Thermal disaster alarm system of lithium ion battery energy storage power station and electronic equipment

Abstract

The invention discloses a thermal disaster alarm system and electronic equipment for a lithium ion battery energy storage power station, and relates to the technical field of thermal disaster monitoring, and the system comprises a power station dividing module which is used for dividing the lithium ion battery energy storage power station into M thermal disaster risk control monitoring areas; the node configuration module is used for configuring M edge thermal runaway prediction nodes; the thermal runaway suppression decision module is used for outputting a real-time thermal runaway prevention and control strategy; the decision generation module is used for generating a real-time fire extinguishing risk control decision according to the evolution result; the risk level mapping module is used for obtaining a real-time risk level; and the alarm signal sending module is used for sending the thermal disaster alarm signal to a power station operation and maintenance center. The technical problem that the lithium ion battery energy storage power station fire hazard risk early warning and prevention and control response speed is low is solved, and the technical effects of accurate thermal runaway risk prediction and timely prevention and control based on edge node real-time prediction and cloud decision optimization and improvement of power station fire hazard early warning and emergency response efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal disaster monitoring, and in particular to a thermal disaster alarm system and electronic equipment for a lithium-ion battery energy storage power station. Background Art

[0002] With the transformation of the global energy structure, energy storage technology has become one of the key technologies to support the development of renewable energy. In particular, lithium-ion battery energy storage systems have shown broad application prospects in regulating grid loads, improving power quality, and providing backup power. Lithium-ion battery energy storage power stations have become an important part of modern power systems due to their high efficiency and high-density power storage capacity. However, with the continuous increase in the capacity and power density of energy storage power stations, the safety issues of lithium-ion batteries have gradually emerged. Lithium-ion batteries may experience thermal runaway when encountering extreme use conditions, equipment failures, or environmental changes. This process is usually accompanied by the release of a large amount of heat and toxic gases, which may cause fire or explosion accidents in severe cases, causing major safety hazards. For example, the frequent lithium-ion battery fire accidents in recent years have attracted widespread attention and exposed the weak links in fire prevention and control of current energy storage power stations. Existing thermal disaster alarm systems often have problems such as untimely response, inability to accurately predict the trend of fire spread, and difficulty in making effective responses quickly. These systems fail to make full use of advanced edge computing and cloud decision-making technologies, resulting in the inability to conduct real-time and accurate risk assessment and emergency decision-making when thermal runaway events occur. Summary of the invention

[0003] The present application provides a thermal disaster alarm system and electronic equipment for a lithium-ion battery energy storage power station, which are used to solve the technical problems of slow fire risk warning and prevention and control response speed in a lithium-ion battery energy storage power station.

[0004] In view of the above problems, the present application provides a thermal disaster alarm system and electronic equipment for a lithium-ion battery energy storage power station.

[0005] In a first aspect of the present application, a thermal disaster alarm system for a lithium-ion battery energy storage power station is provided, the system comprising: The power station division module is used to divide the lithium-ion energy storage power station into M thermal disaster risk control monitoring areas through the evolution of thermal runaway propagation; the node configuration module is used to perform risk control sensitivity analysis on the M thermal disaster risk control monitoring areas, and configure M edge thermal runaway prediction nodes in the M thermal disaster risk control monitoring areas according to the analysis results, wherein the M edge thermal runaway prediction nodes are connected to the thermal disaster alarm decision cloud for communication; the thermal runaway suppression decision module is used for the thermal disaster alarm decision cloud to receive and perform thermal runaway suppression according to the M real-time thermal runaway risk features transmitted back by the M edge thermal runaway prediction nodes. A decision-making module is used to output a real-time thermal runaway prevention and control strategy, wherein the real-time thermal runaway prevention and control strategy has a prevention and control time window identifier; a decision-making generation module is used to perform thermal runaway prevention and control evolution based on the real-time thermal runaway prevention and control strategy, and generate a real-time fire extinguishing risk control decision according to the evolution result; a risk level mapping module is used to traverse the risk level mapping table using the prevention and control time window identifier and the real-time fire extinguishing risk control decision to obtain the real-time risk level; an alarm signal sending module is used to package the real-time risk level, the real-time thermal runaway prevention and control strategy and the real-time fire extinguishing risk control decision into a thermal disaster alarm signal and send it to the power station operation and maintenance center.

[0006] A second aspect of the present application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing a thermal disaster alarm system for a lithium-ion battery energy storage power station when executing the executable instructions stored in the memory.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: Through the evolution of thermal runaway propagation, the lithium-ion energy storage power station is divided into M thermal disaster risk control monitoring areas; risk control sensitivity analysis is performed on the M thermal disaster risk control monitoring areas, and M edge thermal runaway prediction nodes are configured in the M thermal disaster risk control monitoring areas according to the analysis results; the thermal disaster alarm decision cloud receives and makes thermal runaway suppression decisions based on the M real-time thermal runaway risk characteristics transmitted back by the M edge thermal runaway prediction nodes, and outputs real-time thermal runaway prevention and control strategies; thermal runaway prevention and control evolution is performed based on the real-time thermal runaway prevention and control strategies, and real-time fire extinguishing risk control decisions are generated according to the evolution results; the prevention and control time window identifier and real-time fire extinguishing risk control decision traverse the risk level mapping table to obtain the real-time risk level; the real-time risk level, real-time thermal runaway prevention and control strategy and real-time fire extinguishing risk control decision are packaged as a thermal disaster alarm signal and sent to the power station operation and maintenance center. The technical effect of accurate thermal runaway risk prediction and timely prevention and control based on edge node real-time prediction and cloud decision optimization is achieved, and the efficiency of fire warning and emergency response in power stations is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of the process flow of a thermal disaster alarm system for a lithium-ion battery energy storage power station provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: power station division module 11, node configuration module 12, thermal runaway suppression decision module 13, decision generation module 14, risk level mapping module 15, alarm signal sending module 16, processor 21, memory 22, input device 23, output device 24. DETAILED DESCRIPTION

[0012] The present application provides a thermal disaster alarm system and electronic equipment for a lithium-ion battery energy storage power station, which is used to solve the technical problems of slow fire risk warning and prevention and control response speed in a lithium-ion battery energy storage power station.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a thermal disaster alarm system for a lithium-ion battery energy storage power station, the system comprising: The power station division module 11 is used to divide the lithium-ion energy storage power station into M thermal disaster risk control monitoring areas through the evolution of thermal runaway propagation.

[0015] Specifically, the power station division module 11 first divides the lithium-ion energy storage power station into M thermal disaster risk control monitoring areas according to the possible thermal runaway expansion of the battery in different areas through the thermal runaway propagation evolution method. Specifically, when the battery pack has thermal runaway, a large amount of heat will be released, and may trigger a chain reaction of the surrounding batteries. This process spreads gradually. Therefore, by analyzing the temperature gradient between battery packs, it is possible to predict which areas are most likely to have thermal runaway, and then divide different monitoring areas. To this end, the system terminal combines different historical energy storage scenarios and analyzes the temperature gradient between battery packs through a thermal runaway propagation model to identify and divide M thermal disaster risk control monitoring areas. The division of each area helps to conduct targeted risk assessment and monitoring according to the different working conditions of the battery and the environment, so as to timely discover potential thermal runaway risks and avoid disasters such as fire or explosion.

[0016] In a possible implementation, the lithium-ion energy storage power station is divided into M thermal disaster risk control monitoring areas through the evolution of thermal runaway propagation. The system includes: The risk control factors of the lithium-ion energy storage power station are mined to obtain K thermal disaster risk control factors; the risk control monitoring points are located in the lithium-ion energy storage power station according to the K thermal disaster risk control factors, and a power station monitoring plan is generated, wherein the power station monitoring plan includes K groups of thermal disaster risk control monitoring points; multiple historical energy storage scenarios of the lithium-ion energy storage power station are called; the thermal runaway propagation evolution of the K groups of thermal disaster risk control monitoring points is performed using the multiple historical energy storage scenarios, and the lithium-ion energy storage power station is divided into the M thermal disaster risk control monitoring areas.

[0017] Specifically, in the power station division module 11, the system terminal first uses the thermal runaway accident data of similar lithium-ion battery energy storage power stations to mine the risk control factors that may affect battery safety, and obtains K types of thermal disaster risk control factors. These factors include but are not limited to battery temperature, current, voltage, ambient humidity, charge and discharge rate, and battery aging status. These factors are potential triggering factors for thermal disasters. Therefore, by conducting a detailed analysis of these factors, basic data can be provided for subsequent monitoring point configuration and risk warning. Subsequently, based on the mined K types of thermal disaster risk control factors, the risk control monitoring points are located in the power station, that is, the points with the same monitoring equipment in the lithium-ion energy storage power station are counted to obtain K groups of thermal disaster risk control monitoring points, each group of thermal disaster risk control monitoring points corresponds to a thermal disaster risk control factor. For example, if point a has a current sensor and point b also has a current sensor, point a and point b will be stored in the same group of thermal disaster risk control monitoring points; after the risk control factors and After the monitoring point is located, the system terminal will call multiple historical energy storage scenarios. These scenarios are based on the actual operation records or experimental data of the power station in the past, which can help understand the working status of the battery pack and the power station and the possible thermal runaway situations under different circumstances. For example, the historical energy storage scenario may include the situation of battery temperature rise and battery charging and discharging rate is fast. These data help to understand the occurrence mode of thermal runaway. Afterwards, using these historical energy storage scenarios, the system terminal will analyze each monitoring point through the thermal runaway propagation model to simulate how factors such as temperature rise and internal battery reaction in the thermal runaway process propagate between battery packs. Through this propagation evolution, the system terminal can understand the temperature change of each monitoring point, and merge similarly according to the predicted temperature change, and divide M thermal disaster risk control monitoring areas. The monitoring points in each thermal disaster risk control monitoring area have similar temperature changes, and each thermal disaster risk control monitoring area has different risk levels and monitoring requirements. Through this process, the system terminal can not only monitor various risk control factors of the power station in real time, but also understand the thermal runaway risk of each area through the evolution analysis of historical data, thereby providing data support for fire prevention and emergency response of the entire lithium-ion energy storage power station.

[0018] In a possible implementation, risk control factors of lithium-ion energy storage power stations are mined to obtain K types of thermal disaster risk control factors. The system includes: The power station building features of the lithium-ion battery energy storage power station are used for feature comparison and mining to obtain multiple sample energy storage power stations; multiple sample thermal runaway accident data sets of the multiple sample energy storage power stations are called online, wherein each sample thermal runaway accident data includes a sample thermal runaway risk level and multiple sample associated risk control factors; risk control factor aggregation is performed on the multiple sample thermal runaway accident data sets to obtain H types of sample risk control factors; risk control factor association analysis is performed on the multiple sample thermal runaway accident data sets based on statistical analysis to obtain H thermal runaway risk coefficients of the H types of sample risk control factors; a thermal runaway risk threshold is preset, and the H thermal runaway risk coefficients are traversed by using the thermal runaway risk threshold to screen out the K types of thermal disaster risk control factors from the H types of sample risk control factors.

[0019] Specifically, in the power station division module 11, the system terminal obtains the power station building characteristics of the lithium-ion battery energy storage power station. The power station building characteristics refer to the overall structural design, layout configuration, and installation method of the battery pack and related equipment of the energy storage power station, which can be obtained from the design drawings of the lithium-ion battery energy storage power station, such as the energy storage battery pack layout, heat dissipation path, heat dissipation material, battery spacing, channel width, cable layout, etc., and then obtains the respective power station building characteristics from other energy storage power stations. By calculating the similarity between the power station building characteristics of the lithium-ion battery energy storage power station and the power station building characteristics of other energy storage power stations, the power station building characteristics of the lithium-ion battery energy storage power station and other energy storage power stations are obtained. The similarity of the energy storage power stations is calculated, and the energy storage power stations with similarity greater than or equal to the similarity threshold are recorded to obtain multiple sample energy storage power stations, wherein the similarity calculation is obtained by Euclidean distance calculation. For example, when calculating the similarity of the energy storage battery group layout, the coordinates of each energy storage battery group are obtained from the design drawings. Through these coordinates, the Euclidean distance is used to calculate the similarity of the energy storage battery group layout between the lithium-ion battery energy storage power station and each energy storage power station. When calculating the similarity of the heat dissipation material, the Euclidean distance is used to calculate the similarity of the heat dissipation material between the lithium-ion battery energy storage power station and each energy storage power station based on the thermal conductivity of the heat dissipation material. The calculated similarity is then The average value is calculated, and the similarity between the lithium-ion battery energy storage power station and each energy storage power station is calculated; then, the thermal runaway accident data sets of multiple sample energy storage power stations are called through networking. These data sets are collected by collecting detailed data when historical thermal runaway accidents occurred to help understand the occurrence and severity of thermal runaway events under the influence of different risk control factors. Each sample thermal runaway accident data includes a sample thermal runaway risk level and multiple sample-related risk control factors. Among them, the sample thermal runaway risk level indicates the severity of the thermal runaway event, which is usually graded from low to high according to risk. Multiple sample-related risk control factors record the large fluctuations when thermal runaway events occur. data, such as battery temperature, current, voltage, ambient humidity, charge and discharge rate, etc.; after obtaining multiple sample thermal runaway accident data, the system terminal aggregates these risk control factors, that is, traverses multiple sample thermal runaway accident data sets, stores the data of the same risk control factor into a set, and obtains multiple risk control factor data sets, and then compares the data volume in each risk control factor data set with the critical data volume, extracts risk control factors that are greater than or equal to the critical data volume, and these risk control factors are factors with large fluctuations that are common when thermal runaway events occur. By storing these risk control factors separately, H types of sample risk control factors are obtained;Afterwards, statistical analysis is performed on the obtained H types of sample risk control factors, and correlation analysis is performed on these factors. The purpose of this step is to obtain the degree of influence of each risk control factor on the thermal runaway event by analyzing the relationship between each risk control factor and the thermal runaway risk level. Specifically, the system terminal uses linear regression or polynomial regression and other methods, taking the risk control factor as the independent variable and the thermal runaway risk level as the dependent variable to establish a regression function. Taking linear regression as an example, a linear fitting function is constructed, and the specific form is; , where R is the risk level, , , ..., As a risk control factor, , , ..., is the thermal runaway risk coefficient, which indicates the degree of influence of each risk control factor on the risk level. In the fitting process, the corresponding data in multiple sample thermal runaway accident data sets are used as input, including multiple sample thermal runaway risk levels and corresponding multiple sample associated risk control factors. To ensure that the fitting function can accurately reflect the influence of each risk control factor on the thermal runaway risk level, the least squares method is used to calculate the thermal runaway risk coefficient of the linear fitting function. , , ..., The goal of the least squares method is to minimize the sum of squares of the errors between the predicted value and the actual value, and then substitute the selected data into the linear fitting function for fitting, adjust the parameters in the function, so that the predicted value after fitting is as close as possible to the actual data, and thus obtain the specific coefficients of the linear fitting function; in order to verify the fitting effect, the mean square error (MSE) of the fitting function is calculated. If the error is small, the fitting function is valid. At this time, the coefficients of the fitted linear fitting function will be stored as H thermal runaway risk coefficients of H sample risk control factors, otherwise, it will be replaced with a polynomial fitting function; after obtaining the H thermal runaway risk coefficients, the system terminal will preset the thermal runaway risk threshold, and compare it with the set threshold by traversing these risk coefficients. The purpose of this step is to screen out those risk control factors that have a significant impact on the thermal runaway risk, and finally obtain K thermal disaster risk control factors (all greater than or equal to the preset thermal runaway risk threshold). These K screened risk control factors are the most influential factors, and they will be used as the main monitoring parameters for real-time tracking and risk assessment in practical applications. Through this process, combined with historical data, risk control factor analysis and regression modeling, the most critical risk control factors can be effectively identified, and real-time monitoring and control can be carried out in actual applications, providing accurate risk prediction and prevention and control strategies for lithium-ion energy storage power stations.

[0020] In a possible implementation, the multiple historical energy storage scenarios are used to perform thermal runaway propagation evolution on the K groups of thermal disaster risk control monitoring points, and the lithium-ion energy storage power station is divided into the M thermal disaster risk control monitoring areas. The system includes: A thermal runaway propagation model is constructed using the power station design information of the lithium-ion battery energy storage power station; the multiple historical energy storage scenarios are input into the thermal runaway propagation model to perform thermal runaway propagation evolution to obtain multiple thermal runaway evolution models; the first thermal runaway evolution model is discretized at a preset model grid scale to obtain a first three-dimensional temperature field distribution map; the temperature gradient is calculated for the first three-dimensional temperature field distribution map to obtain a first temperature change rate array; the first temperature change rate array is segmented using a watershed algorithm to obtain a first sample area segmentation strategy, wherein the first sample area segmentation strategy includes multiple sample temperature change characteristics of multiple sample monitoring areas; and by analogy, the multiple thermal runaway evolution models are segmented to obtain multiple sample area segmentation strategies; the multiple sample area segmentation strategies are similarly merged based on the temperature change characteristics to obtain the M thermal disaster risk control monitoring areas.

[0021] Specifically, in the power station division module 11, in order to construct a thermal runaway propagation model for a lithium-ion battery energy storage power station, it is necessary to rely on various information on the power station design and combine physical principles such as heat conduction, convection and radiation to describe the thermal runaway process. First, the power station design information of the lithium-ion battery energy storage power station is collected, including battery pack layout, heat dissipation path, cooling system configuration, and the thermal characteristics of the battery itself (such as thermal conductivity and specific heat capacity). At the same time, external factors such as temperature, humidity and wind speed in the environment where the battery pack is located must also be considered. These design information provide a basis for constructing a thermal runaway propagation model; based on this information, a physical model of thermal runaway propagation can be constructed. Usually, the thermal runaway propagation process involves heat transfer inside and outside the battery. The heat conduction model is used to describe the internal or external heat of the battery. The heat transfer process between battery packs corresponds to the heat conduction equation in mathematical form. The heat exchange process between the battery surface and the air or coolant requires the use of a thermal convection model, while the radiant heat transfer between the high-temperature battery surface and the external environment is described by a thermal radiation model. After selecting these models, initial conditions and boundary conditions need to be set. The initial condition is the temperature state of the battery pack when it starts working, which is usually close to the ambient temperature. The boundary condition describes the heat exchange process between the battery surface and the environment, including the thermal convection coefficient and the external ambient temperature. After the initial conditions and boundary conditions of the physical model of thermal runaway propagation are set, the system terminal can obtain a thermal runaway propagation model, which can describe the propagation process of thermal runaway in the energy storage power station. Once the thermal runaway propagation model is established, the system terminal will input multiple historical energy storage scenarios into the thermal runaway propagation model for simulation. These historical energy storage scenarios include various battery states, environmental conditions, charging and discharging rates and other factors, which will affect the occurrence and propagation of thermal runaway. The thermal runaway propagation model is used to calculate the thermal runaway evolution process under different energy storage scenarios, and multiple thermal runaway evolution models are obtained. Each model represents the propagation mode of thermal runaway under different environments. Subsequently, one of the multiple thermal runaway evolution models is randomly extracted as the first thermal runaway evolution model. The system terminal will select a numerical method to solve the thermal runaway propagation model. Due to the complex geometric shapes and multi-physical field coupling involved, common numerical solution methods include the finite difference method (FDM). and finite element method (FEM). These methods discretize the model area. The battery pack area is divided into multiple small grids. The division scale is the preset model grid scale (determined based on business needs and expert decisions). Each grid represents a spatial unit, and the temperature change of each grid unit is calculated. The time discretization is completed by selecting an appropriate time step. The temperature distribution inside and outside the battery pack (the temperature value of each grid) is obtained by numerical solution, and these values ​​are plotted into a three-dimensional temperature field distribution map, showing the temperature change of each area in the energy storage power station, which provides a basis for analyzing the propagation of thermal runaway. Afterwards, based on these temperature field distribution maps, the temperature gradient of each area can be further calculated. This temperature gradient is calculated by the gradient operator ( ) is defined as the rate of change of temperature in all directions. The mathematical expression is ; Where T is the temperature, x, y, z are the spatial coordinates, are the rates of change of temperature in the x, y, and z directions respectively; the rate of change in the x direction can be calculated by the forward difference method, and the forward difference method formula is: ; where T is the temperature, is a grid point The temperature at is the current grid point The temperature at is the spatial step length of the grid in the x direction, that is, the preset model grid scale. Similarly, the change rate in the y direction and the z direction is calculated to obtain the final temperature gradient. After obtaining the temperature gradients of all grids, the system terminal stores these temperature gradients to obtain a first temperature change rate array. Further, the first temperature change rate array is segmented using the watershed algorithm. The watershed algorithm is an image processing technology that divides the image into multiple areas by finding the water flow boundary line in the image (that is, the area where the temperature changes drastically). Specifically, the system terminal uses the temperature change rate array as the input image, and the temperature change rate value is used as the intensity in the image. The high gradient The low gradient area corresponds to the bright area in the image, and the low gradient area corresponds to the dark area. Then, some representative seed points are selected as water sources. These seed points are usually selected in areas with large gradients, that is, areas with drastic temperature changes, indicating potential thermal runaway areas. Then, the simulated water flow starts from these seed points and expands to the surrounding areas. In places where the temperature gradient changes drastically, the water flow cannot continue to expand, forming dividing lines. These dividing lines are the boundaries of different areas, thereby dividing the entire temperature change rate array into multiple areas. The temperature change characteristics in each area are similar. After obtaining these segmented areas, the system terminal will further analyze the temperature change characteristics of each area, such as the average temperature change rate, etc. Step 1 identifies and marks these areas as different monitoring areas. The temperature change characteristics of each monitoring area can provide potential thermal runaway risk information, which is convenient for subsequent risk assessment and prevention and control decisions. Through the above process, the system terminal obtains the first sample area segmentation strategy, which includes multiple sample temperature change characteristics of multiple sample monitoring areas, providing an effective basis for subsequent thermal disaster risk control monitoring; after completing the segmentation of the first temperature change rate array, the system terminal uses the same method to perform model discretization, temperature gradient calculation and array segmentation on the remaining thermal runaway evolution models, thereby obtaining the sample area segmentation strategy for each thermal runaway evolution model; finally, the system terminal The mark (average temperature change rate) of each sample monitoring area is extracted, and the absolute difference between the two marks is calculated to determine whether the calculation result is within the deviation threshold. If it is within the deviation threshold, the system terminal will merge the two sample monitoring areas (including merging the sample temperature change characteristics), and then recalculate the mark of the merged sample monitoring area. Repeat the above process until the difference of all marks is greater than the deviation threshold, thereby obtaining M thermal disaster risk control monitoring areas. These areas represent different risk areas in lithium-ion battery energy storage power stations. The thermal runaway risks in each area have certain commonalities, which provide a basis for subsequent risk assessment and prevention and control strategies.

[0022] The node configuration module is used to perform risk control sensitivity analysis on the M thermal disaster risk control monitoring areas, and configure M edge thermal runaway prediction nodes in the M thermal disaster risk control monitoring areas according to the analysis results, wherein the M edge thermal runaway prediction nodes are connected to the thermal disaster alarm decision cloud for communication.

[0023] Specifically, for the M thermal disaster risk control monitoring areas obtained by division, the node configuration module 12 will first use these areas as grouping constraints, and divide each area based on the previously determined K groups of thermal disaster risk control monitoring points to form M thermal disaster risk control monitoring point sets; then, these monitoring point sets are subjected to risk control sensitivity analysis, and the risk characteristics in each area are analyzed, thereby generating M precursor risk feature sets, which are obtained by comprehensively analyzing the monitoring data in the area, and the potential thermal runaway risk characteristics of each area are obtained; based on these analysis results, the system terminal will construct an edge thermal runaway prediction node in each thermal disaster risk control monitoring area, and these prediction nodes are configured according to the risk characteristics in the area, aiming to provide the power station with more accurate thermal runaway prevention capabilities through continuous monitoring and early warning; finally, these edge thermal runaway prediction nodes will communicate with the thermal disaster alarm decision cloud, and transmit the monitoring data and risk prediction information collected by each to the cloud in real time. Through the connection with the cloud, the real-time data of all nodes can be summarized, and comprehensive analysis and decision-making can be carried out, so as to timely identify potential thermal runaway risks and take corresponding prevention and control measures.

[0024] In a possible implementation, a risk control sensitivity analysis is performed on the M thermal disaster risk control monitoring areas, and M edge thermal runaway prediction nodes are configured in the M thermal disaster risk control monitoring areas according to the analysis results. The system includes: Taking the M thermal disaster risk control monitoring areas as grouping constraints, the K groups of thermal disaster risk control monitoring points are divided into M thermal disaster risk control monitoring point sets; risk control sensitivity analysis is performed on the M thermal disaster risk control monitoring point sets to obtain M precursor risk feature sets; M edge thermal runaway prediction nodes are constructed in the M thermal disaster risk control monitoring areas according to the M precursor risk feature sets; a thermal disaster alarm decision cloud is pre-constructed, and the M edge thermal runaway prediction nodes are communicatively connected to the thermal disaster alarm decision cloud.

[0025] Specifically, in the node configuration module 12, the system terminal uses M thermal disaster risk control monitoring areas as grouping constraints to divide K groups of thermal disaster risk control monitoring points, that is, the thermal disaster risk control monitoring points belonging to the same area are divided into a set to form M thermal disaster risk control monitoring point sets. This step is to ensure that the risk control monitoring points in each area can be centralized and effectively managed. After the division, each thermal disaster risk control monitoring point set represents all monitoring points in a region; then, the system terminal performs a risk control sensitivity analysis on the M thermal disaster risk control monitoring point sets. The purpose of this analysis is to evaluate the risk control sensitivity of each monitoring point set and understand the impact of different risk control factors (such as temperature, humidity, charge and discharge rate, etc.) in each area on the thermal runaway risk. The result of the analysis will generate M precursor risk feature sets, each feature set contains the key risk features in the area, and these features reflect the potential thermal runaway risk in the area; then, based on the M obtained by the analysis Precursor risk feature set, the system terminal will configure an edge thermal runaway prediction node in each thermal disaster risk control monitoring area. The main function of these edge prediction nodes is to monitor the real-time data in the area according to the risk characteristics obtained previously, and predict and warn of potential thermal runaway risks through the edge thermal runaway prediction channel. Each prediction node will be responsible for real-time monitoring and data feedback of multiple important parameters such as temperature and charging status in its area; finally, the system terminal will communicate and connect M edge thermal runaway prediction nodes with the pre-built thermal disaster alarm decision cloud (pre-build multiple thermal runaway suppression decision models and connect them in parallel to the thermal disaster alarm decision cloud). Through the cloud, all prediction nodes can summarize the risk data they collect and conduct comprehensive analysis to make an overall thermal disaster decision. The thermal disaster alarm decision cloud can also issue timely warnings to the operation and maintenance center of the power station based on the real-time feedback of the nodes to ensure rapid response and take necessary preventive measures.

[0026] In one possible implementation, the system includes: Taking the first thermal disaster risk control monitoring area as the retrieval condition, multiple first area thermal runaway data are called from the historical thermal disaster alarm records; W groups of thermal runaway monitoring time series data of W thermal disaster risk control monitoring points in the first thermal disaster risk control monitoring point set are extracted from the multiple first area thermal runaway data; thermal runaway feature aggregation is performed on the W groups of thermal runaway monitoring time series data to obtain a first precursor risk feature set, wherein the first precursor risk feature set includes W thermal runaway precursor risk features, and the thermal runaway precursor risk features include a monitoring indicator change range and a monitoring indicator change rate; the W thermal runaway precursors are aggregated. Risk characteristics are used as risk judgment benchmarks to construct W edge thermal runaway prediction channels; the W edge thermal runaway prediction channels are connected in parallel, and the input ends of the W edge thermal runaway prediction channels are communicatively connected to the W risk control monitoring sensors of the W thermal disaster risk control monitoring points to complete the configuration of the first edge thermal runaway prediction node; by analogy, risk control sensitivity analysis is performed on the M thermal disaster risk control monitoring point sets to obtain the M precursor risk feature sets; by analogy, the M edge thermal runaway prediction nodes are constructed in the M thermal disaster risk control monitoring areas according to the M precursor risk feature sets.

[0027] Specifically, in the node configuration module 12, the system terminal randomly extracts a thermal disaster risk control monitoring area from the M thermal disaster risk control monitoring areas as the first thermal disaster risk control monitoring area, and uses the area as a search condition, and calls the thermal runaway data related to the area from the historical thermal disaster alarm records to form a plurality of first area thermal runaway data, which contain detailed records of thermal runaway events that have occurred in the area in the past, such as parameters such as temperature, humidity, battery charge and discharge status, and other factors related to thermal runaway. By calling these historical data, the system terminal can understand the past thermal runaway trend of the area as a basis for subsequent analysis; subsequently, the system terminal retrieves the thermal runaway data from the plurality of first area thermal runaway events. The first thermal disaster risk control monitoring point set is extracted from the data, and W thermal disaster risk control monitoring points are extracted from the first thermal disaster risk control monitoring point set. For these monitoring points, the thermal runaway monitoring time series data of each monitoring point will be extracted to form W groups of thermal runaway monitoring time series data. These thermal runaway monitoring time series data include information such as temperature changes, charging status, and battery health at different time points. These time series data provide the dynamic characteristics of each monitoring point changing over time, helping the system terminal to identify potential thermal runaway risks; then, the thermal runaway features of the W groups of thermal runaway monitoring time series data are aggregated to obtain the first precursor risk feature set. In this process, the system terminal will The risk control factor aggregates each group of thermal runaway monitoring time series data, that is, the data of the same thermal disaster risk control factor in each group of thermal runaway monitoring time series data are aggregated into a set. For each group of aggregated features, the system terminal extracts the maximum and minimum values ​​of the group of aggregated features to obtain the monitoring indicator change range of the group of aggregated features, and then calculates the ratio of the difference between the feature value at each time point in each group of aggregated features and the feature value at the previous time point and the time difference to obtain the feature change rate at each time point, and then calculates the average of the feature change rates of all time points to obtain the monitoring indicator change rate of the group of aggregated features. The calculated monitoring indicator change range and monitoring indicator change rate of each group of aggregated features are averaged. Summarize to obtain the thermal runaway precursor risk characteristics corresponding to the group of thermal runaway monitoring time series data, and then summarize the thermal runaway precursor risk characteristics of W groups of thermal runaway monitoring time series data to obtain the first precursor risk characteristic set. This first precursor risk characteristic set is the basis for evaluating the potential thermal runaway risk of the first thermal disaster risk control monitoring area; then, the system terminal uses the W thermal runaway precursor risk characteristics as the risk judgment benchmark to construct W edge thermal runaway prediction channels. The function of each prediction channel is to predict the possibility of thermal runaway by analyzing the relationship between real-time monitoring data and precursor risk characteristics. Each channel corresponds to an edge prediction node, which specifically monitors the thermal runaway risk of the area and provides early warning;In order to ensure that the system terminal can process multiple monitoring points in parallel, the system terminal connects W edge thermal runaway prediction channels in parallel so that each monitoring point can independently perform prediction and warning. At the same time, the input end of each prediction channel communicates with the W risk control monitoring sensors of the corresponding W thermal disaster risk control monitoring points to obtain the status data of the monitoring points in real time and perform corresponding risk analysis. After this process is completed, the first edge thermal runaway prediction node is configured and has the functions of real-time data collection, analysis and warning. Finally, the system terminal continues to perform similar processing on other thermal disaster risk control monitoring point sets, including risk control sensitivity analysis, and generates corresponding precursor risk feature sets based on the analysis results. Based on these precursor risk feature sets, corresponding edge thermal runaway prediction nodes are configured for each monitoring area. Each node will be responsible for monitoring the temperature, humidity, battery status and other data of its corresponding area, and perform real-time thermal runaway risk assessment. Through this series of steps, the system terminal realizes intelligent warning based on historical data and real-time monitoring data, and can take timely measures before the risk of thermal runaway occurs, thereby improving the safety of lithium-ion battery energy storage power stations. ;

[0028] The thermal runaway suppression decision module 13 is used for receiving the thermal disaster alarm decision cloud and making thermal runaway suppression decisions based on the M real-time thermal runaway risk features transmitted back by the M edge thermal runaway prediction nodes, and outputting a real-time thermal runaway prevention and control strategy, wherein the real-time thermal runaway prevention and control strategy has a prevention and control time window identifier.

[0029] Specifically, the main task of the thermal disaster alarm decision cloud is to receive M real-time thermal runaway risk features sent back from M edge thermal runaway prediction nodes. These features include important parameters such as temperature change rate, charge and discharge rate, and battery health status. These data provide real-time and accurate risk assessment information for cloud-based decision-making; when the M edge thermal runaway prediction nodes detect abnormal data, the M edge thermal runaway prediction nodes will transmit the monitored monitoring index parameters and index change rates as real-time thermal runaway risk features back to the thermal disaster alarm decision cloud. The thermal runaway suppression decision module 13 analyzes and processes the thermal runaway risks of each area through the thermal runaway suppression decision mechanism based on the real-time thermal runaway risk features received by the thermal disaster alarm decision cloud. The process The core is to judge the current risk level through the integrated M thermal runaway suppression decision models and generate a real-time thermal runaway prevention and control strategy. For example, if the temperature in some areas is too high or the charging and discharging rate is abnormal, the generated real-time thermal runaway prevention and control strategy may be to start the cooling system, reduce the charging and discharging rate, etc.; once a decision is made, the thermal disaster alarm decision cloud will output this real-time thermal runaway prevention and control strategy. The strategy not only includes specific control measures, but also comes with a prevention and control time window logo. This logo represents the effective time range of the prevention and control measures to ensure that thermal runaway events do not occur. The design of the prevention and control time window enables the system terminal to dynamically adjust the strategy according to the actual situation and provide precise guidance to the operation and maintenance personnel to ensure the safety and stability of the lithium-ion battery energy storage power station.

[0030] In a possible implementation, the thermal disaster alarm decision cloud receives and makes a thermal runaway suppression decision based on the M real-time thermal runaway risk features transmitted back by the M edge thermal runaway prediction nodes, and outputs a real-time thermal runaway prevention and control strategy. The system includes: Taking the first thermal disaster risk control monitoring area as a retrieval condition, calling multiple first historical area prevention and control strategies of the multiple first area thermal runaway data from the historical thermal disaster alarm record; using a CNN network to build a first thermal runaway suppression decision model; using the multiple first area thermal runaway data and multiple first historical area prevention and control strategies as training data to optimize the parameters of the first thermal runaway suppression decision model; and so on, building M thermal runaway suppression decision models for the M thermal disaster risk control monitoring areas, and connecting the M thermal runaway suppression decision models in parallel to the thermal disaster alarm decision cloud to complete the decision function configuration of the thermal disaster alarm decision cloud; the thermal disaster alarm decision cloud receives and inputs the M real-time thermal runaway risk feature maps returned by the M edge thermal runaway prediction nodes into the M thermal runaway suppression decision models to make a thermal runaway suppression decision, and obtain the real-time thermal runaway prevention and control strategy, wherein the real-time thermal runaway prevention and control strategy includes M real-time prevention and control sub-strategies.

[0031] Specifically, in the thermal runaway suppression decision module 13, when pre-building the thermal disaster alarm decision cloud, the system terminal uses the first thermal disaster risk control monitoring area as a search condition, and calls the thermal runaway data related to the area from the historical thermal disaster alarm records. These data contain information about thermal runaway events that have occurred in the area in the past, as well as the corresponding first historical area prevention and control strategies. These prevention and control strategies include past measures to deal with thermal runaway events and prevention and control time windows. By calling these historical data, the system terminal can understand the past thermal runaway conditions of the area as a basis for formulating new prevention and control strategies; subsequently, the system terminal uses a convolutional neural network (CNN) to construct a first thermal runaway suppression decision model. At this time, the system The system terminal uses the thermal runaway data of multiple first areas and the prevention and control strategies of multiple first historical areas as training data to train the CNN model. Specifically, the system terminal uses CNN to build an initial thermal runaway inhibition decision model structure, including input layer, convolution layer, pooling layer, fully connected layer and output layer, and then uses random initialization to set the initial weights for each layer of the model, and inputs the training data into the initialized model for forward propagation. The data is passed layer by layer through the input layer, convolution layer, pooling layer, fully connected layer and output layer to calculate the prediction result of the thermal runaway risk suppression strategy; then, the cross entropy loss function and the mean square error loss function are used to calculate the loss between the predicted result and the actual result, and The gradient of the loss to the weight of each layer is calculated layer by layer through the back-propagation algorithm, and then the Adam optimizer is used to optimize the model parameters and adjust the weight of each layer to minimize the value of the loss function. This process will continue until the maximum number of iterations is reached. After the training is completed, the performance of the model is tested using the validation set (data not used for training) to evaluate the accuracy of the model in the thermal runaway risk suppression decision-making task. If the accuracy reaches the expected accuracy, the currently trained thermal runaway suppression decision-making model is used as the final model output. Otherwise, the learning rate, batch size and other hyperparameters are adjusted to further improve the prediction ability of the model. Similarly, M thermal runaway suppression decision-making models are constructed for M thermal disaster risk control monitoring areas. Each model is trained and optimized according to the thermal runaway historical data and prevention and control strategies of its own area, and then all the models are connected in parallel and integrated into the thermal disaster alarm decision cloud to complete the decision function configuration of the thermal disaster alarm decision cloud. When M edge thermal runaway prediction nodes transmit M real-time thermal runaway risk features back to the cloud, the thermal disaster alarm decision cloud will receive these data and input them into M thermal runaway inhibition decision models according to the thermal runaway risk features of each area. Each model will make predictions and decisions based on the input real-time data, thereby generating a corresponding real-time thermal runaway prevention and control strategy. This strategy includes M real-time prevention and control sub-strategies, each of which corresponds to specific prevention and control measures in different monitoring areas.Ultimately, these real-time prevention and control sub-strategies will help power plants take precise risk response measures, such as adjusting the charge and discharge rates, starting the cooling system, and increasing the heat dissipation channels, to ensure that battery energy storage power plants can take timely and effective response strategies when facing the risk of thermal runaway, thereby improving the safety and stability of power plants. ;

[0032] The decision generation module 14 is used to perform thermal runaway prevention and control evolution based on the real-time thermal runaway prevention and control strategy, and generate a real-time fire extinguishing risk control decision according to the evolution result.

[0033] Specifically, the decision generation module 14 first conducts prevention and control evolution of the thermal runaway risk in the battery energy storage power station based on the real-time thermal runaway prevention and control strategy. This process is mainly based on the current prevention and control strategy. It simulates the effects of different prevention and control measures in the future through the thermal runaway propagation model, and adjusts the prevention and control strategy in real time. The goal of the prevention and control evolution is to evaluate in real time whether the prevention and control measures are effective and whether the prevention and control measures need to be further strengthened based on the monitoring data such as the temperature and charging and discharging rate in the battery energy storage power station. For example, when an abnormal temperature is detected in a certain area, the system terminal will first start the basic prevention and control measures (i.e., the real-time thermal runaway prevention and control strategy). If the prevention and control measures fail to alleviate the risk in time, the system terminal will decide whether to take stronger measures based on the evolution results. If the real-time thermal runaway prevention and control strategy can mitigate the risk within the prevention and control time window, the real-time thermal runaway prevention and control strategy will be used as a real-time fire extinguishing risk control decision. Otherwise, the real-time thermal runaway prevention and control strategy and the start-up explosion suppression fire extinguishing device will be used as a real-time fire extinguishing risk control decision, and the prevention and control time window will be used to mark it, so as to effectively curb the spread of fire and ensure the safety of the battery energy storage power station. Among them, the explosion suppression fire extinguishing device has the function of spraying nitrogen and liquid nitrogen. The response time of the explosion suppression fire extinguishing device is not higher than 3s. The open fire is extinguished within 5s after the liquid nitrogen is sprayed. 10min after the fire is extinguished, except for the center of both sides of the battery where thermal runaway occurs, the temperature of other positions is ≤70℃. Within 24h after the fire is extinguished, all batteries will no longer be in thermal runaway or burn. This whole process is dynamic, and the evolution of prevention and control is constantly adjusted based on real-time data to ensure that no matter how complex the thermal runaway risk is, timely and effective response decisions can be made to protect the safety of equipment and personnel to the greatest extent.

[0034] The risk level mapping module 15 is used to traverse the risk level mapping table using the prevention and control time window identifier and the real-time fire extinguishing risk control decision to obtain the real-time risk level.

[0035] Specifically, the risk level mapping module 15 first uses the prevention and control time window identifier and the real-time fire extinguishing risk control decision, combined with the risk level mapping table, to evaluate the current thermal runaway risk level; specifically, the prevention and control time window identifier represents the effective time range of the current prevention and control strategy, which indicates how long the prevention and control measures should continue to be effective, and the real-time fire extinguishing risk control decision includes the specific prevention and control measures currently taken, such as starting the explosion suppression and fire extinguishing device, adjusting the charge and discharge rate, starting the cooling system, and increasing the heat dissipation channel. These decisions directly affect the thermal runaway risk of the current power station; the system terminal traverses the risk level mapping table and inputs the current prevention and control time window identifier and the real-time fire extinguishing risk control decision into the table. The risk level mapping table is a predefined table that shows the relationship between different risk levels and corresponding prevention and control measures and time windows. It helps the system terminal to derive the real-time risk level of the power station based on the current prevention and control measures and time windows; ultimately, the real-time risk level will reflect the thermal runaway risk level of the current power station to ensure the safety of the lithium-ion battery energy storage power station.

[0036] The alarm signal sending module 16 is used to package the real-time risk level, real-time thermal runaway prevention and control strategy and real-time fire extinguishing risk control decision into a thermal disaster alarm signal and send it to the power station operation and maintenance center.

[0037] Specifically, the alarm signal sending module 16 first integrates the current real-time risk level, real-time thermal runaway prevention and control strategy and real-time fire extinguishing risk control decision to form a complete thermal disaster alarm signal. The real-time risk level indicates the thermal runaway risk level of the current power station, the real-time thermal runaway prevention and control strategy, the specific prevention and control measures based on the current risk assessment, and the real-time fire extinguishing risk control decision are the actual prevention and control measures; after packaging these key information into an alarm signal, the system terminal will send it to the power station operation and maintenance center. After receiving the alarm signal, the operation and maintenance personnel of the power station operation and maintenance center can quickly understand the current safety status of the power station, the prevention and control measures taken, and whether further emergency response is needed. Through this mechanism, the safety management of the power station can receive timely feedback and response to ensure the safe and stable operation of the battery energy storage power station.

[0038] Embodiment 2: Figure 2 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an implementation of the present invention. Figure 2 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 2 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 2Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the electronic device can be connected through a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0039] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0040] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0041] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The thermal disaster alarm system of the lithium-ion battery energy storage power station is characterized by: The system comprises: The power station division module is used to divide the lithium-ion energy storage power station into M thermal disaster risk control monitoring areas through the evolution of thermal runaway propagation; A node configuration module, used to perform risk control sensitivity analysis on the M thermal disaster risk control monitoring areas, and configure M edge thermal runaway prediction nodes in the M thermal disaster risk control monitoring areas according to the analysis results, wherein the M edge thermal runaway prediction nodes are connected to the thermal disaster alarm decision cloud for communication; A thermal runaway suppression decision module is used for the thermal disaster alarm decision cloud to receive and make thermal runaway suppression decisions based on the M real-time thermal runaway risk features transmitted back by the M edge thermal runaway prediction nodes, and output a real-time thermal runaway prevention and control strategy, wherein the real-time thermal runaway prevention and control strategy has a prevention and control time window identifier; A decision generation module, used to perform thermal runaway prevention and control evolution based on the real-time thermal runaway prevention and control strategy, and generate a real-time fire extinguishing risk control decision according to the evolution result; A risk level mapping module, used to traverse the risk level mapping table using the prevention and control time window identifier and the real-time fire extinguishing risk control decision to obtain a real-time risk level; The alarm signal sending module is used to package the real-time risk level, real-time thermal runaway prevention and control strategy and real-time fire extinguishing risk control decision into a thermal disaster alarm signal and send it to the power station operation and maintenance center.

2. The thermal disaster alarm system of the lithium-ion battery energy storage power station according to claim 1, characterized in that: Through the evolution of thermal runaway propagation, the lithium-ion energy storage power station is divided into M thermal disaster risk control monitoring areas. The system includes: The risk control factors of lithium-ion energy storage power stations were mined to obtain K types of thermal disaster risk control factors; Positioning risk control monitoring points in the lithium-ion energy storage power station according to the K types of thermal disaster risk control factors, and generating a power station monitoring plan, wherein the power station monitoring plan includes K groups of thermal disaster risk control monitoring points; Calling multiple historical energy storage scenarios of the lithium-ion energy storage power station; The multiple historical energy storage scenarios are used to perform thermal runaway propagation evolution on the K groups of thermal disaster risk control monitoring points, and the lithium-ion energy storage power station is divided into the M thermal disaster risk control monitoring areas.

3. The thermal disaster alarm system of the lithium-ion battery energy storage power station according to claim 2, characterized in that: Performing risk control sensitivity analysis on the M thermal disaster risk control monitoring areas, and configuring M edge thermal runaway prediction nodes in the M thermal disaster risk control monitoring areas according to the analysis results, the system comprising: Taking the M thermal disaster risk control monitoring areas as grouping constraints, the K groups of thermal disaster risk control monitoring points are divided into M thermal disaster risk control monitoring point sets; Performing risk control sensitivity analysis on the M thermal disaster risk control monitoring point sets to obtain M precursor risk feature sets; Constructing M edge thermal runaway prediction nodes in the M thermal disaster risk control monitoring areas according to the M precursor risk feature sets; A thermal disaster alarm decision cloud is pre-built, and the M edge thermal runaway prediction nodes are communicatively connected to the thermal disaster alarm decision cloud.

4. The thermal disaster alarm system of the lithium-ion battery energy storage power station according to claim 2, characterized in that: Risk control factors of lithium-ion energy storage power stations are mined to obtain K types of thermal disaster risk control factors. The system includes: Using the power station building features of the lithium-ion battery energy storage power station to perform feature comparison and mining, and obtain multiple sample energy storage power stations; Calling multiple sample thermal runaway accident data sets of the multiple sample energy storage power stations online, wherein each sample thermal runaway accident data set includes a sample thermal runaway risk level and multiple sample-related risk control factors; Aggregating risk control factors on the multiple sample thermal runaway accident data sets to obtain H types of sample risk control factors; Based on statistical analysis, risk control factor correlation analysis is performed on the multiple sample thermal runaway accident data sets to obtain H thermal runaway risk coefficients of the H sample risk control factors; A thermal runaway risk threshold is preset, and the H thermal runaway risk coefficients are traversed by using the thermal runaway risk threshold to screen out the K thermal disaster risk control factors from the H sample risk control factors.

5. The thermal disaster alarm system of the lithium-ion battery energy storage power station according to claim 3, characterized in that: The multiple historical energy storage scenarios are used to perform thermal runaway propagation evolution on the K groups of thermal disaster risk control monitoring points, and the lithium-ion energy storage power station is divided into the M thermal disaster risk control monitoring areas. The system includes: Constructing a thermal runaway propagation model using power station design information of the lithium-ion battery energy storage power station; Inputting the multiple historical energy storage scenarios into the thermal runaway propagation model to perform thermal runaway propagation evolution to obtain multiple thermal runaway evolution models; Discretize the first thermal runaway evolution model at a preset model grid scale to obtain a first three-dimensional temperature field distribution map; Performing temperature gradient calculation on the first three-dimensional temperature field distribution diagram to obtain a first temperature change rate array; Using a watershed algorithm to segment the first temperature change rate array to obtain a first sample area segmentation strategy, wherein the first sample area segmentation strategy includes temperature change characteristics of multiple samples in multiple sample monitoring areas; By analogy, the multiple thermal runaway evolution models are segmented to obtain multiple sample area segmentation strategies; Based on the temperature change characteristics, the multiple sample area segmentation strategies are similarly merged to obtain the M thermal disaster risk control monitoring areas.

6. The thermal disaster alarm system of the lithium-ion battery energy storage power station according to claim 5, characterized in that: The system comprises: Taking the first thermal disaster risk control monitoring area as the search condition, multiple thermal runaway data of the first area are retrieved from the historical thermal disaster alarm records; Extracting W groups of thermal runaway monitoring time series data of W thermal disaster risk control monitoring points in the first thermal disaster risk control monitoring point set from the plurality of first regional thermal runaway data; Performing thermal runaway feature aggregation on the W groups of thermal runaway monitoring time series data to obtain a first precursor risk feature set, wherein the first precursor risk feature set includes W thermal runaway precursor risk features, and the thermal runaway precursor risk features include a monitoring indicator change range and a monitoring indicator change rate; Taking the W thermal runaway precursor risk characteristics as risk judgment benchmarks, W edge thermal runaway prediction channels are constructed; The W edge thermal runaway prediction channels are connected in parallel, and the input ends of the W edge thermal runaway prediction channels are communicatively connected to the W wind control monitoring sensors of the W thermal disaster wind control monitoring points to complete the configuration of the first edge thermal runaway prediction node; By analogy, the risk control sensitivity analysis is performed on the M thermal disaster risk control monitoring point sets to obtain the M precursor risk feature sets; By analogy, the M edge thermal runaway prediction nodes are constructed in the M thermal disaster risk control monitoring areas according to the M precursor risk feature sets.

7. The thermal disaster alarm system of the lithium-ion battery energy storage power station according to claim 6, characterized in that: The thermal disaster alarm decision cloud receives and makes a thermal runaway suppression decision based on the M real-time thermal runaway risk features transmitted back by the M edge thermal runaway prediction nodes, and outputs a real-time thermal runaway prevention and control strategy. The system includes: Using the first thermal disaster risk control monitoring area as a search condition, calling multiple first historical area prevention and control strategies of the multiple first area thermal runaway data from the historical thermal disaster alarm records; The first thermal runaway suppression decision model is constructed using CNN network; Using the plurality of first-region thermal runaway data and the plurality of first-region historical prevention and control strategies as training data to perform parameter optimization on the first thermal runaway suppression decision model; By analogy, M thermal runaway suppression decision models of the M thermal disaster risk control monitoring areas are constructed, and the M thermal runaway suppression decision models are connected in parallel to the thermal disaster alarm decision cloud, so as to complete the decision function configuration of the thermal disaster alarm decision cloud; The thermal disaster alarm decision cloud receives and inputs the M real-time thermal runaway risk feature maps returned by the M edge thermal runaway prediction nodes into the M thermal runaway inhibition decision models to make thermal runaway inhibition decisions, and obtains the real-time thermal runaway prevention and control strategy, wherein the real-time thermal runaway prevention and control strategy includes M real-time prevention and control sub-strategies.

8. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement the thermal disaster alarm system of the lithium-ion battery energy storage power station according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.

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