Intelligent fire monitoring method and system for energy storage system

Abstract

The invention provides an intelligent fire monitoring method and system for an energy storage system, and the method comprises the steps: controlling an intelligent inspection machine with a body to move along a track preset in the energy storage system, carrying out the temperature scanning of a battery unit through an infrared imager carried by the intelligent inspection machine with the body, and obtaining infrared temperature data, meanwhile, collecting image data of a corresponding area through a visible light camera; based on the infrared temperature data and the image data, the battery unit in an abnormal state is recognized, the fire risk level is evaluated, if the fire risk level is the first-level hidden danger and the second-level hidden danger, the intelligent inspection machine is controlled to conduct key review on the area where the battery unit with the first-level hidden danger is located, and the battery unit with the second-level hidden danger is located; the key review comprises the steps of improving the inspection frequency, prolonging the residence observation time and triggering the instant fire extinguishing alarm; and if the hidden danger is the second-level hidden danger, the position and state information of the battery unit is recorded, and a centralized maintenance plan is entered, so that the fire monitoring efficiency and the response accuracy of the energy storage system are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of fire monitoring technology, and in particular to an intelligent fire monitoring method and system for energy storage systems. Background Technology

[0002] Energy storage systems, especially lithium-ion battery energy storage systems, pose a risk of fire due to thermal runaway during operation. Existing fire monitoring methods, such as fixed-point sensor monitoring or regular manual inspections, can typically only perform static or periodic checks and cannot provide dynamic and differentiated monitoring based on the actual condition of the batteries within the system.

[0003] Existing methods are insufficient to intelligently identify the level of fire hazards in the early stages and adjust the allocation of monitoring resources accordingly. This results in insufficient timeliness of monitoring high-risk areas, while resources may be wasted in low-risk areas, leading to low overall monitoring efficiency and response accuracy. Summary of the Invention

[0004] This invention provides an intelligent fire monitoring method and system for energy storage systems, which addresses the shortcomings of low efficiency and low response accuracy in fire monitoring of energy storage systems in the prior art.

[0005] In a first aspect, the present invention provides an intelligent monitoring method for fires in energy storage systems, comprising: Control the embodied intelligent inspection machine to move along a track pre-set inside the energy storage system; During the movement, the infrared imager mounted on the intelligent inspection machine scans the temperature of the battery unit to obtain infrared temperature data, and at the same time, the visible light camera collects image data of the corresponding area. Based on the infrared temperature data and the image data, battery cells with abnormal conditions are identified, and the fire risk level is assessed, including Level 1 hazards and Level 2 hazards. The monitoring behavior of the intelligent inspection robot is dynamically adjusted according to the fire risk level. If it is a Level 1 hazard, the intelligent inspection machine will be controlled to conduct a focused re-inspection of the area where the battery unit with the Level 1 hazard is located. The focused re-inspection includes increasing the inspection frequency, extending the observation time, and triggering an immediate fire extinguishing alarm. If it is a level 2 hidden danger, the location and status information of the battery unit will be recorded and the system will be included in the centralized maintenance plan.

[0006] According to the present invention, an intelligent fire monitoring method for energy storage systems includes assessing the fire risk level, comprising: Based on the infrared temperature data, battery cells whose temperature exceeds a first preset threshold are identified as potentially abnormal cells. The portion of the image data corresponding to the potential abnormal unit is retrieved for visual feature analysis. Based on the combination of the degree of temperature exceeding the standard and the results of visual feature analysis, the fire risk level of the potential abnormal unit is determined to be either a Level 1 or Level 2 hazard.

[0007] According to the present invention, an intelligent fire monitoring method for energy storage systems is provided, wherein determining the fire risk level of the potential abnormal unit as a Level 1 or Level 2 hazard based on a combination of the degree of temperature exceedance and visual feature analysis results includes: If the temperature of the potential abnormal unit exceeds the second preset threshold, it is determined to be a level one hidden danger, where the second preset threshold is higher than the first preset threshold; If the temperature of the potential abnormal unit is between the first preset threshold and the second preset threshold, then based on the visual feature analysis results, it is determined that: when at least one visual feature of bulging, leakage, or smoke is identified, it is determined to be a level one hazard; otherwise, it is determined to be a level two hazard.

[0008] According to the present invention, an intelligent fire monitoring method for an energy storage system includes dynamically adjusting the monitoring behavior of the embodied intelligent inspection machine, comprising: The dynamic inspection route of the energy storage system is updated based on the assessment results of the fire risk level. In areas with Level 1 hazards, the dynamic inspection route is configured to prioritize guiding the embodied intelligent inspection machine to perform a follow-up inspection after the current inspection task is completed.

[0009] The intelligent fire monitoring method for energy storage systems provided by the present invention further includes: Record historical evaluation results, corresponding sensor data, and subsequent verification conclusions; Based on the recorded data, the judgment logic for the fire risk level and the dynamic adjustment strategy for monitoring behavior are optimized through machine learning models.

[0010] According to the intelligent fire monitoring method for energy storage systems provided by the present invention, after triggering the immediate fire extinguishing alarm, the method further includes: The location information of the Level 1 hazard is sent to the fire control unit. Control the fire extinguishing device to move to the corresponding position to extinguish the fire.

[0011] The intelligent fire monitoring method for energy storage systems provided by the present invention further includes: The total number of battery cells assessed as having Level 1 and Level 2 hazards during the current inspection cycle is counted. When the total number exceeds a preset safety threshold, the inspection cycle of the intelligent inspection machine is shortened and its movement speed is increased.

[0012] According to the present invention, an intelligent fire monitoring method for an energy storage system is applied to a embodied intelligent inspection system, including a track installed on the energy storage system housing and an embodied intelligent inspection machine suspended on the track. The embodied intelligent inspection machine includes: ontology; Rolling wheels mounted on the main body are used to cooperate with the track to move the machine along the track; The sensor module integrated on the main body includes at least the infrared imager and the visible light camera; In addition, there is a communication module and a central processing and control module.

[0013] According to the present invention, an intelligent fire monitoring method for an energy storage system is provided, wherein the track includes a top track set on the top of the energy storage system box and a side track set on the side of the box, which together constitute a three-dimensional inspection network. The intelligent inspection machine engages with the top or side track via rolling wheels, and under the command of the central processing and control module, switches tracks at the transfer mechanism where the top and side tracks intersect, thereby achieving all-round coverage inspection of the outer surface of the energy storage box.

[0014] Secondly, the present invention provides an intelligent fire monitoring system for energy storage systems, comprising: The mobile module is used to control the embodied intelligent inspection machine to move along a track pre-set inside the energy storage system; The data acquisition module is used to scan the temperature of the battery unit during the movement using an infrared imager mounted on the intelligent inspection machine to obtain infrared temperature data, and at the same time to acquire image data of the corresponding area using a visible light camera. The assessment module is used to identify battery cells with abnormal conditions based on the infrared temperature data and the image data, and to assess the fire risk level, which includes level one hazard and level two hazard. The adjustment module is used to dynamically adjust the monitoring behavior of the embodied intelligent inspection machine according to the fire risk level. If it is a level one hazard, the embodied intelligent inspection machine is controlled to conduct a focused re-inspection of the area where the battery unit with the level one hazard is located. The focused re-inspection includes increasing the inspection frequency, extending the dwell observation time, and triggering an immediate fire extinguishing alarm. If it is a level two hazard, the location and status information of the battery unit are recorded, and it is included in the centralized maintenance plan.

[0015] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the intelligent fire monitoring method for energy storage systems as described above.

[0016] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent fire monitoring method for energy storage systems as described above.

[0017] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the intelligent fire monitoring method for energy storage systems as described above.

[0018] This invention provides an intelligent fire monitoring method and system for energy storage systems. By combining mobile inspection with dual data acquisition from infrared imagers and visible light cameras, it can perceive the physical state of battery cells in real time. Combined with intelligent assessment of fire risk levels and dynamic adjustment of the inspection robot's monitoring behavior, it achieves dynamic optimization of monitoring resources based on risk, significantly improving the timeliness and accuracy of high-risk fire detection. The track-based mobile inspection has a wide coverage area with no blind spots, overcoming the limitations of fixed sensor layouts. By differentiating between primary and secondary hazards, it avoids resource waste caused by over-responding to low-risk signals, while ensuring high alertness and rapid response to high-risk events, effectively improving overall monitoring efficiency and response accuracy. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an intelligent fire monitoring method for an energy storage system provided in this embodiment; Figure 2 This is a schematic diagram of the structure of the intelligent fire monitoring system for energy storage systems provided in this embodiment; Figure 3 This is a schematic diagram of the structure of the electronic device provided in this embodiment. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] Figure 1 This is a flowchart illustrating an intelligent fire monitoring method for an energy storage system provided in this embodiment.

[0023] like Figure 1 As shown in the figure, an intelligent fire monitoring method for energy storage systems provided by this invention is applicable to a embodied intelligent inspection system. It includes a track installed on the energy storage system enclosure and an embodied intelligent inspection machine suspended on the track. The track is divided into a top track located on the top of the energy storage system enclosure and a side track located on the side of the enclosure; the two are interconnected to form a three-dimensional inspection network. The embodied intelligent inspection machine includes a main body, rolling wheels, a sensor module, a communication module, and a central processing and control module. The sensor module includes at least an infrared imager and a visible light camera, and may also integrate smoke sensors, gas sensors, and temperature and humidity sensors. The rolling wheels adopt a telescopic connection structure, which can flexibly adjust the extension and retraction amount according to the track installation, achieving a tight engagement with the track and ensuring the safe operation of the machine on the track. The method mainly includes the following steps: 101. Control the intelligent inspection machine to move along the track preset inside the energy storage system.

[0024] Specifically, firstly, tracks are installed on the top and sides of the energy storage system enclosure. A transfer mechanism is set at the intersection of the tracks to allow the embodied intelligent inspection robot to switch between the top and side tracks. Through the cooperation of the top and side tracks, a comprehensive, three-dimensional inspection network is formed, ensuring coverage of all areas on the outer surface of the energy storage enclosure and completely solving the problems of limited fixed sensor layout and blind spots in traditional inspections.

[0025] The central processing and control module sends movement commands to the intelligent inspection robot, controlling it to autonomously navigate along a preset track. The movement speed can be flexibly adjusted according to actual inspection needs. In areas without abnormal hazards, the speed can be appropriately increased to improve overall inspection efficiency, while in key hazard areas identified later, the speed can be reduced to accurately collect battery cell status data and ensure monitoring accuracy.

[0026] Through a three-dimensional track network and autonomous machine movement, a comprehensive inspection of the battery cells of the energy storage system is achieved, laying the foundation for subsequent multi-dimensional data collection. At the same time, the telescopic connection design of the rolling wheels further improves the stability and safety of the machine's movement.

[0027] 102. During the movement, the infrared imager on the intelligent inspection machine scans the temperature of the battery unit to obtain infrared temperature data, and at the same time, the visible light camera collects image data of the corresponding area.

[0028] Specifically, as the embodied intelligent inspection robot moves along the track, it simultaneously activates all sensors in its sensor module to collect data in real time and continuously. Among these, the infrared imager continuously performs a comprehensive temperature scan of each battery cell it passes, converting the battery cell's temperature information into precise infrared temperature data. This provides a clear picture of the battery cell's heating status and allows for the timely detection of batteries with abnormal temperatures. The visible light camera simultaneously acquires image data of the corresponding temperature-scanned area, clearly recording the battery cell's appearance and providing valuable material for subsequent visual feature analysis.

[0029] The smoke sensor, gas sensor, and temperature and humidity sensor in the sensor module simultaneously collect data on smoke concentration, gas composition, temperature, and humidity in the surrounding environment. This auxiliary data supplements the status information of the battery cell and the surrounding environment from multiple dimensions, making data collection more comprehensive. All collected infrared temperature data, image data, and auxiliary sensor data are transmitted in real time to the central processing and control module via the communication module to ensure that the data can be processed and analyzed in a timely manner.

[0030] By using a dual-core data acquisition system of infrared imager and visible light camera, combined with supplementary data acquisition from other auxiliary sensors, multi-dimensional and comprehensive battery cell status data can be obtained. This avoids the one-sidedness of information caused by single data acquisition and provides reliable and comprehensive data support for subsequent anomaly identification and risk level assessment.

[0031] 103. Based on infrared temperature data and image data, identify battery cells with abnormal conditions and assess the fire risk level, which includes Level 1 and Level 2 hazards.

[0032] Specifically, the infrared temperature data is first analyzed and processed in a targeted manner to screen out battery cells whose temperature exceeds the first preset threshold. These battery cells are marked as potentially abnormal cells, thus initially identifying potential monitoring targets.

[0033] Subsequently, image data captured by visible light cameras, corresponding one-to-one with potential abnormal units, were retrieved. These images underwent detailed visual feature analysis, focusing on identifying any abnormal appearance features of the battery units, such as bulging, leakage, or smoke. Simultaneously, data from auxiliary sensors, such as smoke and gas sensors, was combined to detect abnormal smoke or harmful gases, resulting in multi-data fusion analysis to further verify the actual state of the potential abnormal units.

[0034] After completing the comprehensive data analysis, the fire risk level of potential abnormal units is accurately determined based on the combination of the degree of temperature exceeding the standard and the results of visual feature analysis. If the temperature of a potential abnormal unit exceeds the second preset threshold, and the second preset threshold is higher than the first preset threshold, regardless of whether the image data and auxiliary sensor data show other anomalies, the battery unit is directly determined to be a Level 1 hazard, indicating that its fire risk is extremely high and immediate action is required.

[0035] If the temperature of a potential abnormal unit is between the first preset threshold and the second preset threshold, the determination will be based primarily on the visual feature analysis results: when at least one visual feature of bulging, leakage, or smoke is identified, it is determined to be a Level 1 hazard; if no of the above visual abnormal features are identified, it is determined to be a Level 2 hazard, indicating that there is a certain hazard but no immediate fire risk.

[0036] Meanwhile, the central processing and control module continuously records historical assessment results, corresponding sensor data, and subsequent verification conclusions regarding potential hazards. This data is then used as training samples to input into the machine learning model. Through continuous optimization of the fire risk level determination logic and the dynamic adjustment strategy for monitoring behavior, the entire monitoring system can adapt to different operating conditions and hazard changes within the energy storage system, resulting in more accurate risk assessment and monitoring adjustments.

[0037] Furthermore, when processing sensor alarm signals, the central processing and control module prioritizes the alarm signals from various sensors based on their importance. Visible light camera and infrared imager alarms are in the highest priority tier, while alarms from other auxiliary sensors are in lower priority tiers. Specifically, infrared imager alarms account for 30% of the overall importance, and visible light camera alarms account for 70%. When multiple fault points alarm simultaneously in the energy storage system, the system prioritizes and addresses the fault points corresponding to the most important alarms, ensuring that critical potential hazards are dealt with first.

[0038] By employing a logic of initial screening of potential abnormal units, multi-data fusion verification, and precise risk level determination, combined with the ranking of alarm signal importance, the system achieves accurate identification and classification of potential hazards, avoiding misjudgments and omissions. Simultaneously, by recording relevant data and utilizing machine learning models for optimization, the system acquires self-learning capabilities, continuously improving the accuracy and adaptability of risk assessment.

[0039] 104. Adjust the monitoring behavior of the intelligent inspection machine dynamically according to the fire risk level.

[0040] Based on the risk level assessment results, the central processing and control module flexibly adjusts the monitoring behavior of the intelligent inspection robot to achieve dynamic optimization of monitoring resources.

[0041] Specifically, if an issue is assessed as a Level 1 hazard, the dynamic inspection route is updated first. After the embodied intelligent inspection robot completes its current inspection task, the central processing and control module sends instructions to the robot via the communication module, guiding it to prioritize a follow-up inspection of the area containing the battery unit with the Level 1 hazard, ensuring continuous monitoring of changes in the hazard. Compared to normal inspection frequency, this significantly shortens the inspection interval for that area, allowing for timely understanding of the hazard's development. Once the embodied intelligent inspection robot arrives at the area, it extends its dwell time, continuously collecting data through infrared imagers, visible light cameras, and other auxiliary sensors to comprehensively capture the real-time status of the hazard.

[0042] The central processing and control module immediately triggers an instant fire extinguishing alarm signal. On the one hand, it accurately sends the specific location information of the first-level hazard to the fire control unit through the communication module. On the other hand, it controls the fire extinguishing device mounted on the intelligent inspection machine or the fire extinguishing device matched with the energy storage system to move to the corresponding hazard location, promptly initiate fire extinguishing operations, and quickly contain the development of the fire.

[0043] After the fire extinguishing operation is completed, the intelligent inspection robot will re-inspect the area according to a preset program, using various sensors to detect any risk of reignition. Simultaneously, based on the actual situation of the potential hazard, it will determine whether to disconnect the power to the battery cells in the area to prevent the continued operation of faulty batteries from causing secondary risks. If signs of reignition are detected, the fire extinguishing procedure will be immediately restarted, and monitoring will be continuously strengthened.

[0044] If the risk is assessed as a Level 2 hazard, the central processing and control module will record the specific location information of the battery cell (including the box number, the track section, etc.) and the current status information (including infrared temperature data, image features, auxiliary sensor data, etc.) in detail, and store this information in the system database. At the same time, the hazard will be included in the centralized maintenance plan of the energy storage system and handled uniformly during subsequent centralized maintenance to avoid low-risk hazards occupying too many real-time monitoring resources.

[0045] In addition, at the end of each inspection cycle, the central processing and control module will specifically count the total number of battery cells assessed as having Level 1 and Level 2 hazards during the current inspection cycle. If the total number exceeds the preset safety threshold, it indicates that the energy storage system currently has a large number of hazards and the overall risk is high. At this time, the overall inspection cycle of the intelligent inspection machine will be automatically shortened, and the machine's movement speed will be appropriately increased. While ensuring monitoring quality, this will improve overall inspection efficiency and enable the faster detection of new hazards.

[0046] For Level 1 hazards, a comprehensive set of key measures are implemented, including priority review, increased frequency, extended monitoring, timely fire suppression, and follow-up inspections to prevent reignition, ensuring rapid response and effective control of high-risk hazards. Level 2 hazards are addressed through centralized planning to avoid resource waste. Furthermore, by dynamically adjusting inspection cycles and speeds, the allocation of monitoring resources is further optimized, comprehensively improving the efficiency and accuracy of fire monitoring and response in energy storage systems.

[0047] This embodiment utilizes mobile inspection combined with dual data acquisition from infrared imagers and visible light cameras to perceive the physical state of battery cells in real time. Combined with intelligent fire risk assessment and dynamic adjustment of the inspection robot's monitoring behavior, it achieves dynamic optimization of monitoring resources based on risk, significantly improving the timeliness and accuracy of high-risk fire detection. The track-based mobile inspection has a wide coverage area with no blind spots, overcoming the limitations of fixed sensor deployment. By differentiating between primary and secondary hazards, it avoids resource waste caused by over-responding to low-risk signals, while ensuring high alertness and rapid response to high-risk events, effectively improving overall monitoring efficiency and response accuracy.

[0048] Figure 2 This is a schematic diagram of the intelligent fire monitoring system for energy storage systems provided in this embodiment.

[0049] like Figure 2 As shown in the figure, this embodiment provides an intelligent fire monitoring system for energy storage systems, comprising: The mobile module 201 is used to control the embodied intelligent inspection machine to move along a track preset inside the energy storage system; The acquisition module 202 is used to scan the temperature of the battery unit by the infrared imager mounted on the intelligent inspection machine during the movement, obtain infrared temperature data, and at the same time acquire image data of the corresponding area by the visible light camera. The assessment module 203 is used to identify battery cells with abnormal conditions based on infrared temperature data and image data, and to assess the fire risk level, which includes level one hazard and level two hazard. The adjustment module 204 is used to dynamically adjust the monitoring behavior of the embodied intelligent inspection machine according to the fire risk level. If it is a level 1 hazard, the embodied intelligent inspection machine will be controlled to conduct a key re-inspection of the area where the battery unit with the level 1 hazard is located. The key re-inspection includes increasing the inspection frequency, extending the dwell observation time, and triggering an immediate fire extinguishing alarm. If it is a level 2 hazard, the location and status information of the battery unit will be recorded and the system will be included in the centralized maintenance plan.

[0050] Figure 3 This is a schematic diagram of the structure of the electronic device provided in this embodiment.

[0051] like Figure 3 As shown, the electronic device may include a processor 301, a communication interface 302, a memory 303, and a communication bus 304. The processor 301, communication interface 302, and memory 303 communicate with each other via the communication bus 304. The processor 301 can call logical instructions stored in the memory 303 to execute an intelligent fire monitoring method for the energy storage system.

[0052] Furthermore, the logical instructions in the aforementioned memory 303 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the intelligent fire monitoring method for energy storage systems provided by the above methods.

[0054] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the intelligent fire monitoring method for energy storage systems provided by the methods described above.

[0055] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0056] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent monitoring of fires in energy storage systems, characterized in that, include: Control the embodied intelligent inspection machine to move along a track pre-set inside the energy storage system; During the movement, the infrared imager mounted on the intelligent inspection machine scans the temperature of the battery unit to obtain infrared temperature data, and at the same time, the visible light camera collects image data of the corresponding area. Based on the infrared temperature data and the image data, battery cells with abnormal conditions are identified, and the fire risk level is assessed, including Level 1 hazards and Level 2 hazards. The monitoring behavior of the intelligent inspection robot is dynamically adjusted according to the fire risk level. If it is a Level 1 hazard, the intelligent inspection machine will be controlled to conduct a focused re-inspection of the area where the battery unit with the Level 1 hazard is located. The focused re-inspection includes increasing the inspection frequency, extending the observation time, and triggering an immediate fire extinguishing alarm. If it is a level 2 hidden danger, the location and status information of the battery unit will be recorded and the system will be included in the centralized maintenance plan.

2. The method according to claim 1, characterized in that, The assessment of fire risk levels includes: Based on the infrared temperature data, battery cells whose temperature exceeds a first preset threshold are identified as potentially abnormal cells. The portion of the image data corresponding to the potential abnormal unit is retrieved for visual feature analysis. Based on the combination of the degree of temperature exceeding the standard and the results of visual feature analysis, the fire risk level of the potential abnormal unit is determined to be either a Level 1 or Level 2 hazard.

3. The method according to claim 2, characterized in that, The method of determining the fire risk level of the potential abnormal unit as a Level 1 or Level 2 hazard based on the combination of the degree of temperature exceeding the standard and the results of visual feature analysis includes: If the temperature of the potential abnormal unit exceeds the second preset threshold, it is determined to be a level one hidden danger, where the second preset threshold is higher than the first preset threshold; If the temperature of the potential abnormal unit is between the first preset threshold and the second preset threshold, then based on the visual feature analysis results, it is determined that: when at least one visual feature of bulging, leakage, or smoke is identified, it is determined to be a level one hazard; otherwise, it is determined to be a level two hazard.

4. The method according to claim 1, characterized in that, The dynamic adjustment of the monitoring behavior of the embodied intelligent inspection machine includes: The dynamic inspection route of the energy storage system is updated based on the assessment results of the fire risk level. In areas with Level 1 hazards, the dynamic inspection route is configured to prioritize guiding the embodied intelligent inspection machine to perform a follow-up inspection after the current inspection task is completed.

5. The method according to claim 1, characterized in that, Also includes: Record historical evaluation results, corresponding sensor data, and subsequent verification conclusions; Based on the recorded data, the judgment logic for the fire risk level and the dynamic adjustment strategy for monitoring behavior are optimized through machine learning models.

6. The method according to claim 1, characterized in that, After triggering the immediate fire suppression alarm, the following is also included: The location information of the Level 1 hazard is sent to the fire control unit. Control the fire extinguishing device to move to the corresponding position to extinguish the fire.

7. The method according to claim 1, characterized in that, Also includes: The total number of battery cells assessed as having Level 1 and Level 2 hazards during the current inspection cycle is counted. When the total number exceeds a preset safety threshold, the inspection cycle of the intelligent inspection machine is shortened and its movement speed is increased.

8. The method according to any one of claims 1-7, characterized in that, The system is applied to a embodied intelligent inspection system, including a track installed on the energy storage system housing and an embodied intelligent inspection machine suspended on the track. The embodied intelligent inspection machine includes: ontology; Rolling wheels mounted on the main body are used to cooperate with the track to move the machine along the track; The sensor module integrated on the main body includes at least the infrared imager and the visible light camera; In addition, there is a communication module and a central processing and control module.

9. The method according to claim 8, characterized in that, The track includes a top track set on the top of the energy storage system box and side tracks set on the sides of the box, which together form a three-dimensional inspection network. The intelligent inspection machine engages with the top or side track via rolling wheels, and under the command of the central processing and control module, switches tracks at the transfer mechanism where the top and side tracks intersect, thereby achieving all-round coverage inspection of the outer surface of the energy storage box.

10. An intelligent fire monitoring system for an energy storage system, characterized in that, include: The mobile module is used to control the embodied intelligent inspection machine to move along a track pre-set inside the energy storage system; The data acquisition module is used to scan the temperature of the battery unit during the movement using an infrared imager mounted on the intelligent inspection machine to obtain infrared temperature data, and at the same time to acquire image data of the corresponding area using a visible light camera. The assessment module is used to identify battery cells with abnormal conditions based on the infrared temperature data and the image data, and to assess the fire risk level, which includes level one hazard and level two hazard. The adjustment module is used to dynamically adjust the monitoring behavior of the embodied intelligent inspection machine according to the fire risk level; wherein, if it is a level one hazard, the embodied intelligent inspection machine is controlled to conduct a key re-inspection of the area where the battery unit with the level one hazard is located, and the key re-inspection includes increasing the inspection frequency, extending the dwell observation time, and triggering an immediate fire extinguishing alarm. If it is a level 2 hidden danger, the location and status information of the battery unit will be recorded and the system will be included in the centralized maintenance plan.

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