A temperature detection and control method and system for an electric energy storage module

Abstract

The present application relates to the technical field of energy storage equipment detection, and discloses a temperature detection and control method and system for an electric energy storage module, the method comprising: acquiring infrared image data of the energy storage module from a first acquisition point, and identifying the first and second targets therein. If only the first target is identified and the matching value is low, the acquisition point is controlled to move in the first direction until the matching value reaches the standard, and the image is updated to continue identification; at this time, if there is still only the first target, its temperature value is calculated and combined with the temperature value obtained by the proximity temperature measurement module to calculate a comprehensive temperature value as the detection temperature value. If two targets are identified and the distance is too close, the acquisition point is controlled to move in the second direction, and the temperature value of the first target is calculated after updating the image as the detection temperature value. Finally, an alarm is triggered when the detected temperature value exceeds the preset alarm value. By flexibly adjusting 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, and abnormalities are warned in time, thereby improving the accuracy of detection.

Description

Technical Field

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

[0002] In solar energy storage systems, energy storage modules, as core components, store excess energy during periods of sufficient sunlight to ensure power during periods of low electricity; smooth power output to improve energy storage quality; shift peaks and fill valleys to rationally distribute energy; serve as an emergency backup power source to ensure the operation of critical loads; and participate in grid frequency and voltage regulation to maintain grid stability. However, over long-term operation, energy storage modules can develop faults such as poor contact, insulation damage, and internal short circuits due to load fluctuations, environmental factors, and component aging. If these faults are not detected and addressed promptly, they can lead to localized power supply anomalies at best; or, at worst, serious safety hazards such as equipment overheating, arcing, and even explosions.

[0003] Temperature is a key characteristic parameter that reflects the operating status of energy storage modules. Internal anomalies, such as increased contact resistance, increased winding losses, or deterioration of the insulation, often lead to increased energy loss and, in turn, localized temperature increases. Monitoring the temperature of key parts of energy storage modules provides a direct and effective way to assess their operating status. Temperature signals are easy to acquire and process. Sensors can convert temperature changes into electrical signals, enabling data analysis to assess the module's operating status.

[0004] Currently, infrared thermal imaging technology is widely used in energy storage module temperature detection due to its ability to obtain non-contact images of the device's surface temperature distribution. This technology can quickly scan the overall temperature of the energy storage module, facilitating the identification of areas with obvious temperature anomalies. However, in practice, infrared thermal imaging detection is susceptible to interference from thermal radiation from other devices in the background environment. For example, in a complex energy storage device layout, the heat generated by adjacent devices may cause confusion in temperature information in the infrared image, affecting the accurate determination of the target energy storage module's temperature. Furthermore, when an energy storage module is obscured by other devices or structural components, infrared thermal imaging cannot effectively obtain temperature data on its entire surface, potentially missing temperature anomalies in key areas, leading to inaccurate detection results or even misjudgments. Summary of the Invention

[0005] In order to improve the accuracy of detection results, the present application provides a temperature detection and control method and system for an electric energy storage module.

[0006] In a first aspect, the present application provides a temperature detection and control method for an electric energy storage module, which adopts the following technical solution:

[0007] A temperature detection and control method for an electric energy storage module comprises the following steps:

[0008] Acquire initial infrared image data corresponding to the energy storage module in real time based on the first acquisition point;

[0009] identifying a first target and a second target from the initial infrared image data;

[0010] If the first target is identified and the second target is not identified, a matching value between the initial infrared image data and the first target is calculated; if the matching value is less than a preset first reference value, the first acquisition point is controlled to move a set distance in a first direction until the matching value is greater than a preset second reference value; the initial infrared image data is updated to the updated infrared image data, and the first target and the second target are continuously identified; wherein the first reference value is less than the second reference value; the matching value comprehensively considers the clarity, completeness, and feature consistency of the target in the image, assigns different weights to the calculation results of the three, and obtains the matching value through weighted summation;

[0011] If the updated infrared image data still identifies the first target but does not identify the second target, then calculating the first temperature value of the first target based on the infrared image data; controlling the proximity temperature measurement module to approach the first target, measuring the temperature of the first target to obtain a second temperature value; calculating a comprehensive temperature value based on the first temperature value and the second temperature value, and outputting the comprehensive temperature value as the detected temperature value of the first target; the proximity temperature measurement module is a device capable of performing non-contact close-range temperature measurement close to the first target, including a drone, a humanoid robot, an unmanned vehicle, or a robotic arm equipped with a temperature measurement sensor;

[0012] Otherwise, if the first target and the second target are identified, the distance between the first target and the second target is calculated as the target distance; if the target distance is less than a preset first reference distance, the first acquisition point is controlled to move a set distance in a second direction until the target distance is greater than a preset second reference distance; wherein the first direction and the second direction have a set angle; the initial infrared image data is updated, and a third temperature value of the first target is calculated based on the updated infrared image data, and the third temperature value is output as the first target detection temperature value; wherein, when the target distance is less than the preset first reference distance, it indicates that the first target and the second target have overlap or adjacent interference in the image;

[0013] If the output first target detection temperature value is greater than the preset alarm temperature value, an alarm prompt will be issued.

[0014] By adopting the above technical solution, the first and second targets in a single infrared image are identified, the acquisition point positions are dynamically adjusted, and targeted strategies are adopted for different detection situations. If only the first target is identified, the first acquisition point is controlled to move in the first direction (horizontally) for shooting. If the matching value does not meet the expectation, it indicates possible interference. Then, a proximity temperature measurement module is used for close measurement. The first temperature value calculated from the infrared image data is combined with the second temperature value obtained by proximity temperature measurement to obtain a composite temperature value as the detection temperature value. This effectively avoids temperature misjudgments caused by thermal radiation from other equipment and makes the detection results more accurate. If two targets are identified, vertical movement is determined based on distance. If the distance between the two targets increases after controlling the first acquisition point to move in the second direction (vertical) for shooting, it indicates non-overlapping interference, and the first temperature value is directly output. This ensures that even in the presence of occlusion, accurate temperature data of key parts of the energy storage module can be obtained, reducing detection omissions and misjudgments caused by occlusion. Therefore, this method can effectively eliminate thermal radiation interference, solve occlusion problems, and achieve multi-dimensional detection and verification. At the same time, it can quickly respond to abnormal temperatures and issue alarms through automated processes, significantly improving the accuracy and reliability of detection, the timeliness of fault warnings, and detection efficiency, thereby ensuring the stable operation of the energy storage system.

[0015] Optionally, the step of calculating the third temperature value of the first target based on the updated infrared image data further includes the following sub-steps:

[0016] Calculating a first infrared imaging area value of the first target according to the updated infrared image data;

[0017] Calculating a second infrared imaging area value of the second target according to the updated infrared image data;

[0018] Calculating an area difference between the first infrared imaging area value and the second infrared imaging area value;

[0019] If the area difference is positive, the set angle is adjusted inversely according to the area difference. The larger the area difference is, the smaller the set angle is, and the smaller the area difference is, the larger the set angle is.

[0020] If the area difference is negative, the set angle is adjusted according to the positive correlation of the area difference. The larger the area difference is, the larger the set angle is, and the smaller the area difference is, the smaller the set angle is.

[0021] The calculation of the area difference and the adjustment of the angle in this step are only applicable to the scenario where the second target is recognized.

[0022] By adopting the above technical solution, the set angle is adjusted according to the area difference between the first and second targets, which can make the movement direction of the collection point more consistent 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 allows the collection point to more closely capture the details of the first target during subsequent movement, avoiding the dispersion of the shooting angle due to an excessively large angle, ensuring that the boundary and temperature distribution of the two targets can be clearly distinguished, and preventing the impact of image overlap or blur on temperature detection accuracy. When the area difference is negative, increasing the set angle helps to fully cover smaller targets and avoid missing key information due to an excessively small angle, thereby improving the accuracy of temperature detection of both targets and providing more reliable data support for energy storage module fault diagnosis.

[0023] Optionally, 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 further includes the following sub-steps:

[0024] The proximity temperature measurement module includes a drone and a temperature sensor provided on the drone, wherein the temperature sensor has a temperature measurement projection in the infrared image when it approaches the energy storage module;

[0025] updating the infrared image data;

[0026] identifying the temperature measurement projection from the updated infrared image data;

[0027] If the temperature measurement projection is not recognized, an obstruction warning is issued;

[0028] If the temperature measurement projection is identified, the distance or angle between the proximity temperature measurement module and the first target is adjusted inversely according to the size of the temperature measurement projection; the larger the temperature measurement projection, the smaller the distance or angle; the smaller the temperature measurement projection, the larger the distance or angle;

[0029] In this step, the set distance is the moving distance in the first direction, and the set angle is the angle between the first direction and the second direction.

[0030] By adopting the above technical solution, the presence of obstruction is determined by identifying the temperature measurement projection in the infrared image. If the temperature measurement projection is not identified, an obstruction warning is immediately issued, which can promptly remind the operation and maintenance personnel that key parts of the energy storage module may be obstructed, resulting in the temperature sensor being unable to obtain data normally. It effectively makes up for the shortcomings of relying solely on infrared images and target recognition for detection. Even in a complex energy storage equipment environment, when there are cables, other structural components, etc. that obstruct the temperature measurement sensor, it can be quickly sensed and feedback can be given, making the detection system's judgment on the status of the energy storage module more reliable, avoiding equipment failures or safety accidents caused by potential detection loopholes. The set distance or set angle is adjusted inversely according to the size of the temperature measurement projection, so that the detection system can dynamically optimize the detection parameters according to the actual detection situation. When the temperature projection is large, it indicates that the temperature sensor is close to the energy storage module. In this case, reducing the set distance or setting the angle can avoid measurement errors or potential impacts on the equipment caused by too close distance or improper angle. When the temperature projection is small, increasing the set distance or setting the angle ensures that the temperature sensor is in the appropriate detection position, obtains accurate temperature data at the optimal viewing angle and distance, and improves the accuracy and stability of detection.

[0031] Optionally, 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 further includes the following sub-steps:

[0032] The proximity temperature measurement module includes a drone and a temperature measurement sensor network arranged on the drone, wherein each grid node of the temperature measurement sensor network is provided with a temperature sensor.

[0033] Acquiring temperature data from all temperature sensors;

[0034] An average value of all the temperature data is calculated as the second temperature value.

[0035] By adopting the above technical solution, a temperature measurement sensor network consisting of multiple grid node temperature sensors is set up on the UAV. The first target temperature data can be collected in multiple dimensions, and the error can be reduced by using data redundancy and complementarity. The average value of all temperature data is used as the second temperature value. This not only improves the accuracy of temperature detection and truly reflects the target temperature condition, but also enhances the reliability of the detection results. At the same time, it realizes rapid and comprehensive detection, adapts to complex working conditions, and provides rich data for intelligent operation and maintenance, assists in accurate fault location and operation and maintenance decision-making, and effectively improves the efficiency and quality of energy storage module detection.

[0036] Optionally, the step of acquiring the temperature data of all the temperature sensors further includes the following sub-steps:

[0037] Setting a sensor adjustment matrix corresponding to the temperature measurement sensor network, wherein the elements in the sensor adjustment matrix are weighted coefficients corresponding to each of the temperature sensors;

[0038] The temperature data is calculated based on the sensor adjustment matrix and the collected temperature value of the temperature sensor;

[0039] The weighting coefficients at the middle position of the sensor adjustment matrix are adjusted in a positive correlation according to the second temperature value, and the weighting coefficients at the edge positions of the sensor adjustment matrix are adjusted in an anti-correlation manner.

[0040] By adopting the above technical solution and setting the sensor adjustment matrix and weighting coefficients, it is possible to perform differentiated weighted calculations on the collected temperature values ​​based on the characteristics of different temperature sensors, their installation locations, and their importance to the target temperature detection. When the second temperature value is high, it indicates that there may be a temperature anomaly in the target's central area. Increasing the weighting coefficient for the middle position can further emphasize the weight of the temperature data in the central area and more accurately capture abnormal temperature changes. When the second temperature value is low, reducing the weighting coefficient for the edge positions can reduce the impact of edge interference data on the overall results, ensuring the reliability and accuracy of the temperature data and effectively improving the detection system's ability to capture temperature anomalies.

[0041] Optionally, the method further comprises the following steps:

[0042] If the third temperature value is greater than a preset reference temperature value, identifying a local overheating location corresponding to the third temperature value from the updated infrared image data;

[0043] collecting a first magnetic field signal of the local overheating portion;

[0044] If the first magnetic field signal is greater than a preset reference signal value, an equipment failure prompt is issued.

[0045] By adopting this technical solution, the energy storage module status is no longer solely determined by temperature data. Instead, when the first target temperature exceeds a preset reference temperature, indicating localized overheating, magnetic field signals are collected from the corresponding locations. Temperature anomalies are often a direct indicator of energy storage module failure, but single temperature detection can lead to misjudgments. For example, a sudden rise in ambient temperature, or other non-fault-related factors, can also cause a temperature rise. Magnetic field signals, on the other hand, can reflect the module's internal electrical conditions, including current distribution and electromagnetic induction. When internal faults such as poor contact or winding shorts occur, the magnetic field signals will significantly change. By dually detecting and cross-validating temperature and magnetic field signals, faults can be diagnosed from both thermal and electromagnetic perspectives, effectively eliminating interference from a single factor and significantly improving fault diagnosis accuracy, avoiding misjudgments and missed detections. Changes in magnetic field signals often precede significant damage caused by equipment failure. Promptly detecting magnetic field signals after a temperature rise can detect anomalies early in the development of a fault.

[0046] Optionally, the method further comprises the following steps:

[0047] Acquiring the temperature data corresponding to the local overheating portion;

[0048] If the temperature data is greater than a preset reference data, a temperature measurement sensor network is set up. When the temperature measurement sensor network covers the local overheating area, a second magnetic field signal of the local overheating area is collected; the signal difference between the first magnetic field signal and the second magnetic field signal is calculated; if the signal difference is less than a preset signal reference value, an occlusion warning is issued.

[0049] By adopting the above technical solution, in the complex operating environment of energy storage equipment, magnetic field signals are easily interfered with by surrounding equipment, metal structures, and other factors. Simply detecting the magnetic field signal at a single moment may not accurately reflect the device's true status. This technical solution effectively eliminates environmental interference with magnetic field signal detection by comparing the difference in magnetic field signals before and after the temperature sensor network is covered. When the signal difference is less than the preset reference value and an obstruction warning is issued, it indicates that the currently detected magnetic field signal anomaly is likely caused by external obstruction or environmental interference, rather than a device malfunction. This avoids erroneous detection results due to environmental interference, ensures the authenticity and reliability of the detection results, and provides solid data support for energy storage module status assessment. A complete closed-loop detection process is formed, from determining temperature data from local overheating areas, to collecting magnetic field signals and calculating the difference, and then issuing an obstruction warning based on the difference results. Each link is closely connected and mutually verified, effectively compensating for potential loopholes in a single detection step.

[0050] In a second aspect, the present application provides a temperature detection and control system for an electric energy storage module, which adopts the following technical solution:

[0051] A temperature detection and control system for an electric energy storage module comprises a processor, wherein the processor executes the steps of any one of the above-mentioned temperature detection and control methods for an electric energy storage module.

[0052] In summary, this application includes at least one of the following beneficial technical effects:

[0053] By identifying targets in infrared images, dynamically adjusting the location of collection points and detection strategies, and combining infrared image temperature calculation, proximity temperature measurement fusion, and multi-node data collection and weighted processing of the temperature sensor network, the temperature data of the energy storage module can be accurately acquired, effectively avoiding thermal radiation interference and occlusion effects, and improving detection accuracy. Based on dual detection and difference analysis of temperature and magnetic field signals, the fault location and type can be accurately identified, reducing the risk of misjudgment or missed judgment.

[0054] Based on the temperature difference between targets, the imaging area difference and the temperature measurement projection size, the detection parameters such as the set distance and set angle of the collection point are intelligently adjusted, so that the detection system can adapt to complex working conditions and different detection requirements, optimize the detection process and improve detection efficiency.

[0055] Build a complete closed-loop detection process to provide timely warnings for temperature anomalies, magnetic field signal anomalies, and potential obstruction risks, discover hidden equipment failures and detection interference factors in advance, enhance detection reliability, provide operation and maintenance personnel with sufficient response time, and ensure the stable operation of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The present invention is a step diagram of a temperature detection and control method for an electric energy storage module.

[0057] Figure 2 This is a comparison chart of the infrared images after the first acquisition point is moved horizontally.

[0058] Figure 3 This is the infrared image comparison chart after the first acquisition point is moved vertically.

[0059] Figure 4 This is a step diagram for adjusting the set angle according to the area difference in the step of updating infrared image data.

[0060] Figure 5 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

[0061] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0062] Throughout this specification, 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 representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0063] 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:

[0064] Based on the first acquisition point, infrared image data corresponding to the energy storage module is acquired in real time. High-precision infrared thermal imaging equipment is used to acquire initial infrared image data corresponding to the energy storage module in real time. The image data intuitively presents the temperature distribution on the surface of the energy storage module in the form of thermal radiation.

[0065] The image recognition algorithm identifies a first target and a second target from the initial infrared image data. The first target and the second target correspond to, for example, a key energy storage device and an adjacent energy storage device of the energy storage module, respectively. Thermal radiation generated by the adjacent energy storage device may interfere with the detection of the first target.

[0066] When the first target is detected but the second target is not, the system calculates a match value between the infrared image data and the first target. This match value comprehensively considers factors such as the target's clarity, completeness, and feature consistency in the image. Clarity is measured using gradient magnitude statistics and Laplace variance to measure image edges and grayscale variations; completeness is assessed using contour overlap and area ratio to assess the integrity of the target's outline and region; and feature consistency is achieved using a keypoint matching algorithm and deep learning-derived feature vector comparison. Finally, the three calculated results are assigned different weights and a weighted sum is used to determine the final match value, which quantifies the degree of fit between the initial infrared image data and the first target. If the match value falls below a preset first reference value, it indicates that the image at the current viewing angle is subject to interference or occlusion, resulting in insufficient information about the first target. At this point, the first acquisition point is controlled to move in the first (horizontal) direction by a set distance, which is optimized based on the equipment layout and detection accuracy requirements. During this movement, the system continuously updates the infrared image data and repeats the target recognition operation until the match value exceeds a preset second reference value. If only the first target is ultimately identified, the system calculates the first target's first temperature based on the updated infrared image data. Simultaneously, a proximity temperature measurement module equipped with a specialized temperature sensor—such as a flexible drone, an adaptable humanoid robot, an unmanned vehicle suitable for ground operations, or a robotic arm capable of high-precision operation—is dispatched to approach the first target for close-range, high-precision temperature measurement, obtaining a second temperature value. A scientific algorithm then calculates a weighted average of the first and second temperature values ​​to produce a composite value, which serves as the detected temperature for the first target. This process effectively eliminates interference from thermal radiation from adjacent equipment, ensuring that the detection results truly reflect the temperature of the first target.

[0067] If the first target and the second target are simultaneously identified in the initial 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 interfere with each other in the image, which may affect the accuracy of temperature detection. At this time, the first acquisition point is controlled to move a set distance in the second direction (vertical) at a set angle to the first direction. By adjusting the shooting height and angle, the image viewing angle is optimized until the target distance is greater than the preset second reference distance. After the position adjustment is completed, 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 detection blind spot problem caused by mutual occlusion of devices.

[0068] The system compares the detected temperature output with the pre-set alarm temperature in real time. If the detected temperature exceeds the threshold, a multi-level alarm mechanism is immediately activated. Through various means, including audible and visual alarms, SMS push notifications, and backend system alerts, abnormal information is immediately transmitted to operations and maintenance personnel, allowing them to take timely measures to prevent further failures and ensure the safe and stable operation of the energy storage system.

[0069] By dynamically adjusting collection point locations, integrating multimodal data, and implementing intelligent decision-making strategies, the system effectively overcomes the two major challenges of traditional infrared detection—thermal radiation interference and equipment obstruction. This not only enables multi-dimensional detection and verification of energy storage module temperatures, but also significantly shortens detection cycles through a fully automated process. This significantly improves detection accuracy, reliability, and the timeliness of fault warnings, providing a solid technical foundation for the stable operation of energy storage systems.

[0070] In the process of calculating the third temperature value of the first target based on the updated infrared image data, in order to further improve the accuracy of energy storage module detection, the following sub-steps are also included:

[0071] Based on an infrared thermal imaging data analysis algorithm, the third temperature value of the first target and the fourth temperature value of the second target are calculated from the updated infrared image data. This calculation process is not a simple numerical reading, but rather comprehensively considers multiple factors such as the pixel value of the updated infrared image data, thermal radiation intensity, and ambient temperature compensation to ensure that the obtained temperature values ​​reflect the actual temperature of the target as accurately as possible.

[0072] The temperature difference between the third temperature value and the fourth temperature value is calculated. This difference is the key basis for subsequent adjustment of the set distance, and reflects the degree of difference in thermal state between the first target and the second target.

[0073] Depending on the positive or negative temperature difference, the system will adopt different adjustment strategies to dynamically adjust the set distance value.

[0074] If the temperature difference is positive, meaning the temperature of the first target is higher than that of the second, the first target is relatively hotter. In this case, the larger the temperature difference, the more significant the difference in thermal state between the two targets. In this case, the system adjusts the set distance in a positive correlation with the temperature difference: the larger the temperature difference, the larger the set distance; the smaller the temperature difference, the smaller the set distance. For example, in a large substation energy storage module inspection scenario, the first target is a transformer winding operating at high load, and the second target is an adjacent switchgear operating at relatively low load. Calculations show that the transformer winding's third temperature is 80°C, while the switchgear's fourth temperature is 30°C, resulting in a temperature difference of 50°C. This large difference indicates a significant difference in thermal state between the two targets. To prevent the high heat generated by the transformer winding from interfering with the switchgear temperature measurement during close-range inspection, while ensuring clear infrared images of both targets, the system increases the set distance, for example, from 2 meters to 3 meters. This reduces thermal interference while accurately distinguishing the temperatures of the two targets.

[0075] If the temperature difference is negative, meaning the temperature of the first target is lower than that of the second target, it indicates that the first target is relatively cool. In this case, the system adjusts the set distance inversely based on the temperature difference: the larger the temperature difference, the smaller the set distance; the smaller the temperature difference, the larger the set distance. For example, in a data center energy storage module inspection, the first target is a backup uninterruptible power supply (UPS), and the second target is a server power supply module operating at full capacity. After calculation, the UPS's third temperature value is 25°C, and the server power supply module's fourth temperature value is 60°C, resulting in a temperature difference of -35°C. This large difference indicates a significant difference in thermal conditions. To more accurately capture the temperature information of the cooler UPS and prevent inaccurate temperature data due to excessive distance, the system reduces the set distance, for example, from 3 meters to 1.5 meters. This adjustment ensures that the inspection results more accurately reflect the temperature conditions of each component of the energy storage module, effectively avoiding detection errors.

[0076] By 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 energy storage module detection and providing stronger guarantees for the safe and stable operation of the energy storage system.

[0077] Reference Figure 4 In the step of updating the infrared image data, in order to further optimize the detection viewing angle and accuracy, this method uses a dynamic adjustment mechanism based on the target imaging area difference to perform a refined analysis of the infrared imaging areas of the first and second targets, thereby achieving intelligent adaptation of the movement direction of the acquisition point. The specific steps are as follows:

[0078] Using image recognition and edge detection algorithms, the contours of the first and second targets are accurately delineated from the updated infrared image data. Based on this, the first and second infrared imaging areas of the first and second targets are calculated, respectively, by combining the conversion relationship between infrared image pixel density and actual physical size.

[0079] Perform a difference calculation 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 spatial proportions of the two targets in the infrared image.

[0080] According to the positive and negative characteristics of the area difference, the system implements differentiated adjustment strategies:

[0081] If the area difference is positive, it indicates that the first target's image area in the infrared image is larger than the second target. In this case, the system adjusts the set angle of the acquisition point inversely based on the size of the area difference: the larger the area difference, the smaller the set angle; the smaller the area difference, the larger the set angle. For example, in an energy storage module inspection at an energy storage station, the first target is a large energy storage device (with a larger image area for the heat sink area), and the second target is an adjacent small energy storage device. Calculations show that the image area of ​​the first target is 800 pixels², while that of the second target is 200 pixels², resulting in an area difference of 600 pixels². Due to this significant difference, the system automatically reduces the set angle of the acquisition point from the initial 45° to 20°, focusing the camera's field of view on the transformer's critical heat dissipation area. This effectively avoids loss of detail due to perspective distraction during subsequent mobile capture, accurately capturing temperature changes in the local hotspots of the large energy storage device, and clearly distinguishing the boundary between the two devices to prevent temperature data confusion caused by image overlap.

[0082] 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 a 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 the 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 outline and temperature information of the first target are fully covered, avoiding missing the abnormal heating of the first target due to the narrow viewing angle, while taking into account the temperature detection of the second target, to achieve synchronous and accurate monitoring of targets of different sizes.

[0083] Through this dynamic adjustment mechanism based on the difference in target imaging area, the detection system can perceive changes in target morphology in real time and intelligently optimize the shooting angle and the movement direction of the acquisition point. This adaptive adjustment strategy effectively solves the problems of image information loss and blurred target boundaries caused by the fixed angle of view in traditional detection. It significantly improves the quality of infrared image data and the accuracy of temperature detection, providing more comprehensive and reliable data support for energy storage module fault diagnosis, and effectively ensuring the stable operation of the energy storage system.

[0084] Reference Figure 5 In the process of controlling the proximity temperature measurement module to accurately measure the temperature of the first target, in order 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 temperature measurement projection. The specific implementation process is as follows:

[0085] The proximity temperature measurement module uses a drone equipped with a high-precision temperature sensor as its core carrier. As the drone and temperature sensor approach the energy storage module, the sensor's thermal radiation creates a unique temperature projection in the infrared image. This projection not only visually illustrates the sensor's spatial position, but its shape and size also reveal key information such as the distance and angle between the sensor and the target. The system continuously updates infrared image data and uses image recognition and feature extraction algorithms to precisely identify the outline and position of the temperature projection within the massive image data.

[0086] If the temperature measurement projection is not identified in the updated infrared image data, this means that the field of view of the temperature sensor may be blocked by cables, metal brackets, and other equipment components around the energy storage module, resulting in the sensor being unable to collect target temperature data normally. At this time, the system will immediately trigger the blockage warning mechanism and send early warning information to the operation and maintenance personnel through multiple channels such as sound and light alarms, pop-up prompts on the operation and maintenance platform, and SMS push notifications, with detailed marking of the location of the suspected blocked energy storage module and the sensor's loss of connection status. For example, in an inspection mission at a large substation, when a drone carrying a temperature measurement sensor approached a group of high-voltage switch cabinets, the temperature measurement projection disappeared from the infrared image because the cable tray temporarily installed on the top of the cabinet blocked the sensor's field of view. The system quickly issued an obstruction warning, and the operation and maintenance personnel intervened in time to adjust the drone's flight path, avoiding detection blind spots caused by missing data.

[0087] If the temperature measurement projection is successfully identified, the system will implement a dynamic adjustment strategy based on the size of the projection. When the temperature measurement projection area is large, it indicates that the distance between the temperature measurement sensor and the energy storage module is relatively close. If the current distance or angle is maintained at this time, measurement errors may occur due to interference from thermal radiation, and there may even be a risk of collision with the equipment. The system will automatically reduce the set distance or set angle according to the anti-correlation principle. For example, when detecting energy storage equipment, as the drone gradually approaches, the temperature measurement projection occupies a larger area in the infrared image. The system will adjust the drone's flight distance 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 temperature measurement projection area is small, it means that the sensor is far away from the target or the angle is not good. The system will increase the set distance or set angle to optimize the detection position. For example, when inspecting the energy storage modules of high-rise distribution towers, the drone's initial flight altitude was too high, and the temperature measurement projection only appeared as a tiny light spot in the infrared image. The system then controlled the drone to descend, adjusted the distance from 3 meters to 2 meters, and expanded the shooting angle so that the sensor could fully cover the target area and obtain comprehensive temperature information.

[0088] This intelligent adjustment mechanism, based on temperature projection, achieves adaptive optimization of detection parameters by real-time sensing of the spatial relationship between the sensor and the energy storage module. This not only effectively compensates for the susceptibility of traditional infrared detection to obstruction and interference, but also provides dual guarantees for the precise positioning and safe operation of temperature sensors in the complex and ever-changing energy storage equipment environment. By dynamically adjusting the detection distance and angle, it significantly improves the accuracy and stability of temperature data acquisition, laying a solid data foundation for energy storage module status assessment and fault warning, and effectively ensuring the safe and reliable operation of the energy storage system.

[0089] 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 understand the temperature condition of the first target, the proximity temperature measurement module including the drone and the temperature measurement sensor network installed thereon is used, including the following sub-steps:

[0090] The proximity temperature measurement module uses a drone as a carrier, upon which a carefully arranged temperature sensor network is deployed. This network consists of multiple grid nodes, each equipped with a high-precision temperature sensor. These temperature sensors are evenly distributed throughout the sensor network, forming a dense and comprehensive temperature monitoring network. This layout enables temperature data to be collected from various angles and positions on the primary target.

[0091] As the drone approaches the target along its predetermined route, the temperature sensors distributed across the network begin operating simultaneously, acquiring real-time temperature data. These sensors possess high sensitivity and rapid response, accurately capturing subtle changes in the target's temperature within a short period of time.

[0092] The temperature data collected by all temperature sensors is aggregated. Then, an average value is calculated using a de-averaging algorithm, and this average value is used as the second temperature value. This comprehensive consideration of the data obtained by each sensor reduces the impact of individual sensor errors or local temperature fluctuations.

[0093] By deploying a temperature sensing network consisting of multiple grid-node temperature sensors on a drone, multi-dimensional data collection can be achieved. Sensors in different locations can sense the temperature of the primary target from multiple angles, leveraging the redundant and complementary nature of the data to effectively reduce errors. For example, when testing a large energy storage transformer, temperatures may vary across different parts of the transformer. Some parts may be cooler due to better heat dissipation, while others may be hotter due to heavy loads. Using only a single sensor for testing would likely only capture localized temperature information, resulting in inaccurate results. However, using a temperature sensing network, individual sensors can simultaneously collect temperature data from different locations. Through comprehensive analysis and processing of this data, the calculated average value more accurately reflects the overall temperature condition of the transformer, thereby improving temperature detection accuracy.

[0094] In the step of obtaining temperature data from all temperature sensors, a dynamic weighting mechanism based on the sensor adjustment matrix was established to further improve the accuracy and adaptability of temperature detection. The specific sub-steps include the following:

[0095] The system pre-builds a sensor adjustment matrix tailored to the specific layout of the temperature sensor network. Each element in the matrix corresponds to a weighting coefficient for a temperature sensor. Initial values ​​for these coefficients are determined based on the sensor's accuracy, installation location, and the importance of the target area. For example, sensors located in the center of the temperature sensor network, directly covering key components of the energy storage module, are assigned higher initial weighting coefficients; sensors at the edge, more susceptible to environmental interference, are assigned lower coefficients. This provides a preliminary distinction between the importance of different sensor data.

[0096] After obtaining the raw temperature values ​​from each temperature sensor, the system performs differentiated calculations based on the weighting coefficients in the sensor adjustment matrix. Each sensor's temperature value is multiplied by its corresponding weighting coefficient, and all the products are summed to produce the weighted temperature data. This calculation method overcomes the limitations of traditional averaging, allowing data from key sensors to account for a greater proportion of the final result, thereby more accurately reflecting the temperature characteristics of the target core area.

[0097] Based on the final calculated second temperature value, the system dynamically optimizes the weighting coefficients in the sensor adjustment matrix. The specific strategy is as follows: When the second temperature value is above a preset threshold, indicating a potential overheating risk in the target's central area, the system increases the weighting coefficients in the center of the sensor adjustment matrix in a positively correlated manner, while simultaneously decreasing the coefficients at the edges in an anti-correlated manner, further emphasizing the weighting of the temperature data in the central area. When the second temperature value is below the threshold, the coefficients are adjusted in the opposite direction to reduce the impact of edge interference data on the overall results, ensuring the reliability and accuracy of the temperature data.

[0098] Taking energy storage station inspections as an example, energy storage equipment is located in peripheral areas and is significantly affected by environmental factors. During the initial inspection phase, the system constructs a sensor adjustment matrix with an initial weighting coefficient of 0.8 for sensors in the central area covering the energy storage equipment and 0.2 for sensors in peripheral areas. When a drone carrying a temperature sensor network approaches a transformer to collect data, the system calculates preliminary temperature data based on the matrix weighting.

[0099] If the second temperature value detected reaches 85°C (higher than the preset threshold of 75°C), the system determines that the central area of ​​the transformer may be overheating and immediately increases the weighting coefficient of the central sensor to 0.9 and reduces the coefficient of the peripheral area to 0.1. This reweighted calculation more accurately captures the local hot spot temperature of the winding reaching 92°C, reducing the deviation by 15% compared to the initial calculation result, effectively avoiding the missed detection of abnormal temperatures due to interference from peripheral data.

[0100] Conversely, if the second temperature value is only 40°C, the system automatically reduces the weighting coefficient of the edge sensors from 0.2 to 0.05, reducing the impact of external environmental factors (such as wind and heat dissipation from surrounding equipment) on the edge sensor data while maintaining the coefficient of the central area stable. The resulting temperature data more closely matches the actual operating temperature of the transformer, with a 22% reduction in data standard deviation, significantly improving the credibility of the test results and providing a more reliable basis for maintenance personnel to formulate maintenance strategies.

[0101] 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.

[0102] This dynamic weighting mechanism, based on a sensor adjustment matrix, effectively balances detection accuracy and environmental interference through differentiated processing and adaptive adjustment of sensor data at different locations. It not only accurately captures abnormal temperature changes in key areas of the energy storage module, but also maintains the stability of detection results under complex operating conditions, significantly improving the adaptability and reliability of the energy storage module temperature detection system in various operating scenarios.

[0103] To promptly and accurately diagnose potential faults during the operation of the energy storage module, this method establishes a fault diagnosis mechanism based on dual detection of temperature and magnetic field signals. This mechanism breaks the limitations of traditional single detection methods and significantly improves the reliability and foresight of fault diagnosis through cross-validation of multi-dimensional data. The specific steps are as follows:

[0104] After the system calculates the third temperature value of the first target based on the updated infrared image data, it compares it with a preset reference temperature value. If the third temperature value is higher than the reference temperature value, it indicates that the first target may be locally overheating. However, temperature abnormalities do not always indicate equipment failure. Non-fault factors, such as sudden changes in ambient temperature or short periods of high load, can also cause temperature increases. Therefore, the system does not immediately determine that the equipment is faulty, but instead implements more precise detection methods.

[0105] The system accurately identifies the localized overheating area corresponding to the third temperature value from the updated infrared image data. This identification process relies on advanced image analysis algorithms to precisely match temperature data with image pixels, thereby pinpointing the specific location of the overheated area. Subsequently, high-precision magnetic field detection equipment is used to collect the first magnetic field signal from the localized overheating area. This magnetic field signal contains rich electrical information and is essentially a manifestation of the electrical conditions within the energy storage module, such as current distribution and electromagnetic induction. Faults such as poor contact or winding shorts within the energy storage module disrupt the normal current path and distribution, causing significant changes in the magnetic field signal.

[0106] The system compares the collected first magnetic field signal with a preset reference signal value. If the first magnetic field signal is greater than the reference signal value, it indicates that the electromagnetic state of the locally overheated area is abnormal, indicating a potential electrical fault. The system immediately issues an equipment fault notification. Fault notification methods include but are not limited to audio and visual alarms, text message notifications to operation and maintenance personnel, and marking the fault location on the energy storage monitoring platform, ensuring that operation and maintenance personnel have immediate access to fault information.

[0107] For example, during a routine inspection of an energy storage station, the system, through infrared image analysis, discovered that the third temperature value of the first target reached 85°C, exceeding the preset reference temperature of 80°C. The system then identified the winding area as a localized overheating site and used a high-sensitivity magnetic field sensor mounted on a drone to collect magnetic field signals. The first magnetic field signal strength at this location was 50μT, significantly higher than the preset reference signal value of 30μT. Based on the dual anomalies in both temperature and magnetic field signals, the system quickly determined that the energy storage device might be faulty and immediately issued a fault alert. Upon receiving the notification, operations and maintenance personnel conducted a detailed inspection of the energy storage device and ultimately confirmed the presence of the aforementioned fault. The timely discovery prevented further deterioration of the fault, preventing serious damage to the transformer and a widespread power outage.

[0108] By dual-detecting and mutually verifying temperature and magnetic field signals, this method diagnoses energy storage module faults from both thermal and electromagnetic perspectives, effectively eliminating interference from a single factor. This multi-dimensional detection approach not only significantly improves the accuracy of fault diagnosis, avoiding misdiagnoses and missed detections, but also allows for the timely capture of anomalies in the early stages of a fault's development through changes in magnetic field signals, saving valuable time for energy storage equipment maintenance and significantly enhancing the safety and stability of energy storage system operations.

[0109] To more accurately detect faults and abnormal conditions in energy storage modules, the detection mechanism has been further improved based on the existing detection process. The specific steps are as follows:

[0110] After identifying a localized overheating area, the system continuously acquires temperature data from that area. This process relies on a network of high-precision temperature sensors to ensure real-time and accurate capture of temperature changes in that area. By comparing this data with pre-set reference data, the system determines whether the temperature in that area is abnormal.

[0111] When the temperature reading exceeds the preset reference value, it indicates a severe local overheating condition, requiring further investigation. At this point, while the temperature sensor network covers the local overheating area, the system will collect a second magnetic field signal from that area. This is to obtain the magnetic field signal characteristics under specific conditions (i.e., when the temperature sensor network is covered) for comparison with the previously collected first magnetic field signal.

[0112] The system compares the first and second magnetic field signals and calculates the difference between them. This difference reflects how the magnetic field signal changes under different detection conditions (before and after the temperature sensor network is covered). By analyzing the signal difference, it can be determined whether the magnetic field signal anomaly is caused by a device failure or interference from external environmental factors.

[0113] The calculated signal difference is compared with the preset signal reference value. If the signal difference is less than the preset signal reference value, it indicates that the magnetic field signals collected twice have not changed much. The current detected magnetic field signal anomaly may not be caused by a device failure, but by external obstruction or environmental interference. In this case, the system will promptly issue an obstruction warning to alert operation and maintenance personnel to possible external interference factors.

[0114] In the complex operating environment of energy storage equipment, magnetic field signals are easily interfered with by other surrounding equipment, metal structures and other factors. If only the magnetic field signal at a single moment is detected, it is very likely that inaccurate detection results will be obtained due to environmental interference, which will lead to misjudgment of equipment failure. By comparing the difference in magnetic field signals before and after the temperature sensor network is covered, this method can effectively eliminate the interference of environmental factors on magnetic field signal detection. When the signal difference is less than the preset reference value and an occlusion warning is issued, it can be clearly indicated that the abnormality of the currently detected magnetic field signal is not a problem with the equipment itself, avoiding erroneous detection results caused by environmental interference and ensuring the authenticity and reliability of the detection results.

[0115] Take the inspection of an energy storage station as an example. During one inspection, the system detected overheating in a local area of ​​an energy storage device. The temperature data showed 85°C, exceeding the preset reference value of 70°C. The system then collected a first magnetic field signal from the overheated area, with an intensity of 40μT. Later, when the temperature sensor network covered the overheated area, it collected a second magnetic field signal with an intensity of 42μT. The calculated difference between the two signals was 2μT, which was less than the preset signal reference value of 5μT. At this point, the system determined that the abnormal magnetic field signal was likely caused by interference from metal structures or other equipment surrounding the energy storage device, rather than a malfunction in the energy storage device itself, and promptly issued an obstruction warning. Based on the warning information, operations and maintenance personnel inspected and adjusted the environment around the energy storage device. After retesting, they obtained an accurate magnetic field signal, avoiding a possible false detection and unnecessary equipment repairs.

[0116] This method forms a complete closed-loop detection process, from determining temperature data from localized overheating areas, to collecting and calculating magnetic field differences, and finally issuing occlusion warnings based on these differences. Each step is closely interconnected and mutually verified, effectively addressing potential vulnerabilities in individual detection steps. This significantly improves the accuracy and reliability of energy storage module testing, providing a strong guarantee for the stable operation of the energy storage system.

[0117] An embodiment of the present application further discloses a temperature detection and control system for an electric energy storage module, comprising a processor, wherein the processor executes the steps of any one of the above-described temperature detection and control methods for an electric energy storage module.

[0118] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A temperature detection and control method for an electric energy storage module, characterized in that: The steps include: Acquire initial infrared image data corresponding to the energy storage module in real time based on the first acquisition point; identifying a first target and a second target from the initial infrared image data; If the first target is recognized and the second target is not recognized, calculating a matching value between the initial infrared image data and the first target; If the matching value is less than a preset first reference value, controlling the first acquisition point to move a set distance in the first direction until the matching value is greater than a preset second reference value; Updating the initial infrared image data to the updated infrared image data and continuing to identify the first target and the second target; wherein the first reference value is less than the second reference value; the matching value comprehensively considers the clarity, completeness, and feature consistency of the target in the image, assigns different weights to the calculation results of the three, and obtains the matching value through weighted summation; If the updated infrared image data still identifies the first target but does not identify the second target, then calculating the first temperature value of the first target based on the infrared image data; controlling the proximity temperature measurement module to approach the first target, measuring the temperature of the first target to obtain a second temperature value; calculating a comprehensive temperature value based on the first temperature value and the second temperature value, and outputting the comprehensive temperature value as the detected temperature value of the first target; the proximity temperature measurement module is a device capable of performing non-contact close-range temperature measurement close to the first target, including a drone, a humanoid robot, an unmanned vehicle, or a robotic arm equipped with a temperature measurement sensor; Otherwise, if the first target and the second target are identified, the distance between the first target and the second target is calculated as the target distance; if the target distance is less than a preset first reference distance, the first acquisition point is controlled to move a set distance in a second direction until the target distance is greater than a preset second reference distance; wherein the first direction and the second direction have a set angle; the initial infrared image data is updated, and a third temperature value of the first target is calculated based on the updated infrared image data, and the third temperature value is output as the first target detection temperature value; wherein, when the target distance is less than the preset first reference distance, it indicates that the first target and the second target have overlap or adjacent interference in the image; If the output first target detection temperature value is greater than the preset alarm temperature value, an alarm prompt will be issued.

2. The temperature detection and control method of the power storage module according to claim 1, characterized in that: The step of calculating the third temperature value of the first target based on the updated infrared image data further includes the following sub-steps: Calculating a first infrared imaging area value of the first target according to the updated infrared image data; Calculating a second infrared imaging area value of the second target according to the updated infrared image data; Calculating an area difference between the first infrared imaging area value and the second infrared imaging area value; If the area difference is positive, the set angle is adjusted inversely according to the area difference. The larger the area difference is, the smaller the set angle is, and the smaller the area difference is, the larger the set angle is. If the area difference is negative, the set angle is adjusted according to the positive correlation of the area difference. The larger the area difference is, the larger the set angle is, and the smaller the area difference is, the smaller the set angle is. The calculation of the area difference and the adjustment of the angle in this step are only applicable to the scenario where the second target is recognized.

3. The temperature detection and control method of the power storage module according to claim 1, characterized in that: 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 further includes the following sub-steps: The proximity temperature measurement module includes a drone and a temperature sensor provided on the drone, wherein the temperature sensor has a temperature measurement projection in the infrared image when it approaches the energy storage module; updating the infrared image data; identifying the temperature measurement projection from the updated infrared image data; If the temperature measurement projection is not recognized, an obstruction warning is issued; If the temperature measurement projection is identified, the distance or angle between the proximity temperature measurement module and the first target is adjusted inversely according to the size of the temperature measurement projection; the larger the temperature measurement projection, the smaller the distance or angle; the smaller the temperature measurement projection, the larger the distance or angle; In this step, the set distance is the moving distance in the first direction, and the set angle is the angle between the first direction and the second direction.

4. The temperature detection and control method of the power energy storage module according to claim 1, characterized in that: 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 further includes the following sub-steps: The proximity temperature measurement module includes a drone and a temperature measurement sensor network arranged on the drone, wherein each grid node of the temperature measurement sensor network is provided with a temperature sensor. Acquiring temperature data from all temperature sensors; An average value of all the temperature data is calculated as the second temperature value.

5. The temperature detection and control method of the power storage module according to claim 4, characterized in that: The step of obtaining the temperature data of all the temperature sensors further includes the following sub-steps: Setting a sensor adjustment matrix corresponding to the temperature measurement sensor network, wherein the elements in the sensor adjustment matrix are weighted coefficients corresponding to each of the temperature sensors; The temperature data is calculated based on the sensor adjustment matrix and the collected temperature value of the temperature sensor; The weighting coefficients at the middle position of the sensor adjustment matrix are adjusted in a positive correlation according to the second temperature value, and the weighting coefficients at the edge positions of the sensor adjustment matrix are adjusted in an anti-correlation manner.

6. The temperature detection and control method of the power energy storage module according to claim 4, characterized in that: The method further comprises the steps of: If the third temperature value is greater than a preset reference temperature value, identifying a local overheating location corresponding to the third temperature value from the updated infrared image data; collecting a first magnetic field signal of the local overheating portion; If the first magnetic field signal is greater than a preset reference signal value, an equipment failure prompt is issued.

7. The temperature detection and control method of the power storage module according to claim 6, characterized in that: The method further comprises the steps of: Acquiring temperature data corresponding to the local overheating portion; If the temperature data is greater than a preset reference data, a temperature measurement sensor network is set up. When the temperature measurement sensor network covers the local overheating area, a second magnetic field signal of the local overheating area is collected; the signal difference between the first magnetic field signal and the second magnetic field signal is calculated; if the signal difference is less than a preset signal reference value, an occlusion warning is issued.

8. A temperature detection and control system for an electric energy storage module, characterized in that: The method comprises a processor, wherein 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 to 7.

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