Temperature detection control method and system for electric power energy storage module

Abstract

The invention relates to the technical field of energy storage equipment detection, and discloses a temperature detection control method and system for an electric power energy storage module, and the method comprises the steps: obtaining the infrared image data of the energy storage module from a first collection point, and recognizing a first target and a second target; if only the first target is recognized and the matching value is low, controlling the collection point to move in the first direction until the matching value reaches the standard, and updating the image to continue recognition; if only the first target exists, the temperature value of the first target is calculated and combined with the temperature value obtained by the proximity temperature measurement module to calculate a comprehensive temperature value as a detection temperature value. And if double targets are recognized and the distance is too short, controlling the acquisition point to move in a second direction, updating the image, and calculating a first target temperature value as a detection temperature value. And finally, alarming when the detected temperature value exceeds a preset alarm value. Through flexible adjustment of the position of the acquisition point and multi-mode temperature measurement, interference is effectively eliminated, the temperature of the energy storage module is accurately detected, early warning of abnormity is timely performed, and the detection accuracy is improved.

Description

Technical Field

[0001] This application relates to the technical field of energy storage device detection, and in particular to a temperature detection and control method and system for a power energy storage module. Background Art

[0002] In a solar energy storage system, the energy storage module, as a core component, can store excess electric energy during sufficient sunlight to ensure power supply during power outage periods; smooth power output and improve energy storage quality; cut peak and fill valley to reasonably allocate energy; act as an emergency backup power supply to ensure the operation of critical loads; and can also participate in power grid frequency modulation and voltage regulation to maintain power grid stability. However, during long-term operation of the energy storage module, affected by load changes, environmental factors, component aging, etc., faults such as poor contact, insulation damage, and internal short circuits may occur. If these faults cannot be detected and handled in time, it will lead to abnormal local power supply in the lightest case; in the worst case, it may cause serious safety accidents such as equipment overheating, arc discharge, or even explosion.

[0003] Temperature is an important characteristic parameter reflecting the working state of the energy storage module. When abnormalities occur inside the energy storage module, such as an increase in contact resistance, an increase in winding loss, and deterioration of the insulating medium, it is often accompanied by an increase in energy loss, which in turn leads to a local temperature rise. By monitoring the temperature of key parts of the energy storage module, its operating state can be intuitively and effectively judged. Temperature signals are easy to obtain and process. The temperature change can be converted into an electrical signal through a sensor, and then data analysis technology can be used to evaluate the working state of the energy storage module. Currently, in the temperature detection of energy storage modules, infrared thermal imaging technology has been widely used because it can non-contactedly obtain the surface temperature distribution image of the device. This technology can quickly scan the overall temperature of the energy storage module, facilitating the discovery of obvious temperature abnormal areas. However, in practical applications, infrared thermal imaging detection is easily interfered by the thermal radiation of other devices in the environmental background. For example, in a complex energy storage device layout, the heat generation of adjacent devices may cause confusion of temperature information in the infrared image, affecting the accurate judgment of the temperature of the target energy storage module. At the same time, when the energy storage module is blocked by other devices or structural components, infrared thermal imaging cannot effectively obtain the temperature data of its entire surface, and may miss the temperature abnormalities of key parts, resulting in inaccurate detection results or even misjudgment. Summary of the Invention

[0004] In order to improve the accuracy of the detection results, this application provides a temperature detection and control method and system for a power energy storage module.

[0005] In a first aspect, this application provides a temperature detection and control method for a power energy storage module, adopting the following technical solution: A temperature detection and control method for a power energy storage module includes the following steps: Obtain the infrared image data corresponding to the energy storage module in real time based on the first acquisition point; Identify the first target and the second target from the infrared image data; If the first target is identified and the second target is not identified, calculate the matching value between the infrared image data and the first target; if the matching value is less than the preset first reference value, control the first acquisition point to move a set distance in the first direction until the matching value is greater than the preset second reference value; update the infrared image data, and continue to identify the first target and the second target; If the first target is identified and the second target is not identified, calculate the first temperature value of the first target according to the infrared image data; control the proximity temperature measurement module to approach the first target and measure the temperature of the first target to obtain the second temperature value; calculate the comprehensive temperature value according to the first temperature value and the second temperature value, and output the comprehensive temperature value as the detected temperature value; Otherwise, if the first target and the second target are identified, calculate the distance between the first target and the second target as the target distance; if the target distance is less than the preset first reference distance, control the first acquisition point to move a set distance in the second direction until the target distance is greater than the preset second reference distance; where the first direction and the second direction have a set included angle; update the infrared image data, calculate the third temperature value of the first target according to the infrared image data, and output the third temperature value as the detected temperature value; If the output detected temperature value is greater than the preset alarm temperature value, give an alarm prompt.

[0006] By adopting the above technical solution, by identifying the first and second targets in a single infrared image, dynamically adjusting the position of the acquisition point, and adopting targeted strategies for different detection situations. When only the first target is identified, control the first acquisition point to move and shoot in the first direction (horizontal). If the matching value does not meet the expectation, it indicates that there may be interference. Then, use the proximity temperature measurement module for close-range measurement, and fuse the first temperature value calculated from the infrared image data with the second temperature value obtained by proximity temperature measurement to obtain the comprehensive temperature value as the detected temperature value, effectively avoiding temperature misjudgment caused by the thermal radiation of other devices and making the detection result more in line with the actual situation. When two targets are identified, judge whether to move vertically according to the distance. If so, control the first acquisition point to move and shoot in the second direction (vertical). If the distance between the two targets becomes farther, it means that there is no overlapping interference, and directly output the first temperature value, ensuring that accurate temperature data of the key parts of the energy storage module can be obtained even in the case of occlusion, reducing detection omissions and misjudgments caused by occlusion. Therefore, this method can effectively eliminate thermal radiation interference, solve the occlusion problem, realize multi-dimensional detection and verification, and at the same time quickly respond to abnormal temperatures and alarm through an automated process, significantly improving the accuracy, reliability, fault warning timeliness and detection efficiency of the detection, and ensuring the stable operation of the energy storage system.

[0007] Optionally, in the step of calculating the third temperature value of the first target according to the infrared image data, the following sub-steps are further included: Calculate the fourth temperature value of the second target according to the infrared image data; Calculate the temperature difference between the third temperature value and the fourth temperature value; If the temperature difference is positive, adjust the value of the set distance in positive correlation with the temperature difference. The larger the temperature difference, the larger the set distance, and the smaller the temperature difference, the smaller the set distance; If the temperature difference is negative, adjust the value of the set distance in negative correlation with the temperature difference. The larger the temperature difference, the smaller the set distance, and the smaller the temperature difference, the larger the set distance.

[0008] By adopting the above technical solution, the set distance is adjusted according to the difference between the third temperature value and the fourth temperature value, so that the detection process can dynamically adjust the position of the acquisition point according to the actual temperature difference between the targets. When the temperature difference is positive, it means that the temperature of the first target is higher than that of the second target. The larger the difference, the more obvious the thermal state difference between the two. At this time, increasing the set distance can ensure obtaining a clear image while avoiding the superposition of thermal interference that may be brought by close-range detection, and accurately distinguish the temperatures of the two targets; when the temperature difference is negative, reducing the set distance can more carefully capture the temperature information of the target with a lower temperature, prevent inaccurate temperature data caused by too far a distance, effectively avoid detection errors, and ensure that the detection result more truly reflects the temperature conditions of each part of the energy storage module.

[0009] Optionally, in the step of updating the infrared image data, the following sub-steps are further included: Calculate a first infrared imaging area value of the first target based on the infrared image data; Calculate a second infrared imaging area value of the second target based on the infrared image data; Calculate an area difference between the first infrared imaging area value and the second infrared imaging area value; If the area difference is positive, inversely adjust the set angle according to the area difference. The larger the area difference, the smaller the set angle; the smaller the area difference, the larger the set angle; If the area difference is negative, directly adjust the set angle according to the area difference. The larger the area difference, the larger the set angle; the smaller the area difference, the smaller the set angle.

[0010] By adopting the above technical solution, adjusting the set angle according to the area difference between the first target and the second target can make the moving direction of the acquisition point more in line with the actual situation of the target. When the area difference is positive, that is, the imaging area of the first target is larger than that of the second target, reducing the set angle can make the acquisition point capture the details of the first target more concentratedly during the subsequent movement, avoiding the shooting angle from being too dispersed due to too large an angle, ensuring that the boundaries and temperature distributions of the two targets can be clearly distinguished, and preventing the temperature detection accuracy from being affected by imaging overlap or blur; when the area difference is negative, increasing the set angle helps to comprehensively cover the smaller target, avoiding missing key information due to too small an angle, thereby improving the accuracy of temperature detection for the two targets and providing more reliable data support for the fault diagnosis of the energy storage module.

[0011] Optionally, in the step of controlling the proximity temperature measurement module to approach the first target and measuring the temperature of the first target to obtain a second temperature value, the following sub-steps are further included: The proximity temperature measurement module includes a drone and a temperature measurement sensor arranged on the drone. When the temperature measurement sensor approaches the energy storage module, it has a temperature measurement projection in the infrared image; Update the infrared image data; Identify the temperature measurement projection from the infrared image data; If the temperature measurement projection is not identified, give an occlusion warning; If the temperature measurement projection is identified, inversely adjust the set distance or the set angle according to the size of the temperature measurement projection; the larger the temperature measurement projection, the smaller the set distance or the set angle; the smaller the temperature measurement projection, the larger the set distance or the set angle.

[0012] By adopting the above technical solution, it is possible to judge whether there is an occlusion situation by identifying the temperature measurement projection in the infrared image. If the temperature measurement projection is not recognized, an occlusion warning is immediately given, which can timely remind the operation and maintenance personnel that there may be an occlusion at the key parts of the energy storage module, resulting in the temperature measurement sensor being unable to obtain data normally. This effectively makes up for the deficiency of simply relying on infrared images and target recognition for detection. Even in a complex energy storage device environment, when there is an occlusion of the temperature measurement sensor by cables, other structural components, etc., it can quickly sense and feedback, making the judgment of the state of the energy storage module by the detection system more reliable, and avoiding equipment failures or safety accidents caused by potential detection loopholes. According to the inverse correlation between the size of the temperature measurement projection, the set distance or the set angle is adjusted, so that the detection system can dynamically optimize the detection parameters according to the actual detection situation. When the temperature measurement projection is large, it indicates that the distance between the temperature measurement sensor and the energy storage module is relatively close. At this time, reducing the set distance or the set angle can avoid measurement errors or potential impacts on the equipment caused by too close a distance or improper angle; when the temperature measurement projection is small, the set distance or the set angle is increased to ensure that the temperature measurement sensor is in a suitable detection position, and accurate temperature data is obtained at the best perspective and distance, improving the accuracy and stability of the detection.

[0013] Optionally, in the step of controlling the proximity temperature measurement module to approach the first target and measuring the temperature of the first target to obtain a second temperature value, the following sub-steps are further included: The proximity temperature measurement module includes a drone and a temperature measurement sensor network arranged on the drone. Each grid node of the temperature measurement sensor network is provided with a temperature sensor. Obtain the temperature data of all the temperature sensors; Calculate the average value of all the temperature data as the second temperature value.

[0014] By adopting the above technical solution, a temperature measurement sensor network composed of multiple grid node temperature sensors is arranged on the drone, which can collect the temperature data of the first target in multiple dimensions, reduce errors by using data redundancy and complementarity, and use the average value of all temperature data as the second temperature value, which not only improves the accuracy of temperature detection, truly reflects the temperature condition of the target, but also enhances the reliability of the detection result; at the same time, it realizes fast and comprehensive detection, adapts to complex working conditions, and also provides rich data for intelligent operation and maintenance, helps with accurate fault location and operation and maintenance decision-making, and effectively improves the efficiency and quality of the energy storage module detection.

[0015] Optionally, in the step of obtaining the temperature data of all the temperature sensors, the following sub-steps are further included: Set a sensor adjustment matrix corresponding to the temperature measurement sensor network, and the elements in the sensor adjustment matrix are the weighting coefficients corresponding to each temperature sensor; Calculate the temperature data according to the sensor adjustment matrix and the collected temperature values of the temperature sensors; Adjust the weighting coefficient at the middle position of the sensor adjustment matrix in a positive correlation with the second temperature value, and adjust the weighting coefficient at the edge position of the sensor adjustment matrix in an inverse correlation with the second temperature value.

[0016] By adopting the above technical solution, by setting the sensor adjustment matrix and the weighting coefficient, it is possible to perform differential weighting calculations on the collected temperature values according to the characteristics of different temperature sensors, the installation positions, and the importance of detecting the target temperature. When the second temperature value is relatively high, it indicates that there may be a temperature anomaly in the target central area. At this time, increasing the weighting coefficient at the middle position can further highlight the weight of the temperature data in the central area and more accurately capture the abnormal temperature change; when the second temperature value is relatively low, reducing the weighting coefficient at the edge position can reduce the influence of edge interference data on the overall result, ensure the reliability and accuracy of the temperature data, and effectively improve the ability of the detection system to capture temperature anomalies.

[0017] Optionally, the method further includes the following steps: If the third temperature value is greater than a preset reference temperature value, identify the locally overheated part corresponding to the third temperature value from the infrared image data; Collect the first magnetic field signal of the locally overheated part; If the first magnetic field signal is greater than a preset reference signal value, give a device failure prompt.

[0018] By adopting the above technical solution, it no longer simply relies on temperature data to judge the state of the energy storage module. Instead, when it is found that the first target temperature is higher than the preset reference temperature value, that is, when there are signs of local overheating, the magnetic field signal of the corresponding part is further collected. Temperature anomalies are often an intuitive manifestation of energy storage module failures, but single temperature detection may have misjudgments. For example, non-fault factors such as a sudden increase in ambient temperature may also cause the temperature to rise. The magnetic field signal can reflect the electrical states such as the internal current distribution and electromagnetic induction of the energy storage module. When there are faults such as poor contact and winding short circuit inside, the magnetic field signal will change significantly. Through the dual detection and mutual verification of temperature and magnetic field signals, faults are judged from two dimensions of thermal effect and electromagnetic effect, effectively eliminating the interference of single factors, significantly improving the accuracy of fault diagnosis, and avoiding misjudgments and missed judgments. The change of the magnetic field signal often occurs before the serious damage caused by device failures. Detecting the magnetic field signal in time after the temperature rises can capture abnormal information at the initial stage of fault development.

[0019] Optionally, the method further includes the following steps: Obtain the temperature data corresponding to the locally overheated part; If the temperature data is greater than a preset reference data, when the temperature measurement sensor network covers the locally overheated part, collect a second magnetic field signal of the locally overheated part; Calculate a signal difference between the first magnetic field signal and the second magnetic field signal; If the signal difference is less than a preset signal reference value, perform an occlusion warning.

[0020] By adopting the above technical solution, in the complex operating environment of the energy storage device, the magnetic field signal is easily interfered by other surrounding devices, metal structures and other factors. Simply detecting the magnetic field signal at a single moment may not accurately reflect the true state of the device. This technical solution can effectively eliminate the interference of environmental factors on the detection of the magnetic field signal by comparing the magnetic field signal differences before and after the temperature measurement sensor network covers. When the signal difference is less than the preset reference value and an occlusion warning is performed, it indicates that the abnormal magnetic field signal detected currently may be caused by external occlusion or environmental interference, rather than a device itself fault, avoiding false detection results caused by environmental interference, ensuring the authenticity and reliability of the detection results, and providing solid data support for the state assessment of the energy storage module. From judging the temperature data of the locally overheated part, to collecting and calculating the difference of the magnetic field signal, and then to issuing an occlusion warning according to the difference result, a complete closed-loop detection process is formed. Each link is closely connected and mutually verified, effectively making up for the possible loopholes in a single detection step.

[0021] In a second aspect, the present application provides a temperature detection and control system for a power energy storage module, adopting the following technical solution: A temperature detection and control system for a power energy storage module includes a processor, and the processor executes the steps of the temperature detection and control method for the power energy storage module as described in any one of the above.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: By identifying the targets in the infrared image, dynamically adjusting the acquisition point positions and detection strategies, combining infrared image temperature calculation, proximity temperature measurement fusion, and multi-node data acquisition and weighted processing of the temperature measurement sensor network, accurately obtaining the temperature data of the energy storage module, effectively avoiding thermal radiation interference and occlusion effects, and improving the detection accuracy; based on dual detection and difference analysis of temperature and magnetic field signals, accurately identifying the fault locations and types, and reducing the risks of misjudgment and missed judgment.

[0023] According to the temperature difference between targets, the imaging area difference, and the temperature measurement projection size, intelligently adjusting detection parameters such as the set distance and set angle of the acquisition points, enabling the detection system to adapt to complex working conditions and different detection requirements, optimizing the detection process, and improving the detection efficiency.

[0024] Build a complete closed loop of the detection process, and provide timely warnings for temperature anomalies, magnetic field signal anomalies, and potential obstruction risks. Discover hidden dangers of equipment failures and detection interference factors in advance, enhance detection reliability, provide sufficient response time for operation and maintenance personnel, and ensure the stable operation of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The invention is a step diagram of a temperature detection and control method of an electric energy storage module.

[0026] Figure 2 It is a comparison chart of the infrared images after the first acquisition point is moved horizontally.

[0027] Figure 3 It is the infrared image comparison chart after the first acquisition point is moved vertically.

[0028] Figure 4 A diagram showing the steps for dynamically adjusting the set distance based on the temperature difference.

[0029] Figure 5 This is a step diagram of adjusting the set angle according to the area difference in the step of updating infrared image data.

[0030] Figure 6 This is a step diagram of controlling the proximity temperature measurement module to approach the first target, measuring the temperature of the first target to obtain a second temperature value, and adjusting the set distance or the set angle according to the anti-correlation of the size of the temperature measurement projection. DETAILED DESCRIPTION

[0031] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0032] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0033] The present application discloses a temperature detection and control method for an electric energy storage module. Figure 1 , Figure 2 and Figure 3 , including the following steps: Obtain the infrared image data corresponding to the energy storage module in real time based on the first acquisition point; use a high-precision infrared thermal imaging device to obtain the infrared image data corresponding to the energy storage module in real time. The image data visually presents the temperature distribution on the surface of the energy storage module in the form of thermal radiation.

[0034] The image recognition algorithm identifies the first target and the second target from the infrared image data; the first target and the second target respectively correspond to the key energy storage devices and adjacent energy storage devices of the energy storage module. Among them, the thermal radiation generated by the adjacent energy storage devices may interfere with the detection of the first target.

[0035] When the first target is detected but the second target is not found, the system calculates the matching value between the infrared image data and the first target. This matching value comprehensively considers factors such as the clarity, integrity, and feature matching degree of the target in the image. Among them, clarity measures the degree of image edge and gray change through gradient amplitude statistics and Laplacian variance; integrity evaluates the target contour and area integrity through contour coincidence degree and area ratio; feature matching degree is achieved by comparing the feature vectors extracted by the key point matching algorithm and deep learning. Finally, different weights are assigned to the calculation results of the three, and the final matching value is obtained through weighted summation to quantify the degree of fit between the infrared image data and the first target. If the matching value is lower than the preset first reference value, it indicates that the image under the current perspective is interfered or blocked, resulting in insufficient acquisition of the first target information. At this time, control the first acquisition point to move a set distance in the first direction (horizontal), and this distance is optimized according to the equipment layout and detection accuracy requirements. During the movement, the system continuously updates the infrared image data and repeats the target recognition operation until the matching value exceeds the preset second reference value. If only the first target is finally recognized, the system calculates the first temperature value of the first target based on the infrared image data; at the same time, dispatch a proximity temperature measurement module equipped with a professional temperature sensor, such as a flexible and mobile drone, a highly adaptable humanoid robot, an unmanned vehicle convenient for ground operation, or a robotic arm with high-precision operation ability, to approach the first target for close-range and high-precision temperature measurement to obtain the second temperature value. Through a scientific algorithm, the first temperature value and the second temperature value are weighted and averaged for fusion calculation to obtain the comprehensive temperature value as the detection temperature value. This process effectively eliminates the interference of the thermal radiation of adjacent devices and ensures that the detection result truly reflects the temperature condition of the first target.

[0036] If the first target and the second target are simultaneously identified in the infrared image data, the system will accurately calculate the target distance between the two. When the target distance is less than the preset first reference distance, it means that the first target and the second target overlap or are adjacent and interfere in the image, which may affect the accuracy of temperature detection. At this time, control the first acquisition point to move a set distance in the second direction (vertical direction) at a set angle with the first direction. By adjusting the shooting height and angle, optimize the image perspective until the target distance is greater than the preset second reference distance. After completing the position adjustment, the system updates the infrared image data and calculates the third temperature value of the first target based on the new image, and outputs it as the detected temperature value. This strategy effectively solves the problem of detection blind spots caused by mutual occlusion of devices.

[0037] The system compares the output detected temperature value with the preset alarm temperature value in real time. Once the detected temperature value exceeds the threshold, immediately activate the multi-level alarm mechanism, and transmit the abnormal information to the operation and maintenance personnel in the first time through various methods such as sound and light alarm, SMS push, and background system early warning, so as to take measures in time to avoid the expansion of faults and ensure the safe and stable operation of the energy storage system.

[0038] By dynamically adjusting the position of the acquisition point, multi-modal data fusion, and intelligent decision-making strategies, effectively overcome the two major problems of thermal radiation interference and equipment occlusion in traditional infrared detection. Not only realizes the multi-dimensional detection and verification of the temperature of the energy storage module, but also significantly shortens the detection cycle through a fully automated process, significantly improves the accuracy, reliability of detection and the timeliness of fault warning, and provides a solid technical guarantee for the stable operation of the energy storage system.

[0039] Refer to Figure 4 , in the process of calculating the third temperature value of the first target based on the infrared image data, in order to further improve the accuracy of the energy storage module detection, the following sub-steps are also included: Based on the infrared thermal imaging data analysis algorithm, calculate the third temperature value of the first target and the fourth temperature value of the second target respectively from the infrared image data. This calculation process is not a simple numerical reading, but comprehensively considers multiple factors such as the pixel value, thermal radiation intensity, and environmental temperature compensation of the infrared image data to ensure that the obtained temperature value can reflect the actual temperature of the target as accurately as possible.

[0040] Calculate the temperature difference between the third temperature value and the fourth temperature value. This difference is the key basis for adjusting the set distance subsequently, and it reflects the degree of difference in the thermal state between the first target and the second target.

[0041] According to the positive and negative conditions of the temperature difference, the system will adopt different adjustment strategies to dynamically adjust the value of the set distance.

[0042] If the temperature difference is positive, that is, the temperature of the first target is higher than that of the second target, it indicates that the first target is in a relatively hotter state. At this time, the larger the temperature difference, the more obvious the difference in their thermal states. In this case, the system will adjust the set distance in a positive correlation with the temperature difference, that is, the larger the temperature difference, the larger the set distance; the smaller the temperature difference, the smaller the set distance. For example, in the detection scenario of the energy storage module in a large substation, the first target is the transformer winding operating at high load, and the second target is the adjacent switch cabinet operating at relatively low load. After calculation, the third temperature value of the transformer winding is 80°C, and the fourth temperature value of the switch cabinet is 30°C, with a temperature difference of 50°C. Since this difference is large, it indicates that the difference in their thermal states is obvious. To avoid the high heat emitted by the transformer winding during close-range detection from interfering with the temperature detection of the switch cabinet and ensure that the infrared images of the two targets can be clearly obtained, the system will increase the set distance. For example, the originally set distance of 2 meters is adjusted to 3 meters. In this way, both the thermal interference superposition can be reduced, and the temperatures of the two targets can be accurately distinguished.

[0043] If the temperature difference is negative, that is, the temperature of the first target is lower than that of the second target, it indicates that the first target is in a relatively colder state. At this time, the system will adjust the set distance in an inverse correlation with the temperature difference, that is, the larger the temperature difference, the smaller the set distance; the smaller the temperature difference, the larger the set distance. For example, in the detection of the energy storage module in a data center, the first target is the standby uninterruptible power supply (UPS), and the second target is the server power module operating at full load. After calculation, the third temperature value of the UPS is 25°C, and the fourth temperature value of the server power module is 60°C, with a temperature difference of -35°C. Since this difference is large, it indicates that the difference in their thermal states is obvious. To more carefully capture the temperature information of the relatively cooler UPS and prevent inaccurate temperature data due to too far a distance, the system will reduce the set distance. For example, the originally set distance of 3 meters is adjusted to 1.5 meters. Through such adjustment, it can be ensured that the detection results more truly reflect the temperature conditions of each part of the energy storage module and effectively avoid detection errors.

[0044] Through the above strategy of dynamically adjusting the set distance based on the temperature difference, the detection process can more flexibly and accurately adapt to the actual temperature differences between targets in different energy storage modules, thereby significantly improving the accuracy and reliability of the energy storage module detection and providing more powerful guarantee for the safe and stable operation of the energy storage system.

[0045] Refer to Figure 5 , in the step of updating the infrared image data, to further optimize the detection perspective and accuracy, this method realizes the intelligent adaptation of the moving direction of the acquisition point through a dynamic adjustment mechanism based on the difference in the imaging areas of the targets and a refined analysis of the infrared imaging areas of the first target and the second target. The specific steps are as follows: Through image recognition and edge detection algorithms, the contour boundaries of the first target and the second target are accurately outlined from the infrared image data. On this basis, combined with the conversion relationship between the pixel density and the actual physical size of the infrared image, the first infrared imaging area value of the first target on the image and the second infrared imaging area value of the second target on the image are calculated respectively.

[0046] Perform a difference operation on the imaging area values of the two targets to obtain the area difference between the first infrared imaging area value and the second infrared imaging area value. The difference intuitively reflects the difference in the space ratios occupied by the two targets in the infrared image.

[0047] According to the positive and negative characteristics of the area difference, the system implements a differential adjustment strategy: If the area difference is positive, it indicates that the imaging area of the first target in the infrared image is larger than that of the second target. At this time, the system will inversely adjust the set angle of the acquisition point according to the size of the area difference, that is, the larger the area difference, the smaller the set angle; the smaller the area difference, the larger the set angle. For example, in the detection of energy storage modules in an energy storage station, the first target is a large energy storage device (the imaging area of the radiator area is larger), and the second target is an adjacent small energy storage device. After calculation, the imaging area of the first target is 800 pixels², and the second target is 200 pixels², with an area difference of 600 pixels². Due to the significant difference, the system automatically reduces the set angle of the acquisition point from the initial 45° to 20°, making the shooting perspective more focused on the key heat dissipation area of the transformer. During the subsequent moving shooting process, it effectively avoids the loss of details caused by the dispersed perspective, accurately captures the temperature changes of the local hot spots of the large energy storage device, and clearly distinguishes the boundaries of the two devices, preventing the confusion of temperature data caused by imaging overlap.

[0048] If the area difference is negative, that is, the imaging area of the second target is larger than that of the first target, the system will adjust the set angle in positive correlation with the area difference. The larger the area difference, the larger the set angle; the smaller the area difference, the smaller the set angle. For example, in the detection scenario of an energy storage station, it is calculated that the imaging area of the first target is 150 pixels², and the second target is 600 pixels², with an area difference of -450 pixels². In view of this, the system quickly increases the set angle from 30° to 55°, widening the shooting field of view to ensure that the complete contour and temperature information of the first target are fully covered, avoiding the omission of abnormal heat generation of the first target due to too narrow a perspective, while taking into account the temperature detection of the second target, and achieving synchronous and accurate monitoring of targets of different sizes.

[0049] Through the above dynamic adjustment mechanism based on the difference in the target imaging area, the detection system can real-time sense the changes in the target morphology and intelligently optimize the shooting angle and the moving direction of the acquisition point. This adaptive adjustment strategy effectively solves the problems of missing image information and blurred target boundaries caused by fixed perspectives in traditional detections, significantly improves the quality of infrared image data and the accuracy of temperature detection, provides more comprehensive and reliable data support for the fault diagnosis of energy storage modules, and strongly guarantees the stable operation of the energy storage system.

[0050] Referring to Figure 6 , in the process of controlling the proximity temperature measurement module to accurately measure the temperature of the first target, to further ensure the reliability of data acquisition and the optimization of detection parameters, this method is implemented based on the dynamic monitoring and adjustment mechanism of the temperature measurement projection. The specific implementation process is as follows: Use a drone equipped with a high-precision temperature sensor as the core carrier of the proximity temperature measurement module. When the drone approaches the energy storage module with the temperature sensor, the thermal radiation of the sensor itself will form a unique temperature measurement projection in the infrared image. This projection not only intuitively presents the spatial position of the sensor, but also its shape and size contain key information such as the distance and angle between the sensor and the target. The system continuously updates the infrared image data and uses image recognition and feature extraction algorithms to accurately lock the contour and position of the temperature measurement projection from the massive image information.

[0051] If the temperature measurement projection cannot be recognized in the updated infrared image data, it means that the field of view of the temperature sensor may be blocked by cables, metal brackets, other equipment components, etc. around the energy storage module, resulting in the sensor being unable to normally collect the target temperature data. At this time, the system will immediately trigger the occlusion warning mechanism and send warning messages to the operation and maintenance personnel through multiple channels such as audible and visual alarms, pop-up prompts on the operation and maintenance platform, and SMS push, and detailedly mark the position of the energy storage module suspected of being occluded and the sensor disconnection status. For example, in the detection task of a large substation, when the drone carried the temperature sensor and approached a group of high-voltage switch cabinets, the temperature measurement projection disappeared in the infrared image due to the temporary cable bridge installed on the cabinet top blocking the sensor's view. The system quickly issued an occlusion warning, and the operation and maintenance personnel intervened in time to adjust the flight path of the drone, avoiding the detection blind area caused by data loss.

[0052] If the temperature measurement projection is successfully recognized, the system will implement a dynamic adjustment strategy based on the size of the projection. When the area of the temperature measurement projection is large, it indicates that the distance between the temperature measurement sensor and the energy storage module is relatively close. At this time, if the current distance or angle is maintained, it may cause measurement errors due to thermal radiation interference, and there is even a risk of colliding with the equipment. The system will automatically reduce the set distance or set angle according to the inverse correlation principle. For example, when detecting an energy storage device, as the drone gradually approaches, the temperature measurement projection occupies a large area in the infrared image. The system will adjust the flight distance of the drone from the initial 1.5 meters to 1 meter and fine-tune the shooting angle to ensure that the sensor obtains stable and accurate temperature data within a safe distance. Conversely, when the area of the temperature measurement projection is small, it means that the distance between the sensor and the target is far or the angle is not good. The system will increase the set distance or set angle to optimize the detection position. For example, when detecting the energy storage module of a high-rise power distribution tower, due to the initial flight height of the drone being too high, the temperature measurement projection only presents a small spot in the infrared image. The system immediately controls the drone to descend, adjusts the distance from 3 meters to 2 meters, and expands the shooting angle so that the sensor can completely cover the target area and obtain comprehensive temperature information.

[0053] This intelligent adjustment mechanism based on the temperature measurement projection realizes the adaptive optimization of detection parameters by real-time sensing the spatial relationship between the sensor and the energy storage module. It not only effectively makes up for the defect of being easily blocked and interfered in traditional infrared detection, but also provides double guarantees for the accurate positioning and safe operation of the temperature measurement sensor in the complex and changeable energy storage device environment. By dynamically adjusting the detection distance and angle, it significantly improves the accuracy and stability of temperature data acquisition, lays a solid data foundation for the status assessment and fault warning of the energy storage module, and effectively ensures the safe and reliable operation of the energy storage system.

[0054] In the key step of controlling the proximity temperature measurement module to approach the first target and measure the temperature of the first target to obtain the second temperature value, in order to more accurately and comprehensively grasp the temperature condition of the first target, a proximity temperature measurement module including a drone and a temperature measurement sensor network arranged on it is used, including the following sub-steps: The proximity temperature measurement module uses the drone as a carrier and carefully arranges a temperature measurement sensor network on it. This temperature measurement sensor network is composed of multiple grid nodes, and each grid node is equipped with a high-precision temperature sensor. These temperature sensors are evenly distributed at various positions of the sensor network, forming a dense and comprehensive temperature monitoring network. Through such a layout, temperature data of the first target can be collected from different angles and positions.

[0055] When the drone approaches the first target according to a predetermined route, each temperature sensor distributed on the temperature measurement sensor network starts to work simultaneously, and the temperature data of the first target is obtained in real time. These sensors have the characteristics of high sensitivity and fast response, and can accurately capture the subtle changes in the target temperature in a short time.

[0056] Summarize the temperature data collected by all temperature sensors. Then, calculate the average value of these data through an algorithm of removing the average value, and use this average value as the second temperature value. Comprehensively consider the data obtained by each sensor to reduce the influence caused by the error of a single sensor or local temperature fluctuation.

[0057] By setting up a temperature measurement sensor network composed of multiple grid node temperature sensors on the drone, multi-dimensional data collection can be achieved. Sensors at different positions can sense the temperature of the first target from multiple angles, and utilize the redundancy and complementarity characteristics between data to effectively reduce errors. For example, when detecting a large energy storage transformer, the temperatures of different parts of the transformer may vary. Some parts may have a lower temperature due to better heat dissipation, while some parts may have a higher temperature due to a larger load. If only a single sensor is used for detection, it is very likely that only local temperature information can be obtained, resulting in inaccurate detection results. By using a temperature measurement sensor network, each sensor can collect the temperature data of different parts simultaneously. Through comprehensive analysis and processing of these data, the calculated average value can more truly reflect the overall temperature condition of the transformer, thereby improving the accuracy of temperature detection.

[0058] In the step of obtaining the temperature data of all temperature sensors, to further improve the accuracy and adaptability of temperature detection, a dynamic weighting mechanism based on a sensor adjustment matrix is established, which specifically includes the following sub-steps: The system pre-constructs a corresponding sensor adjustment matrix according to the layout characteristics of the temperature measurement sensor network. Each element in the matrix corresponds to a weighting coefficient of a temperature sensor, and the initial values of these coefficients are set according to the detection accuracy of the sensor, the installation position, and the importance of the target area. For example, sensors located in the central area of the temperature measurement sensor network and directly covering the key components of the energy storage module are given higher initial weighting coefficients; while sensors at the edge position that are vulnerable to environmental interference are assigned lower coefficients, so as to initially distinguish the importance of the data of different sensors.

[0059] After obtaining the original collected temperature values of each temperature sensor, the system performs differential calculations based on the weighting coefficients in the sensor adjustment matrix. Multiply the collected temperature value of each sensor by its corresponding weighting coefficient, and then sum all the product results to finally obtain the weighted temperature data. This calculation method breaks the limitations of traditional average calculations, making the data of sensors at key positions account for a larger proportion in the final result, thus more accurately reflecting the temperature characteristics of the target core area.

[0060] The system dynamically optimizes the weighting coefficients in the sensor adjustment matrix according to the second temperature value obtained from the final calculation. The specific strategy is as follows: when the second temperature value is higher than the preset threshold, it indicates that there may be an overheating risk in the target central area. At this time, the system increases the weighting coefficients in the middle position of the sensor adjustment matrix in a positive correlation manner, and at the same time reduces the coefficients at the edge positions according to the anti-correlation rule, further highlighting the weight of the temperature data in the central area; when the second temperature value is lower than the threshold, the coefficients are adjusted in the reverse direction to reduce the influence of edge interference data on the overall result and ensure the reliability and accuracy of the temperature data.

[0061] Taking the detection of an energy storage station as an example, the energy storage equipment is located in areas such as the edge and is greatly affected by environmental factors. In the initial stage of detection, in the sensor adjustment matrix constructed by the system, the initial weighting coefficient of the central area sensor covering the energy storage equipment is set to 0.8, and the coefficient of the edge area sensor is set to 0.2. When the unmanned aerial vehicle carrying the temperature measurement sensor network approaches the transformer to collect data, the system obtains preliminary temperature data based on matrix weighting calculation.

[0062] If it is detected that the second temperature value reaches 85°C (higher than the preset threshold of 75°C), the system determines that there may be an overheating hidden danger in the central area of the transformer, and immediately increases the weighting coefficient of the central area sensor to 0.9 and reduces the coefficient of the edge area to 0.1. After re-weighting calculation, the local hot spot temperature of the winding is more accurately captured to reach 92°C, and the deviation value is reduced by 15% compared with the initial calculation result, effectively avoiding the missed detection of abnormal temperatures caused by edge data interference.

[0063] Conversely, if the second temperature value is only 40°C, the system automatically reduces the weighting coefficient of the edge area sensor from 0.2 to 0.05, reduces the influence of the external environment (such as natural wind and heat dissipation of surrounding equipment) on the edge sensor data, and at the same time maintains the stability of the central area coefficient. The finally obtained temperature data is more in line with the actual operating temperature of the transformer, and the data standard deviation is reduced by 22%, significantly improving the credibility of the detection results and providing a more reliable basis for maintenance personnel to formulate maintenance strategies.

[0064] Among them, the initial value of the weighting coefficient in this embodiment is set as an average coefficient. For example, when there are two weighting coefficients, the weighting coefficients are 0.5 respectively, and so on.

[0065] This dynamic weighting mechanism based on the sensor adjustment matrix effectively balances the contradiction between detection accuracy and environmental interference through differential processing and adaptive adjustment of sensor data at different positions. It can not only accurately capture the abnormal temperature changes in the key areas of the energy storage module but also maintain the stability of the detection results under complex working conditions, greatly improving the adaptability and reliability of the temperature detection system of the energy storage module for various operating scenarios.

[0066] During the operation of the energy storage module, in order to diagnose potential faults in a timely and accurate manner, this method establishes a fault diagnosis mechanism based on dual detection of temperature and magnetic field signals. This mechanism breaks through the limitations of traditional single detection methods and significantly improves the reliability and forward-looking of fault diagnosis through cross-verification of multi-dimensional data. The specific steps are as follows: After the system calculates the third temperature value of the first target based on the infrared image data, it will compare it with the preset reference temperature value. If the third temperature value is higher than the reference temperature value, it indicates that the first target may have a local overheating situation. However, an abnormal temperature does not necessarily mean a device failure. Non-fault factors such as sudden changes in environmental temperature and short-term high-load operation may also cause the temperature to rise. Therefore, at this time, the system will not immediately determine a device failure but will further adopt more precise detection means.

[0067] The system will accurately identify the local overheating part corresponding to the third temperature value from the infrared image data. This identification process relies on advanced image analysis algorithms, which can accurately match the temperature data with the image pixels to determine the specific location of the overheating area. Subsequently, a high-precision magnetic field detection device is used to collect the first magnetic field signal of this local overheating part. The magnetic field signal contains rich electrical information, which is essentially the external manifestation of the electrical states such as the internal current distribution and electromagnetic induction in the energy storage module. When faults such as poor contact and winding short circuit occur inside the energy storage module, the normal path and distribution of the current will be disrupted, resulting in obvious changes in the magnetic field signal.

[0068] The system compares the collected first magnetic field signal with the preset reference signal value. If the first magnetic field signal is greater than the reference signal value, it means that the electromagnetic state of the local overheating part is abnormal and there is likely a potential electrical fault. At this time, the system immediately gives a device fault prompt. The fault prompt methods include but are not limited to audible and visual alarms, sending text message notifications to operation and maintenance personnel, and marking the fault location on the energy storage monitoring platform, etc., to ensure that operation and maintenance personnel can obtain fault information in a timely manner.

[0069] Taking the detection of a certain energy storage station as an example, in a routine detection, the system found through infrared image analysis that the third temperature value of the first target reached 85°C, exceeding the preset reference temperature value of 80°C. The system immediately locked this winding area as the locally overheated part and collected magnetic field signals from it using a highly sensitive magnetic field sensor installed on a drone. After detection, the first magnetic field signal intensity at this part was 50 μT, significantly higher than the preset reference signal value of 30 μT. Based on the dual anomalies of temperature and magnetic field signals, the system quickly determined that the energy storage device might have a fault and immediately issued a fault prompt. After receiving the notice, the operation and maintenance personnel conducted a detailed inspection of the energy storage device and finally confirmed that the energy storage device did have the above-mentioned fault. Due to the timely discovery, the further deterioration of the fault was avoided, and the serious damage of the transformer and the occurrence of a large-scale power outage accident were prevented.

[0070] Through the dual detection and mutual verification of temperature and magnetic field signals, this method judges the faults of energy storage modules from two dimensions of thermal effect and electromagnetic effect, effectively eliminating the interference of single factors. This multi-dimensional detection method not only significantly improves the accuracy of fault diagnosis, avoids misjudgment and missed judgment, but also can capture abnormal information in a timely manner through the change of magnetic field signals at the initial stage of fault development, winning valuable time for the maintenance of energy storage devices and greatly enhancing the safety and stability of the operation of energy storage systems.

[0071] In order to more accurately detect the faults and abnormal conditions of energy storage modules, on the basis of the existing detection process, the detection mechanism is further improved, and the specific steps are as follows: After identifying the locally overheated part, the system continuously obtains the temperature data of this part. This process relies on a high-precision temperature sensor network to ensure that the temperature changes of the locally overheated part can be captured in real time and accurately. By comparing with the preset reference data, it is judged whether the temperature of this part is in an abnormal state.

[0072] When the temperature data is greater than the preset reference data, it means that the locally overheated situation is relatively serious and further in-depth detection is required. At this time, when the temperature measurement sensor network covers the locally overheated part, the system will collect the second magnetic field signal of this part. The purpose is to obtain the magnetic field signal characteristics under specific conditions (i.e., when the temperature measurement sensor network covers), so as to compare with the first magnetic field signal collected before.

[0073] The system compares the first magnetic field signal and the second magnetic field signal and calculates the signal difference between them. This difference can reflect the change of magnetic field signals under different detection conditions (before and after the temperature measurement sensor network covers). By analyzing the signal difference, it can be judged whether the abnormality of the magnetic field signal is caused by the fault of the device itself or is interfered by external environmental factors.

[0074] Compare the calculated signal difference with a preset signal reference value. If the signal difference is less than the preset signal reference value, it indicates that the change in the magnetic field signals collected twice is not significant. The abnormality of the currently detected magnetic field signal may not be caused by a malfunction of the device itself, but rather by external occlusion or environmental interference. At this time, the system will promptly issue an occlusion warning to alert the operation and maintenance personnel of the possible external interference factors.

[0075] In the complex operating environment of energy storage devices, magnetic field signals are easily interfered by factors such as other surrounding devices and metal structures. If only the magnetic field signal at a single moment is detected, it is very likely to obtain inaccurate detection results due to environmental interference, resulting in misjudgment of device failures. By comparing the magnetic field signal differences before and after the temperature measurement sensor network coverage, this method can effectively eliminate the interference of environmental factors on the magnetic field signal detection. When the signal difference is less than the preset reference value and an occlusion warning is issued, it can clearly indicate that the abnormality of the currently detected magnetic field signal is not a problem of the device itself, avoiding false detection results caused by environmental interference and ensuring the authenticity and reliability of the detection results.

[0076] Take the detection of an energy storage station as an example. During a certain detection, the system found that a local area of a certain energy storage device showed overheating, and the temperature data showed 85°C, exceeding the preset reference data of 70°C. The system immediately collected the first magnetic field signal of this locally overheated part, with an intensity of 40 μT. Then, when the temperature measurement sensor network covered this locally overheated part, the second magnetic field signal was collected, with an intensity of 42 μT. The calculated signal difference between the two was 2 μT, which was less than the preset signal reference value of 5 μT. At this time, the system judged that the abnormality of the currently detected magnetic field signal might be caused by the interference of metal structures or other devices around the energy storage device, rather than a malfunction of the energy storage device itself, and thus promptly issued an occlusion warning. According to the warning information, the operation and maintenance personnel checked and adjusted the environment around the energy storage device. After re-detection, accurate magnetic field signals were obtained, avoiding a possible false detection and unnecessary equipment maintenance.

[0077] From the judgment of the temperature data of the locally overheated part, to the collection and difference calculation of the magnetic field signals, and then to the issuance of an occlusion warning based on the difference result, this method forms a complete closed-loop detection process. Each link is closely connected and mutually verified, effectively making up for the possible loopholes in a single detection step, greatly improving the accuracy and reliability of the energy storage module detection, and providing a strong guarantee for the stable operation of the energy storage system.

[0078] The embodiment of this application also discloses a temperature detection control system for a power energy storage module, including a processor, and the processor executes the steps of the temperature detection control method for the power energy storage module as described in any one of the above.

[0079] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A temperature detection and control method for a power energy storage module, characterized in that, It includes the following steps: Based on the first acquisition point, obtain the infrared image data corresponding to the energy storage module in real time; Identify the first target and the second target from the infrared image data; If the first target is identified and the second target is not identified, calculate the matching value between the infrared image data and the first target; If the matching value is less than the preset first reference value, control the first acquisition point to move a set distance in the first direction until the matching value is greater than the preset second reference value; Update the infrared image data and continue to identify the first target and the second target; If the first target is identified and the second target is not identified, calculate the first temperature value of the first target according to the infrared image data; Control the proximity temperature measurement module to approach the first target and measure the temperature of the first target to obtain the second temperature value; Calculate the comprehensive temperature value according to the first temperature value and the second temperature value, and output the comprehensive temperature value as the detected temperature value; Otherwise, if the first target and the second target are identified, calculate the distance between the first target and the second target as the target distance; if the target distance is less than the preset first reference distance, control the first acquisition point to move a set distance in the second direction until the target distance is greater than the preset second reference distance; wherein, the first direction and the second direction have a set included angle; update the infrared image data, calculate the third temperature value of the first target according to the infrared image data, and output the third temperature value as the detected temperature value; If the output detected temperature value is greater than the preset alarm temperature value, give an alarm prompt.

2. The temperature detection and control method of the power energy storage module according to claim 1, characterized in that, In the step of calculating the third temperature value of the first target according to the infrared image data, the following sub-steps are further included: Calculate the fourth temperature value of the second target according to the infrared image data; Calculate the temperature difference between the third temperature value and the fourth temperature value; If the temperature difference is positive, adjust the value of the set distance in positive correlation with the temperature difference. The larger the temperature difference, the larger the set distance, and the smaller the temperature difference, the smaller the set distance; If the temperature difference is negative, adjust the value of the set distance in negative correlation with the temperature difference. The larger the temperature difference, the smaller the set distance, and the smaller the temperature difference, the larger the set distance.

3. The temperature detection and control method of the power energy storage module according to claim 1 or 2, characterized in that, In the step of updating the infrared image data, the following sub-steps are further included: Calculate the first infrared imaging area value of the first target according to the infrared image data; Calculate the second infrared imaging area value of the second target according to the infrared image data; Calculate the area difference between the first infrared imaging area value and the second infrared imaging area value; If the area difference is positive, adjust the set included angle in negative correlation with the area difference. The larger the area difference, the smaller the set included angle, and the smaller the area difference, the larger the set included angle; If the area difference is negative, the set angle is adjusted positively correlated with the area difference. The larger the area difference, the larger the set angle; the smaller the area difference, the smaller the set angle.

4. The temperature detection and control method of the power energy storage module according to claim 1, characterized in that, In the step of controlling the proximity temperature measurement module to approach the first target and measuring the temperature of the first target to obtain a second temperature value, the following sub-steps are further included: The proximity temperature measurement module includes a drone and a temperature sensor disposed on the drone. When the temperature sensor approaches the energy storage module, it has a temperature measurement projection in the infrared image; Update the infrared image data; Identify the temperature measurement projection from the infrared image data; If the temperature measurement projection is not identified, perform an occlusion warning; If the temperature measurement projection is identified, the set distance or the set angle is adjusted inversely correlated with the size of the temperature measurement projection; the larger the temperature measurement projection, the smaller the set distance or the set angle; the smaller the temperature measurement projection, the larger the set distance or the set angle.

5. The temperature detection and control method of the power energy storage module according to claim 1, characterized in that In the step of controlling the proximity temperature measurement module to approach the first target and measuring the temperature of the first target to obtain a second temperature value, the following sub-steps are further included: The proximity temperature measurement module includes a drone and a temperature measurement sensor network disposed on the drone. Each grid node of the temperature measurement sensor network is provided with a temperature sensor. Obtain the temperature data of all the temperature sensors; Calculate the average value of all the temperature data as the second temperature value.

6. The temperature detection and control method of the power energy storage module according to claim 5, characterized in that, In the step of obtaining the temperature data of all the temperature sensors, the following sub-steps are further included: Set a sensor adjustment matrix corresponding to the temperature measurement sensor network. The elements in the sensor adjustment matrix are the weighting coefficients corresponding to each temperature sensor; Calculate the temperature data according to the sensor adjustment matrix and the collected temperature values of the temperature sensors; Adjust the weighting coefficient at the middle position of the sensor adjustment matrix positively correlated with the second temperature value, and adjust the weighting coefficient at the edge position of the sensor adjustment matrix inversely correlated with the second temperature value.

7. The temperature detection and control method for the power energy storage module according to claim 5, characterized in that, The method further includes the following steps: If the third temperature value is greater than a preset reference temperature value, identify the locally overheated part corresponding to the third temperature value from the infrared image data; Collect the first magnetic field signal of the locally overheated part; If the first magnetic field signal is greater than a preset reference signal value, perform a device failure prompt.

8. The temperature detection and control method of the power energy storage module according to claim 7, characterized in that, The method further includes the following steps: Obtain the temperature data corresponding to the locally overheated part; If the temperature data is greater than a preset reference data, when the temperature measurement sensor network covers the locally overheated part, collect the second magnetic field signal of the locally overheated part; Calculate the signal difference between the first magnetic field signal and the second magnetic field signal; If the signal difference is less than a preset signal reference value, perform an occlusion warning.

9. A temperature detection and control system for a power energy storage module, characterized in that, It includes a processor, and the processor executes the steps of the temperature detection and control method of the power energy storage module according to any one of claims 1-8.

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