Fire response control method and system for lithium battery energy storage box

Abstract

The present disclosure provides a fire response control method and system for a lithium battery energy storage box, relating to the technical field of lithium batteries, which comprises: collecting battery compartment layout information of the lithium battery energy storage box; reading data of the lithium battery energy storage box to obtain real-time control data; laying out temperature collection devices through the battery compartment layout information; obtaining temperature data collection results; collecting historical detection data with time sequence identifiers; constructing a lithium battery feature data set of the lithium battery energy storage box; constructing an abnormality monitoring model; outputting abnormality warning level information; generating multi-level fire response control data; and managing the lithium battery energy storage box. The technical problem of inaccurate and timely fire warning of the lithium battery energy storage box and inability to avoid fire accidents in time due to the single and inaccurate setting of the warning threshold in the prior art is solved.

Description

Technical Field

[0001] This disclosure relates to the field of lithium battery technology, specifically to a fire response control method and system for a lithium battery energy storage box. Background Technology

[0002] Lithium-ion batteries possess advantages such as high operating voltage, long cycle life, low self-discharge, high power handling capacity, light weight, no pollution, and minimal water consumption during production. Therefore, they are widely used in energy storage systems for hydropower, thermal power, wind power, and solar power plants, gradually becoming the mainstream energy storage product. However, when the stability of a lithium-ion battery energy storage system becomes uncontrollable, it may lead to abnormal power transmission and storage, or even fire and explosion. Typically, energy storage containers are equipped with fire alarm systems, usually featuring sensors for early warning and triggering.

[0003] Currently, existing technologies suffer from technical problems due to the relatively simple and imprecise setting of warning thresholds, which leads to inaccurate and untimely fire warnings for lithium battery energy storage boxes, thus failing to prevent fire accidents in a timely manner. Summary of the Invention

[0004] This disclosure provides a fire response control method and system for lithium battery energy storage boxes, which solves the technical problem in the prior art that the fire warning for lithium battery energy storage boxes is not accurate and timely due to the relatively simple and imprecise setting of the warning threshold, and thus cannot avoid fire accidents in time.

[0005] According to a first aspect of this disclosure, a fire response control method for a lithium battery energy storage box is provided, comprising: acquiring battery compartment layout information of the lithium battery energy storage box; reading data from the lithium battery energy storage box through the data interaction device to obtain real-time control data; deploying a temperature acquisition device based on the battery compartment layout information, wherein the temperature acquisition device has a acquisition location identifier; acquiring temperature data through the temperature acquisition device to obtain temperature data acquisition results; acquiring historical detection data of the lithium battery energy storage box, wherein the historical detection data has a time sequence identifier; extracting features from the historical detection data to construct a lithium battery feature dataset of the lithium battery energy storage box, wherein the lithium battery feature dataset includes feature categories and feature values; fusing the time sequence identifier with the lithium battery feature dataset, using the data fusion result as a base dataset to construct the anomaly monitoring model; inputting the temperature data acquisition results and the real-time control data into the anomaly monitoring model and outputting anomaly warning level information; generating multi-level fire response control data based on the anomaly warning level information and the battery compartment layout information; and managing the lithium battery energy storage box through the multi-level fire response control data.

[0006] According to a second aspect of this disclosure, a fire response control system for a lithium battery energy storage box is provided, comprising: a battery compartment layout information acquisition module, which is used to acquire battery compartment layout information of the lithium battery energy storage box; a data reading module, which is used to read data from the lithium battery energy storage box through the data interaction device to obtain real-time control data; a temperature acquisition device deployment module, which is used to deploy the temperature acquisition device based on the battery compartment layout information, wherein the temperature acquisition device has a acquisition location identifier; a temperature data acquisition module, which is used to acquire temperature data through the temperature acquisition device to obtain temperature data acquisition results; a historical detection data acquisition module, which is used to acquire historical detection data of the lithium battery energy storage box, wherein the historical detection data has a time sequence identifier; and feature data. The system comprises the following modules: a feature dataset construction module, which extracts features from the historical detection data to construct a lithium battery feature dataset for the lithium battery energy storage box, wherein the lithium battery feature dataset includes feature categories and feature values; an anomaly monitoring model construction module, which fuses the time-series identifier with the lithium battery feature dataset, using the fusion result as the base dataset to construct the anomaly monitoring model; an anomaly monitoring module, which inputs the temperature data acquisition results and the real-time control data into the anomaly monitoring model and outputs anomaly warning level information; a fire response control data generation module, which generates multi-level fire response control data based on the anomaly warning level information and the battery compartment layout information; and an energy storage box management module, which manages the lithium battery energy storage box using the multi-level fire response control data.

[0007] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0008] At least one processor; and

[0009] A memory communicatively connected to the at least one processor; wherein,

[0010] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0011] According to the fire response control method for a lithium battery energy storage box adopted in this disclosure, the following steps are taken: First, battery compartment layout information of the lithium battery energy storage box is collected. Second, data from the lithium battery energy storage box is read through the data interaction device to obtain real-time control data. Third, a temperature acquisition device is deployed based on the battery compartment layout information, wherein the temperature acquisition device has a acquisition location identifier. Fourth, temperature data is acquired through the temperature acquisition device to obtain temperature data acquisition results. Fifth, historical detection data of the lithium battery energy storage box is collected, wherein the historical detection data has a time sequence identifier. Sixth, features are extracted from the historical detection data to construct a lithium battery feature dataset of the lithium battery energy storage box, wherein the lithium battery feature dataset includes feature categories and feature values. Seventh, the time sequence identifier and the lithium battery feature dataset are fused, and the data fusion result is used as the basic dataset to construct the anomaly monitoring model. Eighth, the temperature data acquisition results and the real-time control data are input into the anomaly monitoring model to output anomaly warning level information. Ninth, multi-level fire response control data is generated based on the anomaly warning level information and the battery compartment layout information. Finally, the lithium battery energy storage box is managed through the multi-level fire response control data. This disclosure utilizes a temperature acquisition device and a data interaction device to communicate and obtain real-time control data and temperature data acquisition results of the lithium battery energy storage box. Based on the real-time control data and temperature data acquisition results, it generates abnormality level early warning information, sets different early warning thresholds according to the abnormality level early warning information, and performs multi-level fire control, thereby conducting fire management of the lithium battery energy storage box. This achieves the technical effect of improving the accuracy of fire early warning, enhancing the pertinence and efficiency of fire management, and ensuring the safe operation of the lithium battery energy storage box.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0014] Figure 1 A schematic flowchart illustrating a fire response control method for a lithium battery energy storage box provided in an embodiment of the present invention;

[0015] Figure 2 This is a schematic diagram of the process for managing the lithium battery energy storage box after issuing an early warning based on the normalized recovery temperature threshold in an embodiment of the present invention;

[0016] Figure 3 This is a schematic diagram illustrating the fire management process of lithium battery energy storage boxes based on early warning correction results in an embodiment of the present invention.

[0017] Figure 4 This is a schematic diagram of the fire response control system for a lithium battery energy storage box provided in an embodiment of the present invention;

[0018] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0019] Explanation of reference numerals in the attached diagram: Battery compartment layout information acquisition module 1, data reading module 2, temperature acquisition device layout module 3, temperature data acquisition module 4, historical detection data acquisition module 5, feature dataset construction module 6, anomaly monitoring model construction module 7, anomaly monitoring module 8, fire response control data generation module 9, energy storage box management module 10, electronic device 800, processor 801, memory 802, bus 803. Detailed Implementation

[0020] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0021] In order to solve the technical problem that the existing technology has relatively simple and imprecise warning threshold settings, which leads to inaccurate and untimely fire warnings for lithium battery energy storage boxes and the inability to avoid fire accidents in time, the inventors of this disclosure have creatively obtained a fire response control method and system for lithium battery energy storage boxes. Example 1

[0022] Figure 1 This application provides a diagram illustrating a fire response control method for a lithium battery energy storage box. The method is applied to a fire response control system, which is communicatively connected to a temperature acquisition device and a data interaction device. Figure 1 As shown, the method includes:

[0023] Step S100: Collect and obtain the battery compartment layout information of the lithium battery energy storage box;

[0024] Specifically, this application provides a fire response control method for a lithium battery energy storage box. The method is applied to a fire response control system, which is a system platform for fire early warning and fire management of the lithium battery energy storage box. A temperature acquisition device is a sensor for temperature acquisition, and a data interaction device is a device for acquiring control data from the lithium battery energy storage box. The fire response control system is communicatively connected to the temperature acquisition device and the data interaction device, enabling data exchange and transmission. Specifically, the lithium battery energy storage box includes multiple battery compartments, each containing batteries. The battery compartment layout information refers to the distribution of the multiple battery compartments within the lithium battery energy storage box.

[0025] Step S200: Read data from the lithium battery energy storage box through the data interaction device to obtain real-time control data;

[0026] Specifically, the data interaction device converts various changing physical quantities into analog electrical signals through corresponding sensors, then converts these analog electrical signals into digital signals for storage and preprocessing. It possesses functions of real-time acquisition, automatic storage, instant display, and automatic transmission. Real-time control data refers to the real-time operating data of the lithium battery energy storage box, including data such as charging voltage, discharging voltage, charging current, and discharging current.

[0027] Step S300: Deploy the temperature acquisition device using the battery compartment deployment information, wherein the temperature acquisition device has a acquisition location identifier;

[0028] Specifically, the temperature acquisition device is used to collect temperature data from the battery compartment. It includes various temperature sensors that can be selected and used according to the actual situation. The temperature acquisition device is installed in the lithium battery energy storage box according to the battery compartment layout information. It can be simply understood as installing a temperature acquisition device on each battery compartment. The temperature acquisition device has a collection location mark, which makes it easy to determine the collection location of temperature data and provides a basis for fire early warning and fire management of the lithium battery energy storage box.

[0029] Step S400: Collect temperature data using the temperature acquisition device to obtain temperature data acquisition results;

[0030] Specifically, temperature data is collected by deploying temperature acquisition devices to obtain temperature data acquisition results. The temperature data acquisition results include the acquisition results of each temperature acquisition device. In other words, the temperature data acquisition results are the temperature of each battery compartment.

[0031] Step S500: Collect historical detection data of the lithium battery energy storage box, wherein the historical detection data has a time sequence identifier;

[0032] Specifically, historical testing data refers to the testing data of lithium battery energy storage boxes over a period of time, such as the past month. Historical testing data includes historical temperature testing data, historical control data, and historical maintenance data of each battery compartment in the lithium battery energy storage box. The time sequence refers to the chronological order of the testing. The testing time of the historical testing data is marked according to the chronological order of the testing.

[0033] Step S600: Extract features from the historical detection data to construct a lithium battery feature dataset for the lithium battery energy storage box, wherein the lithium battery feature dataset includes feature categories and feature values;

[0034] Specifically, the lithium battery energy storage box includes multiple battery compartments, each containing batteries. The battery compartments that fail during historical maintenance are different. Feature extraction involves extracting abnormal features from historical detection data and constructing a lithium battery feature dataset based on these abnormal feature data. The lithium battery feature dataset includes feature categories and feature values. The feature category is the type of abnormality detected each time, and the feature value refers to the corresponding abnormality level.

[0035] Step S700: The time sequence identifier is fused with the lithium battery feature dataset, and the fusion result is used as the basic dataset to construct the anomaly monitoring model;

[0036] Specifically, by fusing time-series identifiers with lithium battery feature datasets, the detection time corresponding to abnormal data in the lithium battery feature dataset can be clearly obtained. Battery compartments that have experienced one or more anomalies can be prioritized for management. The data fusion results are used as the base dataset to construct an anomaly monitoring model. The anomaly monitoring model is a neural network model in machine learning, trained using the base dataset. In other words, the anomaly monitoring model has different monitoring thresholds for different battery compartments. For example, a low-level anomaly warning is issued when the temperature of a normal battery compartment exceeds 50°C. If a battery compartment has undergone multiple abnormal maintenance checks and its temperature exceeds 45°C, a low-level anomaly warning is also required. That is, different anomaly thresholds are set for batteries with different historical usage conditions to determine the warning level. The anomaly thresholds include temperature and control data thresholds. Based on this, the anomaly monitoring model is constructed to improve the accuracy of anomaly identification in lithium battery energy storage boxes.

[0037] Step S800: Input the temperature data acquisition results and the real-time control data into the anomaly monitoring model, and output the anomaly warning level information;

[0038] Specifically, the anomaly monitoring model is a functional model that analyzes temperature data acquisition results and real-time control data to obtain anomaly warning level information. In other words, the input data for the anomaly monitoring model are temperature data acquisition results and real-time control data, and the output data is anomaly warning level information. Different levels of warning information are set based on the temperature data acquisition results and real-time control data. The higher the level, the greater the degree of data anomaly. The anomaly warning level information includes the location of the anomaly. Since the temperature acquisition device has a acquisition location identifier, the acquisition location of the temperature data can be determined. Based on this, the location of the abnormal data can be identified. Therefore, the anomaly warning level information includes the anomaly warning level, the abnormal data, and the location of the anomaly.

[0039] Step S900: Generate multi-level fire response control data based on the abnormal warning level information and the battery compartment layout information;

[0040] Specifically, different levels of abnormal warnings require different fire response measures. For example, lower-level abnormal warnings may be addressed through physical cooling and heat dissipation measures, while higher-level abnormal warnings may require power outages for maintenance and repair. Based on this, the severity and location of the abnormality are determined according to the abnormal warning level information, and multi-level fire response control data is generated by combining the battery compartment layout information. This multi-level fire response control data includes multiple levels of fire control measures and their corresponding locations. The lower the abnormal warning level, the lower the level of the corresponding fire response control data. The lower the level of the fire response control data, the simpler the fire control measures, which may only involve simple physical cooling measures. The higher the level of the fire response control data, the more complex the fire control measures, which may require stopping work, power outages for maintenance, or even the use of fire-fighting equipment for fire control.

[0041] Step S1000: Manage the lithium battery energy storage box using the multi-level fire response control data.

[0042] Specifically, the lithium battery energy storage box is managed through multi-level fire response control data. Different levels of fire response control data correspond to different fire management methods, including cooling and early warning processing, thereby realizing fire management of the lithium battery energy storage box.

[0043] Among them, such as Figure 2 As shown, step S1010 in this embodiment includes:

[0044] Step S1011: Set the threshold for the monitoring and early warning level;

[0045] Step S1012: Determine whether the abnormal warning level information meets the supervision warning level threshold;

[0046] Step S1013: When the abnormal warning level information cannot meet the supervision warning level threshold, the normalized recovery temperature threshold is obtained by matching;

[0047] Step S1014: Perform early warning management on the lithium battery energy storage box according to the normalized recovery temperature threshold.

[0048] Specifically, a monitoring and early warning level threshold is set. This threshold is an indicator used to judge the level of abnormal early warning information. It is a level value that can be set according to actual conditions. For example, if there are 5 levels of abnormal early warning, the monitoring and early warning level threshold can be set to 2. This means that if the abnormality in the lithium battery energy storage box is within the monitoring and early warning level threshold range, it is considered that the abnormality is not serious, and the energy storage box can be automatically cooled. If the temperature meets the expected result through automatic cooling, the warning stops. If the warning level displayed by the abnormal early warning information does not meet the monitoring and early warning level threshold, a normalized recovery temperature threshold is obtained. The normalized recovery temperature threshold refers to the temperature of the lithium battery energy storage box when it is working normally and without any safety hazards. Management of the lithium battery energy storage box after issuing an early warning based on the normalized recovery temperature threshold, simply put, involves taking relevant measures (such as air conditioning cooling, heat dissipation, etc.) to restore the abnormal temperature of the lithium battery energy storage box to within the normalized recovery temperature threshold range. This achieves the technical effect of accurately judging abnormal data and targeting different levels of abnormal data.

[0049] In this embodiment, step S1020 includes:

[0050] Step S1021: Obtain a feedback monitoring window based on the abnormal warning level information;

[0051] Step S1022: The temperature acquisition device performs feedback temperature monitoring based on the feedback monitoring window to obtain the feedback temperature monitoring result;

[0052] Step S1023: Determine whether the feedback temperature monitoring result within the feedback monitoring window meets the normalization recovery temperature threshold;

[0053] Step S1024: When the feedback temperature monitoring result can meet the normalized recovery temperature threshold, the current warning of the lithium battery energy storage box is lifted.

[0054] Specifically, a feedback monitoring window is obtained based on the abnormal warning level information. This means that after acquiring the abnormal warning level information, the lithium battery energy storage box undergoes abnormal processing. The feedback monitoring window refers to a time period for temperature monitoring, which is also the time period for abnormal processing. Generally, the higher the abnormal warning level, the longer the abnormal processing time, and the larger the corresponding feedback monitoring window time range. The battery compartment temperature is monitored within this time period using a temperature acquisition device, and the feedback temperature monitoring result is obtained. This result represents the battery compartment temperature after abnormal processing. It is then determined whether the feedback temperature monitoring result within the feedback monitoring window meets the normal recovery temperature threshold. When the feedback temperature monitoring result meets the normal recovery temperature threshold, it indicates that the battery compartment temperature has dropped to the normal operating temperature range. At this point, the lithium battery energy storage box is in a normal and safe operating state, and no further warning is needed. The current warning for the lithium battery energy storage box is then deactivated, achieving the technical effect of feedback detection of abnormal processing results and improving the accuracy of warnings.

[0055] In this embodiment, step S900 further includes:

[0056] Step S910: When the abnormal warning level information meets the supervision warning level threshold, the battery compartment is matched and associated according to the abnormal warning level information and the battery compartment layout information;

[0057] Step S920: Generate power-off control information for the associated battery compartment;

[0058] Step S930: Determine the multi-level cooling control information, and obtain the multi-level fire response control data based on the power outage control information and the multi-level cooling control information.

[0059] Specifically, it is determined whether the abnormal warning level information meets the supervisory warning level threshold. If the abnormal warning level information meets the supervisory warning level threshold, then the associated battery compartment is matched based on the abnormal warning level information and the battery compartment layout information. In simple terms, the battery compartment where the abnormality occurred is determined based on the collection location of the abnormal temperature data in the abnormal warning level information and the battery compartment layout information. The battery compartment where the abnormality occurred is the associated battery compartment. Furthermore, power-off control information for the associated battery compartments is generated to control the disconnection of the circuit connections of the associated battery compartments. Multi-level cooling control information is determined based on the abnormal warning level information. Different warning levels require different cooling control methods. For example, a low warning level indicates that the abnormal temperature is not very high, and cooling methods such as air conditioning or fan cooling can be used. A high warning level can be cooled by liquid cooling or separate heat dissipation after power failure. Multi-level fire response control data is obtained based on the power-off control information and the multi-level cooling control information. This multi-level fire response control data includes different cooling control measures, achieving targeted cooling treatment for different levels of abnormal warnings. This avoids resource waste while ensuring the safe operation of the lithium battery energy storage box.

[0060] In this embodiment, step S1023 further includes:

[0061] Step S10231: When the feedback temperature monitoring result cannot meet the normalized recovery temperature threshold, the temperature change trend of the feedback temperature monitoring result is obtained;

[0062] Step S10232: Obtain the extreme and average temperatures from the feedback temperature monitoring results;

[0063] Step S10233: Generate a correlation fire early warning coefficient based on the temperature change trend, the temperature extreme values, and the temperature average values;

[0064] Step S10234: The abnormal warning level information is weighted and calculated using the associated fire warning coefficient, and fire management is carried out based on the weighted calculation result.

[0065] Specifically, it is determined whether the feedback temperature monitoring results within the feedback monitoring window meet the normalized recovery temperature threshold. When the feedback temperature monitoring results do not meet the normalized recovery temperature threshold, the temperature change trend of the feedback temperature monitoring results is obtained. The temperature change trend can be divided into three trends: temperature rising trend, temperature fluctuation trend, and temperature falling trend. The temperature rising trend means that the temperature in the feedback temperature monitoring results is rising. The temperature fluctuation trend means that the temperature in the feedback temperature monitoring results is fluctuating within a range. The temperature falling trend means that the temperature in the feedback temperature monitoring results is falling. Furthermore, the extreme and average temperatures from the feedback temperature monitoring results are obtained. Extreme temperatures include the maximum and minimum temperature values, while the average temperature is calculated by averaging all temperature values ​​from the feedback monitoring results. Then, a correlation fire warning coefficient is generated based on the temperature change trend, extreme temperatures, and average temperatures. Specifically, the temperature change trend, extreme temperatures, and average temperatures are weighted and distributed, and a weighted calculation is performed based on the weight distribution results to obtain the correlation fire warning coefficient. The correlation fire warning coefficient represents the degree of temperature anomaly. The abnormal warning level information is weighted and calculated using the correlation fire warning coefficient. Based on the weighted calculation results, personnel and equipment arrangements are made for fire management. The larger the weighted calculation result, the more fire-fighting resources are needed, thus improving the accuracy and efficiency of fire management.

[0066] The fire response control system is communicatively connected to the image acquisition device and the smoke monitoring device, such as... Figure 3 As shown, step S800 in this embodiment includes:

[0067] Step S1110: The image acquisition device is used to acquire an image of the lithium battery energy storage box to obtain the image acquisition result;

[0068] Step S1120: Collect smoke from the lithium battery energy storage box using the smoke monitoring device and obtain the smoke collection results;

[0069] Step S1130: Generate auxiliary warning features based on the image acquisition results and the smoke acquisition results;

[0070] Step S1140: Determine whether the auxiliary early warning feature matches the abnormal early warning level information;

[0071] Step S1150: When the auxiliary warning feature does not match the abnormal warning level information, the warning correction of the abnormal warning level information is performed according to the auxiliary warning feature;

[0072] Step S1160: Conduct fire safety management of the lithium battery energy storage box based on the early warning correction results.

[0073] Specifically, image acquisition devices, such as cameras and video cameras, are used to acquire images of the battery compartment inside the lithium battery storage box. Smoke detection devices, such as smoke detectors, are used to detect smoke within the lithium battery storage box. The image acquisition device acquires images of the battery compartment inside the lithium battery storage box, obtaining image acquisition results. The smoke detection device collects smoke from the lithium battery storage box, obtaining smoke collection results, including smoke concentration. The image acquisition results and smoke collection results are analyzed to generate auxiliary warning features. Simply put, the image acquisition results are analyzed to determine if there is an open flame or blackened components in the battery compartment, and the smoke collection results are analyzed for smoke concentration characteristics; these characteristics are the auxiliary warning features. The process involves determining whether the auxiliary warning features match the abnormal warning level information. In other words, it involves determining whether the auxiliary warning features will appear in the lithium battery energy storage box under the current abnormal warning level information. If they do, it means that the auxiliary warning features and the abnormal warning level information match. If the auxiliary warning features do not appear in the lithium battery energy storage box under the current abnormal warning level information, it means that the auxiliary warning features and the abnormal warning level information do not match, and the abnormal warning level information can be considered inaccurate. In this case, the abnormal warning level information is corrected based on the auxiliary warning features to make it match the auxiliary warning features. Then, fire management of the lithium battery energy storage box is carried out based on the warning correction results to achieve the technical effect of ensuring the accuracy of the warning.

[0074] In this embodiment, step S1200 includes:

[0075] Step S1210: Perform a statistical analysis of the warning signs in the lithium battery energy storage box to obtain the statistical results of the warning signs;

[0076] Step S1220: Based on the statistical results of the warning signs, perform battery compartment feature analysis on the lithium battery energy storage box to obtain the battery compartment feature analysis results;

[0077] Step S1230: Perform maintenance and replacement of the lithium battery energy storage box based on the battery compartment feature analysis results.

[0078] Specifically, the system performs early warning sign statistics on lithium battery energy storage boxes, obtaining the results including the number of warnings and their levels. Based on these results, the system analyzes the battery compartment characteristics to assess the wear and tear. For example, a high number of warnings at high levels indicates severe wear and tear on the battery compartment. This analysis provides the battery compartment characteristic analysis results, which are then used to maintain and replace the lithium battery energy storage boxes. In cases of severe wear, the entire lithium battery energy storage box is replaced; in cases of less severe wear, only parts of the battery compartment are repaired or batteries are replaced. This ensures the safe operation of the lithium battery energy storage boxes.

[0079] Based on the above analysis, this disclosure provides a fire response control method for lithium battery energy storage boxes. In this embodiment, real-time control data and temperature data acquisition results of the lithium battery energy storage box are obtained through communication between a temperature acquisition device and a data interaction device. Anomaly level warning information is generated based on the real-time control data and temperature data acquisition results. Different warning thresholds are set according to the anomaly level warning information, and multi-level fire control is performed to conduct fire management of the lithium battery energy storage box. This achieves the technical effect of improving the accuracy of fire warning, improving the pertinence and efficiency of fire management, and ensuring the safe operation of the lithium battery energy storage box. Example 2

[0080] Based on the same inventive concept as the fire response control method for a lithium battery energy storage box in the foregoing embodiments, such as Figure 4 As shown, this application also provides a fire response control system for a lithium battery energy storage box. The system is communicatively connected to a temperature acquisition device and a data interaction device. The system includes:

[0081] Battery compartment layout information acquisition module 1, the battery compartment layout information acquisition module 1 is used to acquire the battery compartment layout information of the lithium battery energy storage box;

[0082] Data reading module 2 is used to read data from the lithium battery energy storage box through the data interaction device to obtain real-time control data;

[0083] Temperature acquisition device deployment module 3, the temperature acquisition device deployment module 3 is used to deploy the temperature acquisition device through the battery compartment deployment information, wherein the temperature acquisition device has a acquisition location identifier;

[0084] Temperature data acquisition module 4, which is used to acquire temperature data through the temperature acquisition device and obtain temperature data acquisition results;

[0085] Historical detection data acquisition module 5, which is used to acquire historical detection data of the lithium battery energy storage box, wherein the historical detection data has a time sequence identifier;

[0086] Feature dataset construction module 6 is used to extract features from the historical detection data and construct a lithium battery feature dataset for the lithium battery energy storage box. The lithium battery feature dataset includes feature categories and feature values.

[0087] Anomaly monitoring model construction module 7 is used to fuse the time sequence identifier with the lithium battery feature dataset, and use the data fusion result as the basic dataset to construct the anomaly monitoring model.

[0088] Anomaly monitoring module 8 is used to input the temperature data acquisition results and the real-time control data into the anomaly monitoring model and output anomaly warning level information;

[0089] Fire response control data generation module 9, which is used to generate multi-level fire response control data based on the abnormal warning level information and the battery compartment layout information;

[0090] Energy storage box management module 10, which is used to manage the lithium battery energy storage box through the multi-level fire response control data.

[0091] Furthermore, the system also includes:

[0092] A monitoring and early warning level threshold setting module, wherein the monitoring and early warning level threshold setting module is used to set the monitoring and early warning level threshold;

[0093] An abnormal warning level information determination module is used to determine whether the abnormal warning level information meets the supervisory warning level threshold.

[0094] A normalized recovery temperature threshold matching module is used to match and obtain a normalized recovery temperature threshold when the abnormal warning level information cannot meet the supervision warning level threshold.

[0095] The post-warning management module is used to perform post-warning management on the lithium battery energy storage box based on the normalized recovery temperature threshold.

[0096] Furthermore, the system also includes:

[0097] A feedback monitoring window acquisition module is used to obtain a feedback monitoring window based on the abnormal warning level information.

[0098] A feedback temperature monitoring module is used to perform feedback temperature monitoring based on the feedback monitoring window through the temperature acquisition device to obtain feedback temperature monitoring results.

[0099] A feedback temperature monitoring result judgment module is used to determine whether the feedback temperature monitoring result in the feedback monitoring window meets the normalized recovery temperature threshold.

[0100] The warning cancellation module is used to cancel the current warning of the lithium battery energy storage box when the feedback temperature monitoring result can meet the normal recovery temperature threshold.

[0101] Furthermore, the system also includes:

[0102] The associated battery compartment matching module is used to match associated battery compartments according to the abnormal warning level information and the battery compartment deployment information when the abnormal warning level information meets the supervision warning level threshold.

[0103] A power failure control information generation module, which is used to generate power failure control information for the associated battery compartment;

[0104] A multi-level fire response control module is used to determine multi-level cooling control information and obtain multi-level fire response control data based on the power outage control information and the multi-level cooling control information.

[0105] Furthermore, the system also includes:

[0106] A temperature change trend acquisition module is used to obtain the temperature change trend of the feedback temperature monitoring result when the feedback temperature monitoring result cannot meet the normalized recovery temperature threshold.

[0107] A temperature value calculation module, which is used to obtain the extreme temperature values ​​and average temperature values ​​in the feedback temperature monitoring results;

[0108] The associated fire early warning coefficient generation module is used to generate an associated fire early warning coefficient based on the temperature change trend, the temperature extreme value, and the temperature average value.

[0109] The weighted calculation module is used to perform weighted calculation on the abnormal warning level information through the associated fire warning coefficient, and to perform fire management based on the weighted calculation result.

[0110] Furthermore, the system also includes:

[0111] An image acquisition module is used to acquire images of the lithium battery energy storage box through the image acquisition device and obtain image acquisition results.

[0112] A smoke collection module is used to collect smoke from the lithium battery energy storage box through the smoke monitoring device and obtain smoke collection results.

[0113] An auxiliary warning feature generation module is used to generate auxiliary warning features based on the image acquisition results and the smoke acquisition results.

[0114] An auxiliary warning feature determination module is used to determine whether the auxiliary warning feature matches the abnormal warning level information.

[0115] The warning correction module is used to correct the abnormal warning level information based on the auxiliary warning feature when the auxiliary warning feature does not match the abnormal warning level information.

[0116] The fire management module is used to manage the fire safety of the lithium battery energy storage box based on the early warning correction results.

[0117] Furthermore, the system also includes:

[0118] The warning sign statistics module is used to perform warning sign statistics on the lithium battery energy storage box and obtain the warning sign statistics results.

[0119] A battery compartment feature analysis module is used to perform battery compartment feature analysis on the lithium battery energy storage box based on the statistical results of the warning sign, and obtain the battery compartment feature analysis results.

[0120] A lithium battery energy storage box maintenance module is used to maintain and replace the lithium battery energy storage box based on the battery compartment feature analysis results.

[0121] The specific example of the fire response control method for a lithium battery energy storage box in the aforementioned Embodiment 1 is also applicable to the fire response control system for a lithium battery energy storage box in this embodiment. Through the foregoing detailed description of the fire response control method for a lithium battery energy storage box, those skilled in the art can clearly understand the fire response control system for a lithium battery energy storage box in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section. Example 3

[0122] Figure 5 This is a schematic diagram based on the third embodiment of the present disclosure, as shown below. Figure 5 As shown, the electronic device 800 in this disclosure may include a processor 801 and a memory 802.

[0123] Memory 802 is used to store programs. Memory 802 may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; memory may also include non-volatile memory, such as flash memory. Memory 802 is used to store computer programs (such as application programs, functional modules, etc. that implement the above methods), computer instructions, etc. The computer programs and computer instructions can be partitioned and stored in one or more memories 802. Furthermore, the computer programs, computer instructions, data, etc., can be accessed by processor 801.

[0124] The aforementioned computer programs and instructions can be stored in one or more partitions of memory 802. Furthermore, the aforementioned computer programs and instructions can be invoked by processor 801.

[0125] The processor 801 is configured to execute the computer program stored in the memory 802 to implement the various steps in the methods described in the above embodiments.

[0126] For details, please refer to the relevant descriptions in the preceding method embodiments.

[0127] The processor 801 and memory 802 can be independent structures or integrated structures. When the processor 801 and memory 802 are independent structures, the memory 802 and the processor 801 can be coupled together via bus 803.

[0128] The electronic device in this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principle are the same, and will not be repeated here.

[0129] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0130] According to embodiments of this disclosure, this disclosure also provides a computer program product comprising: a computer program stored in a readable storage medium, at least one processor of an electronic device being able to read the computer program from the readable storage medium, and the at least one processor executing the computer program causing the electronic device to perform the scheme provided in any of the above embodiments.

[0131] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders.

[0132] This document does not impose any restrictions as long as the desired results of the disclosed technical solution can be achieved.

[0133] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A fire response control method of a lithium battery energy storage tank, characterized by, The method is applied to a fire response control system in communication connection with a temperature acquisition device and a data interaction device, and the method comprises: acquiring battery compartment layout information of a lithium battery energy storage box; reading data of the lithium battery energy storage box through the data interaction device to obtain real-time control data, wherein the real-time control data comprises charging voltage, discharging voltage, charging current and discharging current; arranging the temperature acquisition device according to the battery compartment layout information, wherein the temperature acquisition device has an acquisition position identifier; acquiring temperature data through the temperature acquisition device to obtain a temperature data acquisition result; acquiring historical detection data of the lithium battery energy storage box, wherein the historical detection data has a time sequence identifier; extracting features from the historical detection data to construct a lithium battery feature data set of the lithium battery energy storage box, wherein the lithium battery feature data set comprises feature categories and feature values; fusing the time sequence identifier and the lithium battery feature data set to obtain a data fusion result as a basic data set, and constructing an abnormality monitoring model; inputting the temperature data acquisition result and the real-time control data into the abnormality monitoring model to output abnormality early warning level information; generating multi-level fire response control data according to the abnormality early warning level information and the battery compartment layout information; managing the lithium battery energy storage box through the multi-level fire response control data.

2. The method of claim 1, wherein, The method comprises: setting a supervision early warning level threshold; judging whether the abnormality early warning level information meets the supervision early warning level threshold; when the abnormality early warning level information cannot meet the supervision early warning level threshold, matching a normalizing recovery temperature threshold; managing the lithium battery energy storage box after early warning according to the normalizing recovery temperature threshold.

3. The method of claim 2, wherein, The method comprises: matching a feedback monitoring window according to the abnormality early warning level information; performing feedback temperature monitoring based on the feedback monitoring window through the temperature acquisition device to obtain a feedback temperature monitoring result; judging whether the feedback temperature monitoring result in the feedback monitoring window meets the normalizing recovery temperature threshold; when the feedback temperature monitoring result can meet the normalizing recovery temperature threshold, canceling the current early warning of the lithium battery energy storage box.

4. The method of claim 2, wherein, The method comprises: when the abnormality early warning level information meets the supervision early warning level threshold, matching an associated battery compartment according to the abnormality early warning level information and the battery compartment layout information; generating power-off control information of the associated battery compartment; determining multi-level cooling control information, and obtaining the multi-level fire response control data according to the power-off control information and the multi-level cooling control information.

5. The method of claim 3, wherein, The method comprises: when the feedback temperature monitoring result cannot meet the normalizing recovery temperature threshold, obtaining a temperature change trend of the feedback temperature monitoring result; obtaining a temperature extreme value and a temperature average value in the feedback temperature monitoring result; generating an associated fire early warning coefficient according to the temperature change trend, the temperature extreme value and the temperature average value; The abnormal early warning level information is weighted calculated through the correlation fire early warning coefficient, and fire management is performed according to the weighted calculation result.

6. The method of claim 1, wherein, The fire response control system is in communication connection with an image acquisition device and a smoke monitoring device, and the method comprises: Image acquisition of the lithium battery energy storage box is performed through the image acquisition device to obtain an image acquisition result; Smoke acquisition of the lithium battery energy storage box is performed through the smoke monitoring device to obtain a smoke acquisition result; An auxiliary early warning feature is generated according to the image acquisition result and the smoke acquisition result; It is judged whether the auxiliary early warning feature matches the abnormal early warning level information; When the auxiliary early warning feature does not match the abnormal early warning level information, early warning correction of the abnormal early warning level information is performed according to the auxiliary early warning feature; Fire management of the lithium battery energy storage box is performed according to the early warning correction result.

7. The method of claim 1, wherein, The method comprises: Early warning identification statistics of the lithium battery energy storage box are performed to obtain early warning identification statistical results; Battery compartment feature analysis of the lithium battery energy storage box is performed based on the early warning identification statistical results to obtain battery compartment feature analysis results; Maintenance and replacement of the lithium battery energy storage box are performed through the battery compartment feature analysis results.

8. A fire response control system for a lithium battery energy storage box, characterized in that, The system is in communication connection with a temperature acquisition device and a data interaction device, and the system comprises: A battery compartment layout information acquisition module is configured to acquire battery compartment layout information of the lithium battery energy storage box; A data reading module is configured to read data of the lithium battery energy storage box through the data interaction device to obtain real-time control data, wherein the real-time control data comprises charging voltage, discharging voltage, charging current and discharging current; A temperature acquisition device layout module is configured to layout the temperature acquisition device through the battery compartment layout information, wherein the temperature acquisition device has an acquisition position identifier; A temperature data acquisition module is configured to acquire temperature data through the temperature acquisition device to obtain temperature data acquisition results; A historical detection data acquisition module is configured to acquire historical detection data of the lithium battery energy storage box, wherein the historical detection data has a time sequence identifier; A feature data set construction module is configured to extract features from the historical detection data to construct a lithium battery feature data set of the lithium battery energy storage box, wherein the lithium battery feature data set comprises feature categories and feature values; An abnormality monitoring model construction module is configured to fuse the time sequence identifier and the lithium battery feature data set to construct an abnormality monitoring model, and use the data fusion result as a basic data set; An abnormality monitoring module is configured to input the temperature data acquisition results and the real-time control data into the abnormality monitoring model to output abnormal early warning level information. a fire-fighting response control data generation module, configured to generate multi-level fire-fighting response control data according to the abnormal early warning level information and the battery compartment layout information; an energy storage box management module, configured to manage the lithium battery energy storage box through the multi-level fire-fighting response control data.

9. An electronic device, comprising: comprise: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

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