to the development of an AI algorithm technology, and continuously enhance corresponding intelligent analysis capability, so that the system has continuous online upgrading capability.
The beneficial effects of the invention are as follows:
(1) The invention is based on the multi-source heterogeneous data integrated acquisition, storage and transmission black box hardware terminal:
the invention discloses a cloud-edge-cooperative-based large energy storage power station black box system, which adopts a cloud-edge-cooperative system architecture, a function division and an interaction mechanism, and has the function of early warning and wave recording integration.
(2) The invention is based on edge calculation SDK, container technology and black box hardware terminal supporting cloud edge system iteration upgrade: the black box hardware terminal is designed, so that the data of a battery management system BMS, air conditioning equipment, fire protection equipment, video monitoring equipment and sound acquisition equipment in a battery container can be collected simultaneously, the integrated integration of functions of a plurality of equipment is realized, and the linkage analysis of multi-source heterogeneous data is supported; the black box hardware terminal adopts the edge-based SDK and container technology, supports real-time iterative upgrade of the cloud edge system, realizes decoupling between the software and hardware systems, breaks through the closed architecture of the original software and hardware systems, and realizes compatible application of different software function modules APP in the system.
(3) The invention adopts a data storage and fault wave recording mechanism combining short period with low frequency and high frequency: the invention designs a fault wave recording mechanism, combines the characteristic of long accident occurrence duration of the battery container, adopts long and short period distinction, and low-frequency high-frequency acquisition of different data storage and fault wave recording mechanisms, meets the long-term data requirement and the coverage granularity of short period wave recording data, reduces the workload of conventional high-frequency acquisition and storage of the system, and realizes the effective capture of the data before and after the system faults.
(4) The invention supports a cloud-edge collaborative black box system fault wave recording mechanism: according to the invention, cloud edge cooperation mechanisms of different interval frequencies stored by a black box hardware terminal and stored by a black box early warning and recording system platform and combining local recording and platform system recording are adopted, and the concept of a software black box is adopted, so that complete equipment is prevented from being burnt after a fire event occurs, and pain points of accident recall cannot be realized.
(5) The invention supports cloud-edge cooperative system architecture, interaction mechanism and AI analysis and early warning functions: according to the cloud-edge collaborative system architecture and interaction mechanism, the closed architecture of a traditional system is broken, the functional fusion of a cloud machine learning algorithm and local real-time reasoning is realized, the technical level of a black box system is changed from a technology which can only depend on goods supply at one time to a technology which can be based on continuous iterative updating of multi-source heterogeneous data linkage analysis and AI learning algorithm, the local real-time reasoning is performed, and the intelligent level of the system is improved. Not only can fault recording and accident recall be realized, but also AI analysis and early warning functions can be synchronously realized.
The intelligent upgrading method can be further extended to other battery energy storage equipment application scenes, the intelligent upgrading of the existing system can be realized through a cloud edge architecture and a cloud edge cooperative mechanism, and a solution idea is provided for an intelligent early warning and wave recording system of the energy storage equipment.
Drawings
In order to more clearly illustrate the embodiments of the present invention, the drawings that are required for the description of the embodiments will be briefly described below, it being apparent that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a schematic diagram of a black box hardware terminal system architecture according to the present invention.
FIG. 2 is a schematic diagram of a black box warning recording system according to the present invention.
Fig. 3 is a schematic diagram of functional module division of a black box hardware terminal according to the present invention.
Fig. 4 is a schematic diagram of a software architecture of the black box hardware terminal of the present invention.
Fig. 5 is a schematic diagram of an AI analysis early warning module of the black box hardware terminal of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.
Examples
The invention discloses a cloud-edge cooperation-based large energy storage power station black box system, which comprises a black box hardware terminal and a black box early warning and wave recording system, and is shown with reference to figures 1-5.
As shown in fig. 1, an energy storage power station black box hardware terminal is deployed in a single battery container, and data of a Battery Management System (BMS), air conditioning equipment, fire protection equipment, video monitoring equipment and sound collection equipment in the battery container are collected at the same time; the black box hardware terminal is connected with the battery management system BMS through a LAN (local area network) network cable, is connected with the air conditioning equipment and the fire-fighting equipment through an RS485 communication line, and is connected with the video monitoring equipment and the sound collecting equipment through the LAN network cable.
It should be noted that, the specific connection mode between the black box hardware terminal and the device in the container is not limited to the above-mentioned connection interface and cable type.
As shown in fig. 3, the functional modules of the black box hardware terminal are provided with an electric quantity acquisition and processing module, a fire-fighting air conditioner acquisition and processing module, a video sound acquisition and processing module, a data filtering and cleaning module, a data storage module, a data forwarding module, an AI analysis and early warning module (early warning algorithm upgrading and local real-time reasoning based on cloud deep learning training) and a fault wave recording module.
It should be noted that, the device of the black box hardware terminal function module is not limited to the module, and can be extended according to the system function requirement.
As shown in fig. 4, the black box hardware terminal adopts an open architecture on a software architecture, adopts an open architecture of bottom hardware, a system kernel, edge computing SDKs and containers (formed by a plurality of APPs, collecting APPs, cleaning APPs, forwarding APPs and early warning APPs), and realizes a flat, flexible and efficient system architecture based on software definition; the decoupling between the software and the hardware can be realized, different APP are adopted aiming at different software functional modules, the software functional modules can be flexibly configured and effectively utilized, and innovative software algorithms of different manufacturers can be adopted on the same hardware, so that the application requirements of black box systems with various forms are fully met.
The black box hardware terminal is on a wave recording mechanism:
(1) Aiming at the data of a battery management system BMS, air conditioning equipment and fire fighting equipment, a mode of combining high-frequency data acquisition, long-period low-frequency storage and short-period high-frequency storage is adopted; when no fault exists, the collected data is preprocessed through data filtering and cleaning, long-period data (outside 60 minutes) are stored in a low frequency (1 time every 5 minutes), and data in a short period time (within 60 minutes) are stored in a high frequency (1 time every 5 seconds); when the black box hardware terminal judges that the fault occurs and starts wave recording, a short-period high-frequency acquisition and storage mode is started, and data are acquired in real time (1 time every 1 second) and stored locally.
Data for battery management system BMS, air conditioning equipment, fire fighting equipment: when no fault exists, the data filtering and cleaning are carried out, after the collected data are preprocessed, the black box hardware terminal uploads long-period stored data and short-period high-frequency collected data to a black box early-warning and wave-recording system at regular intervals (1 time every 5 minutes), the black box early-warning and wave-recording system stores the long-period stored data, and the short-period high-frequency collected data only keeps the data in the last short period time; when the black box hardware terminal judges that the fault occurs and starts wave recording, the black box hardware terminal uploads short-period high-frequency acquisition data to the black box early-warning wave recording system in real time, and the black box early-warning wave recording system synchronously stores the short-period high-frequency acquisition data until the fault is over.
(2) Data for video monitoring equipment, sound equipment: when no fault exists, the black box hardware terminal collects video and sound data in real time, and the video and sound data are uploaded to a black box early warning and recording system at intervals of 1 time every 5 minutes; the local machine only stores and stores data in a short period time (within 60 minutes); when the black box hardware terminal judges that the fault occurs and starts wave recording, the black box hardware terminal uploads video sound acquisition data to the black box early-warning wave recording system in real time, and the black box early-warning wave recording system synchronously stores short-period high-frequency acquisition data until the accident is over.
As shown in fig. 2, an energy storage power station black box early warning and wave recording system of a B/S or C/S architecture is deployed at an energy storage power station cloud platform end or an energy storage power station local station platform end; when the cloud deployment is carried out, the black box hardware terminal is connected with the black box early warning and wave recording system through the 4G; and when the system is deployed locally, the black box hardware terminal is connected with the black box early warning and wave recording system through the LAN.
It is worth to say that the black box early warning and recording system can adopt B/S or C/S architecture, and the realization of the system functions is not affected.
The black box early warning and wave recording system adopts an AI machine learning algorithm to carry out linkage analysis and algorithm iteration on all uploaded data according to different functional module requirements of active early warning, and downloads learning results to a black box hardware terminal in real time through high-speed communication, as shown in fig. 5, an AI analysis early warning module (diagnosis algorithm upgrading and local real-time reasoning based on cloud machine learning training) in the black box hardware terminal receives the learning results, carries out further local real-time reasoning, and calculates corresponding active early warning results; the active early warning result can be output to the monitoring and energy management system EMS or related control protection equipment in the related container to be used as a system level backup protection.
The black box early warning and wave recording system can continuously introduce a new trained early warning model aiming at fragmented potential safety hazard scenes according to the development of the AI algorithm technology, and continuously enhance the corresponding intelligent analysis capability, so that the system has continuous online upgrading capability.
It should be noted that the active early warning algorithm of the black box early warning and recording system is not limited to a single machine learning algorithm. The cloud edge cooperative work of the black box hardware terminal and the black box early warning and recording system is realized by the two aiming at the realization of different active early warning algorithms, and the principle is that the work of machine learning and the like which needs to consume a large amount of computation resources is finished in the black box early warning and recording system, and the work of light weight computation and real-time reasoning is finished in the black box hardware terminal; the workload of the two is not fixed, and the workload is required to be configured according to different AI algorithms.
The large energy storage power station black box system based on cloud edge cooperation provided by the invention is described in detail. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.

Claims (7)

1. Large-scale energy storage power station black box system based on cloud limit is cooperated, its characterized in that: the system comprises a black box hardware terminal and a black box early warning and wave recording system, wherein the black box hardware terminal is arranged in a single battery container and is used for collecting data of a battery management system BMS, air conditioning equipment, fire protection equipment, video monitoring equipment and sound collecting equipment in the battery container; the black box early-warning wave-recording system is arranged at the cloud platform end of the energy storage power station or the local station control platform end of the energy storage power station, the black box early-warning wave-recording system is in a B/S or C/S architecture, and when the black box early-warning wave-recording system is arranged at the cloud platform end of the energy storage power station, the black box hardware terminal is connected with the black box early-warning wave-recording system through 4G; when the black box early-warning and wave-recording system is arranged at the local station control platform end of the energy storage power station, a black box hardware terminal is connected with the black box early-warning and wave-recording system through a LAN, and the black box hardware terminal is connected with a battery management system BMS through a LAN network cable; the black box hardware terminal is connected with air conditioning equipment and fire fighting equipment through an RS485 communication line; the black box hardware terminal is connected with the video monitoring equipment and the sound collecting equipment through the LAN network cable, and the functional module of the black box hardware terminal comprises an electric quantity collecting and processing module, a fire-fighting air conditioner collecting and processing module, a video sound collecting and processing module, a data filtering and cleaning module, a data storage module, a data forwarding module, an AI analysis and early warning module and a fault wave recording module.
2. The cloud-edge collaboration-based large energy storage power station black box system of claim 1, wherein: the black box hardware terminal adopts an open architecture on a software architecture, and comprises bottom hardware, a system kernel, an edge computing SDK and a container; the container comprises a plurality of APP, including collection APP, forwarding APP, early warning APP.
3. The cloud-edge collaboration-based large energy storage power station black box system of claim 1, wherein: the black box hardware terminal adopts a mode of combining high-frequency data acquisition, long-period low-frequency storage and short-period high-frequency storage aiming at battery management system BMS, air conditioning equipment and fire fighting equipment data on a wave recording mechanism; when no fault exists, the long period low frequency storage is that the time is longer than 60 minutes, 1 data is stored every 5 minutes, and when the time is not longer than 60 minutes, the short period high frequency storage is that 1 data is stored every 5 seconds; when faults occur, a short-period high-frequency acquisition and storage mode is started, and real-time high-frequency data acquisition is performed.
4. The cloud-edge collaboration-based large energy storage power station black box system of claim 1, wherein: the black box hardware terminal aims at the data of a battery management system BMS, air conditioning equipment and fire fighting equipment on a wave recording mechanism: when no fault exists, the black box hardware uploads 1 time long period low frequency storage data or short period high frequency storage data to the black box early warning wave recording system every 5 minutes, the black box early warning wave recording system stores the long period low frequency storage data, and the short period high frequency storage data only keeps the data in the last short period time; when faults occur, the black box hardware terminal uploads short-period high-frequency acquisition data to the black box early-warning wave recording system in real time, and the black box early-warning wave recording system synchronously stores the short-period high-frequency acquisition data until the accident ends.
5. The cloud-edge collaboration-based large energy storage power station black box system of claim 4, wherein: the black box hardware terminal aims at video monitoring equipment and sound equipment data on a wave recording mechanism: when no fault exists, the black box hardware terminal collects video and sound data in real time, 1 data is uploaded to the black box early warning and recording system every 5 minutes, and the machine only stores data in 60 minutes in a short period; when faults occur, the black box hardware terminal uploads video and sound acquisition data to the black box early warning and recording system in real time, and the black box early warning and recording system synchronously stores short-period high-frequency acquisition data until the accident ends.
6. The cloud-edge collaboration-based large energy storage power station black box system of claim 2, wherein: the black box early warning and wave recording system adopts an AI machine learning algorithm to carry out linkage analysis and algorithm iteration on all uploaded data according to different functional module requirements of active early warning, and transmits learning results to a black box hardware terminal in real time through high-speed communication, an AI analysis early warning module of the black box hardware terminal receives the learning results, calculates corresponding active early warning results, and the active early warning results can be output to relevant control protection equipment in a monitoring and energy management system EMS or an associated container to be used as a system level backup protection.
7. The cloud-edge collaboration-based large energy storage power station black box system of claim 6, wherein: according to the black box early warning and wave recording system, new trained early warning models can be continuously imported according to the development of the AI algorithm technology and aiming at fragmented potential safety hazard scenes, and corresponding intelligent analysis capability is continuously enhanced, so that the system has continuous online upgrading capability.
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