Visualization method and system for energy storage cabinet and storage medium

Abstract

The invention relates to the technical field of visualization, in particular to a visualization method and system for an energy storage cabinet and a storage medium. The method comprises the following steps: acquiring data of the energy storage cabinet, acquiring image information based on the data, and determining an operation visualization state of the energy storage cabinet; analyzing the stability gradient attenuation trend of the energy storage cabinet by using the visual state data, predicting the abnormal interaction influence state of the battery of the energy storage cabinet, and evaluating the degradation condition of the battery; estimating the health degree of the battery, detecting the unbalance condition of power supply, and evaluating the overload degree of the dynamic load of the transmission line; predicting the loss increase trend of the power transmission line; the heat effect accumulation condition of the energy storage cabinet is estimated based on the line loss condition, the deterioration trend of the energy storage cabinet is predicted by combining the heat effect and the stress abnormal condition, and the service life of the energy storage cabinet is estimated. According to the method, the energy storage cabinet is visually optimized, so that the fault identification of the energy storage cabinet is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of visualization technology, and in particular to a visualization method, system and storage medium for an energy storage cabinet. Background Art

[0002] Energy storage cabinets, as core components in energy storage systems, store and release electrical energy, playing a vital role in grid stability, efficient energy utilization, and equipment safety. However, energy storage cabinets can develop a series of structural and electrical faults during long-term operation. If these faults are not detected and repaired promptly, they can lead to reduced energy storage system efficiency and even damage to the equipment or safety hazards. Traditional energy storage cabinet monitoring methods rely on manual inspections or simple sensor data monitoring. This approach is not only inefficient but also makes it difficult to detect potential risks in the early stages of a fault. With the development of technologies such as artificial intelligence, big data, and image processing, visualization-based monitoring methods are gradually becoming a new trend. However, traditional energy storage cabinet visualization methods suffer from inaccurate analysis of energy storage cabinet service life and abnormal aging. Summary of the Invention

[0003] Based on this, it is necessary to provide a visualization method, system and storage medium for an energy storage cabinet to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a visualization method for an energy storage cabinet includes the following steps:

[0005] Step S1: acquiring energy storage cabinet data; collecting energy storage cabinet image data based on the energy storage cabinet data; and determining the energy storage cabinet operation visualization state based on the energy storage cabinet image data.

[0006] Step S2: Detecting the energy storage cabinet stability gradient attenuation trend based on the energy storage cabinet operation visualization status; predicting the energy storage cabinet battery abnormal interaction impact status based on the energy storage cabinet stability gradient attenuation trend; and predicting the energy storage cabinet battery degradation status based on the energy storage cabinet battery abnormal interaction impact status.

[0007] Step S3: Evaluate the battery health of the energy storage cabinet based on the estimated degradation of the battery in the energy storage cabinet; detect the imbalance of the power supply of the energy storage cabinet based on the battery health of the energy storage cabinet; evaluate the dynamic load overload degree of the transmission line based on the imbalance of the power supply of the energy storage cabinet; and predict the growth trend of power transmission line loss based on the dynamic load overload degree of the transmission line.

[0008] Step S4: Estimate the accumulated thermal effect of the energy storage cabinet based on the growth trend of power transmission line loss and the degree of dynamic load overload of the transmission line; predict the degradation trend of the energy storage cabinet based on the accumulated thermal effect of the energy storage cabinet; and estimate the service life of the energy storage cabinet based on the degradation trend of the energy storage cabinet to obtain the service life data of the energy storage cabinet.

[0009] By acquiring energy storage cabinet data and collecting image data, the present invention provides a comprehensive understanding of the cabinet's operating status, providing a foundation for subsequent visual analysis. Analysis of the cabinet's image data accurately determines the cabinet's operating status, enabling the system to promptly detect any abnormal changes. Further monitoring of the cabinet's stability gradient decay trend allows for precise assessment of the cabinet's structure and battery safety, timely prediction of abnormal battery interactions, and effective mitigation of potential safety risks. By predicting the degradation of the cabinet's batteries, the system provides a comprehensive understanding of battery health, providing a scientific basis for battery management and maintenance. Based on the battery health assessment, power supply imbalances can be promptly identified, enabling timely adjustments and optimization of the cabinet's power system to avoid power waste or safety hazards caused by these imbalances. By predicting the dynamic load overload level of the transmission line and the power transmission line losses, the system can proactively identify transmission problems, prevent further energy loss, and thus optimize energy management within the cabinet. By analyzing the accumulated thermal effects and degradation trends of energy storage cabinets, the system can accurately assess the performance degradation of the energy storage cabinets and predict their actual service life through life estimation. Therefore, the present invention optimizes traditional visualization methods for energy storage cabinets, resolving issues with traditional visualization methods for energy storage cabinets, such as inaccurate analysis of energy storage cabinet service life and inaccurate analysis of abnormal aging of energy storage cabinets. This improves the accuracy of both analysis of energy storage cabinet service life and analysis of abnormal aging.

[0010] The present invention further provides a visualization system for an energy storage cabinet, which is used to execute the visualization method for an energy storage cabinet as described above. The visualization system for an energy storage cabinet includes:

[0011] A visualization state determination module is used to obtain energy storage cabinet data; collect energy storage cabinet image data based on the energy storage cabinet data; and determine the energy storage cabinet operation visualization state based on the energy storage cabinet image data;

[0012] The battery degradation prediction module is used to detect the stability gradient attenuation trend of the energy storage cabinet based on the visual operation status of the energy storage cabinet; predict the abnormal interaction influence status of the energy storage cabinet batteries based on the stability gradient attenuation trend of the energy storage cabinet; and predict the battery degradation status of the energy storage cabinet based on the abnormal interaction influence status of the energy storage cabinet batteries;

[0013] The line loss growth trend prediction module is used to estimate and evaluate the battery health of the energy storage cabinet based on the battery degradation status of the energy storage cabinet; detect the imbalance of the energy storage cabinet power supply based on the energy storage cabinet battery health; evaluate the dynamic load overload level of the transmission line based on the imbalance of the energy storage cabinet power supply; and predict the growth trend of power transmission line loss based on the dynamic load overload level of the transmission line;

[0014] The energy storage cabinet service life estimation module is used to estimate the accumulated thermal effects of the energy storage cabinet based on the growth trend of power transmission line losses and the degree of dynamic load overload of the transmission line; predict the degradation trend of the energy storage cabinet based on the accumulated thermal effects of the energy storage cabinet; and estimate the service life of the energy storage cabinet based on the degradation trend of the energy storage cabinet to obtain the service life data of the energy storage cabinet.

[0015] A computer-readable storage medium stores a computer program, wherein the computer program is used to execute the visualization method for an energy storage cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of the steps of a visualization method for an energy storage cabinet;

[0017] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0018] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0019] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0020] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0021] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0022] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0023] To achieve this, please refer to Figures 1 to 3 , a visualization method for an energy storage cabinet, comprising the following steps:

[0024] Step S1: acquiring energy storage cabinet data; collecting energy storage cabinet image data based on the energy storage cabinet data; and determining the energy storage cabinet operation visualization state based on the energy storage cabinet image data.

[0025] In embodiments of the present invention, multiple sensor devices built into the energy storage cabinet are used to acquire operational data, including key parameters such as temperature, humidity, current, voltage, and load. The collection of this data is closely related to real-time monitoring. The data collection process involves the deployment of sensors, which are placed at various locations within the energy storage cabinet to comprehensively monitor its operation. The temperature sensor primarily records temperature changes inside and outside the cabinet in real time. The humidity sensor detects the internal humidity level. The current sensor and voltage sensor monitor the cabinet's current flow and voltage fluctuations, respectively. The load sensor monitors the cabinet's load. This data is connected to the cabinet's monitoring system and data acquisition module, and then transmitted to a central processing platform. The central platform receives this information in real time through the data acquisition system and performs a preliminary assessment of the cabinet's operational status. Specifically, this data includes not only conventional parameters such as temperature, humidity, current, and voltage, but also covers the cabinet's mechanical structure, the temperature distribution of its electronic components, and the health of its battery pack, ensuring a comprehensive understanding of the cabinet's operational health. To further enhance monitoring of the energy storage cabinet's operating status, an image acquisition system is used to capture the cabinet's exterior and heat distribution. A visible light camera and an infrared imager, respectively, are installed on the cabinet's exterior to capture images of the cabinet's exterior. The visible light camera, configured with a resolution of 1920×1080 pixels and a frame rate of 30 FPS, captures the cabinet's overall exterior appearance. These images facilitate subsequent analysis of the cabinet's surface physical characteristics. The infrared imager captures the cabinet's temperature. Its configuration parameters include a temperature measurement range of -20°C to 300°C, a minimum temperature resolution of 0.05°C, and an image frame rate of 25Hz. This allows for accurate detection of heat distribution on and within the cabinet, particularly capturing hotspots with high precision. The captured visible light and infrared image data are fed into a specially designed image processing system, which processes and analyzes the image data using image recognition algorithms to automatically detect and annotate the visual status of the cabinet's exterior and interior. Image recognition technology can identify anomalies or potential issues in the energy storage cabinet by analyzing surface features, temperature hotspots, and key landmarks within the image. In parallel with image processing, the system also integrates real-time sensor data. Using data fusion technology, it combines image information with sensor data to conduct an in-depth analysis of the overall status of the energy storage cabinet, providing a visual representation of its operational status.

[0026] Step S2: Detecting the energy storage cabinet stability gradient attenuation trend based on the energy storage cabinet operation visualization status; predicting the energy storage cabinet battery abnormal interaction impact status based on the energy storage cabinet stability gradient attenuation trend; and predicting the energy storage cabinet battery degradation status based on the energy storage cabinet battery abnormal interaction impact status.

[0027] In this embodiment of the present invention, based on the visualized operational status information of the energy storage cabinet obtained in step S1, the system uses thermal imaging images and mechanical status data to employ various analysis techniques to assess the cabinet's stability gradient decay trend. Specifically, the system acquires thermal images of the cabinet and uses thermal imaging technology to analyze temperature variations across the cabinet's surface. Combined with the cabinet's mechanical status information, the system further determines the cabinet's structural stability. The system utilizes a set of physics-based algorithms to monitor the overall structural deformation of the cabinet, calculate the cabinet's stress and deformation under different operating conditions, and identify risk points. For example, by analyzing the cabinet's deformation during load changes, the system can detect deformations exceeding normal limits in certain parts of the cabinet, indicating potential structural issues in these areas. Furthermore, the system combines the cabinet's hotspot distribution data to analyze heat accumulation in different areas, thereby inferring the cabinet's stability trend. If the temperature in certain areas rises abnormally, the system will issue a warning regarding structural degradation or uneven loading caused by thermal effects, further accelerating the cabinet's stability decay. Based on this, the system further assesses the battery status and combines it with the thermal characteristics of the energy storage cabinet's battery packs. Specifically, the system acquires real-time data on battery surface temperature and, in combination with multiple parameters such as internal battery temperature, voltage, and current, analyzes the interactions between batteries within the energy storage cabinet and their potential impacts. For example, if the temperature of certain batteries rises abnormally, the system can determine whether these batteries are overheating. Combined with the status of surrounding batteries, it can also determine whether there are inter-battery interactions. For example, heat conduction between batteries could cause the temperature of adjacent batteries to rise, leading to faults such as short circuits and increased internal resistance. Through this multi-dimensional data analysis, the system can accurately infer abnormal interactions between batteries within the energy storage cabinet. Specifically, if the temperature of certain batteries exceeds a set safety threshold, combined with fluctuations in battery voltage and current, the system can predict whether these batteries are entering a stage of excessive degradation. Based on changes in temperature and internal resistance, the system can further infer the degradation trend of the entire energy storage cabinet's batteries. Specifically, battery degradation trends include not only capacity loss but also factors such as an increase in internal resistance. Using this data, the system can accurately predict the extent of battery degradation within the energy storage cabinet.

[0028] Step S3: Evaluate the battery health of the energy storage cabinet based on the estimated degradation of the battery in the energy storage cabinet; detect the imbalance of the power supply of the energy storage cabinet based on the battery health of the energy storage cabinet; evaluate the dynamic load overload degree of the transmission line based on the imbalance of the power supply of the energy storage cabinet; and predict the growth trend of power transmission line loss based on the dynamic load overload degree of the transmission line.

[0029] In this embodiment of the present invention, based on the energy storage cabinet battery degradation data obtained in step S2, the system calculates the health of the energy storage cabinet's batteries. This calculation comprehensively considers multiple key battery factors, including actual capacity, charge and discharge cycles, internal resistance, and operating temperature, using a specialized health assessment algorithm. This algorithm is typically based on a battery life model. By analyzing the battery's aging characteristics under different operating conditions and combining them with actual usage, it derives a specific battery health indicator, typically expressed as a percentage. For example, if the battery capacity drops below 80% of its original capacity, the internal resistance increases to twice its normal value, and the charge and discharge cycles far exceed the designed value, the system will indicate a low health assessment result. This health assessment provides the system with a comprehensive understanding of the current battery condition and timely identifies potential issues. Next, the system further analyzes the energy storage cabinet's power supply status based on the battery health data. If the energy storage cabinet's battery health is low, it means the battery cannot effectively support the cabinet's operation under high load, leading to an imbalance in the power supply. At this point, the system monitors the energy storage cabinet's battery current, output voltage, and load distribution in real time to detect any power supply imbalances. The system determines whether the batteries are unable to provide sufficient power to support the cabinet's load or whether battery voltage instability is occurring, potentially impacting the cabinet's stable operation. If a power supply imbalance occurs, the system immediately triggers an alarm and provides maintenance recommendations. Next, based on the power supply imbalance, the system further assesses the dynamic overload level of the power transmission line. To this end, the system monitors current and voltage fluctuations on the power transmission line in real time and uses load flow analysis technology to calculate the load status of the power transmission line in real time. This allows the system to assess the load level of the line in real time and determine whether there is a risk of overload. If the monitored current and voltage on the power transmission line exceed safe ranges, the system conducts further load analysis to determine whether the line is overloaded. In particular, when battery health is low or the load is high, the system can accurately predict whether the load on the power transmission line will continue to increase, leading to increased line losses. By analyzing current and temperature trends, the system can predict future increases in line losses. Specifically, as the load continues to increase, the resistance of the power transmission line will cause the energy loss to gradually increase. The system can predict the future development of line loss based on the current load flow data and the temperature change trend of the line.

[0030] Step S4: Estimate the accumulated thermal effect of the energy storage cabinet based on the growth trend of power transmission line loss and the degree of dynamic load overload of the transmission line; predict the degradation trend of the energy storage cabinet based on the accumulated thermal effect of the energy storage cabinet; and estimate the service life of the energy storage cabinet based on the degradation trend of the energy storage cabinet to obtain the service life data of the energy storage cabinet.

[0031] In this embodiment of the present invention, based on the transmission line loss growth data and load overload status obtained in step S3, the system further evaluates the accumulated thermal effects of the energy storage cabinet. Using a thermal analysis algorithm, the system uses current, voltage, and internal cabinet temperature data to simulate and calculate the heat distribution of each cabinet component. The heat generated by the flow of current, as well as the power consumption of the battery pack and components within the cabinet, directly affect the cabinet's temperature changes. The system monitors the temperatures of various components within the cabinet in real time, particularly the batteries, transmission lines, and power conversion equipment, to determine whether overheating is occurring. Abnormally high temperatures in certain components indicate that these components are experiencing significant thermal stress, exceeding their normal operating range, thereby accelerating component aging. Based on this information, the system calculates the rate of thermal effect accumulation and identifies components that have experienced damage or performance degradation due to prolonged overheating. Specifically, when the system detects that the battery pack surface temperature is persistently above a safety threshold, indicating the presence of a localized hot spot in a particular area of the cabinet, the system analyzes the impact of this heat accumulation on the cabinet structure, particularly its negative impact on the battery chemical reactions. As temperature rises, chemical reactions within batteries accelerate, leading to rapid capacity degradation and potentially safety issues. The system then uses a comprehensive degradation model to analyze the aging process of various components within the energy storage cabinet. This degradation model integrates multiple factors, including temperature fluctuations, mechanical stress, material aging, and battery degradation. By monitoring the stress and thermal loads of each component under different operating conditions over a long period of time, it identifies critical components prone to failure. For example, thermal effects can cause electrolyte evaporation or electrode material deterioration, while prolonged overheating can damage internal battery cells. Based on these degradation trends, the system further identifies components most prone to premature failure and provides their remaining service life. Furthermore, the system uses a life prediction model to estimate the remaining service life of the energy storage cabinet, taking into account the overall structural degradation trends and performance changes of the energy storage cabinet after extended use. The life prediction model calculates the expected remaining service life of the energy storage cabinet based on a comprehensive analysis of data such as the usage, aging rate, and the impact of thermal effects on each component within the cabinet.

[0032] Preferably, step S1 includes the following steps:

[0033] Step S11: setting the visible light camera resolution to 1920×1080 pixels, the minimum light sensitivity to 0.01 lux, and the frame rate to 30 FPS;

[0034] In an embodiment of the present invention, when setting up a visible light camera, the resolution of the camera is determined to be 1920×1080 pixels to ensure that the captured image has sufficient clarity and can provide detailed external surface features of the energy storage cabinet. The minimum light sensitivity of the visible light camera is set to 0.01lux to ensure that changes in the surface of the energy storage cabinet can be clearly captured even in low-light environments, thereby ensuring the accuracy and integrity of the data. In practical applications, the frame rate is set to 30FPS to ensure the smoothness of image acquisition, especially when dynamically monitoring the outside of the energy storage cabinet, to smoothly capture details of fast movement or changes. The visible light camera transmits the captured image data to the central processing system via wired or wireless means, and the system uses these image data to further analyze the external visualization status of the energy storage cabinet.

[0035] Step S12: setting the temperature measurement range of the infrared imager to -20°C to 300°C, the minimum temperature resolution to 0.05°C, and the image frame rate to 25Hz;

[0036] In the embodiment of the present invention, the configuration parameters of the infrared imager are set to a temperature measurement range of -20°C to 300°C, ensuring that it can cover the working conditions of the energy storage cabinet at extremely low to extremely high temperatures, especially for monitoring the internal environment of the energy storage cabinet with large temperature fluctuations. The minimum temperature resolution is 0.05°C, which ensures that the infrared imager can accurately capture the temperature changes inside the energy storage cabinet, especially the slight temperature differences in the hot spot area. The image frame rate is set to 25Hz, so that the infrared imager can track the changes in the internal temperature of the energy storage cabinet in real time, and match these data with the image data collected by the visible light camera to ensure the synchronous update of temperature distribution information and visualization information. In actual applications, the infrared imager sends real-time temperature data to the central processing platform through a dedicated data transmission protocol, providing an accurate basis for subsequent thermal effect analysis.

[0037] Step S13: Acquire energy storage cabinet data;

[0038] In this embodiment of the present invention, the process of acquiring energy storage cabinet data utilizes a variety of built-in sensor devices. These sensors include temperature sensors, humidity sensors, current sensors, voltage sensors, and load sensors, capable of recording the internal and external operating status of the energy storage cabinet in real time. All sensor data is transmitted to a data acquisition module via the energy storage cabinet's monitoring system. The module then transmits the data to a central processing platform via wired or wireless means. The platform receives this data and performs preliminary processing and storage. The data includes information such as the energy storage cabinet's ambient temperature, humidity, load current, and voltage changes.

[0039] Step S14: using a visible light camera to collect energy storage cabinet image data;

[0040] In this embodiment of the present invention, when using a visible light camera to collect energy storage cabinet image data, the visible light camera is activated and set to a resolution of 1920×1080 pixels to ensure high image clarity. The camera captures images of the exterior of the energy storage cabinet at a frame rate of 30 FPS. These images include key features such as the cabinet's surface, structural features, and wiring. The camera uploads the image data to the central processing system via a configured transmission interface. During the image acquisition process, the system monitors image quality in real time to ensure that there is no overexposure, blurring, or data loss during the acquisition process, and that the quality of each frame meets the analysis standards.

[0041] Step S15: Determine the visual operating status of the energy storage cabinet using the infrared imager and the energy storage cabinet image data.

[0042] In an embodiment of the present invention, after acquiring data from a visible light camera and an infrared imager, the system uses image processing algorithms to analyze the operating status of the energy storage cabinet. The system analyzes images captured by the visible light camera to identify surface features on the exterior of the energy storage cabinet, including deformation of the mechanical structure and surface damage. Simultaneously, the temperature distribution images provided by the infrared imager are used to analyze the heat distribution inside and outside the energy storage cabinet. Thermal imaging can be used to detect overheating in the energy storage cabinet, particularly temperature changes in hot spots. By combining these two data sets and utilizing data fusion technology, the system can comprehensively assess the operational visualization of the energy storage cabinet, identify potential failure risks or abnormal behavior, and generate a complete visualization of the energy storage cabinet's operational status.

[0043] Preferably, step S15 includes the following steps:

[0044] Step S151: performing energy storage cabinet image preprocessing on the energy storage cabinet image data to obtain energy storage cabinet image preprocessing data;

[0045] In an embodiment of the present invention, energy storage cabinet image data is subjected to energy storage cabinet image preprocessing. The energy storage cabinet image data is collected by a visible light camera and an infrared imager and transmitted to a data processing system via an image transmission module. Image preprocessing includes operations such as image denoising, brightness adjustment, contrast enhancement, and edge detection. Denoising uses a Gaussian blur or median filtering algorithm to remove noise from the image through smoothing to ensure image clarity. Brightness adjustment is used to eliminate uneven image brightness caused by changes in lighting. Contrast enhancement helps to highlight the detailed features of the energy storage cabinet surface, making subsequent image analysis more accurate. Finally, edge detection (such as the Canny operator) is used to extract structural edges in the image, clearly displaying the external and internal geometric structures of the energy storage cabinet. After these preprocessing steps, the resulting energy storage cabinet image preprocessed data provides clear and noise-free image input for subsequent three-dimensional point cloud feature extraction and surface feature analysis.

[0046] Step S152: collecting three-dimensional point cloud features of the energy storage cabinet image based on the energy storage cabinet image preprocessing data;

[0047] In an embodiment of the present invention, three-dimensional point cloud features of the energy storage cabinet image are collected based on the energy storage cabinet image preprocessing data. After image preprocessing, the three-dimensional spatial information of the energy storage cabinet is extracted by using a computer vision algorithm in combination with three-dimensional imaging technologies such as binocular stereo vision or laser radar (LiDAR). Using image data taken at different angles by two visible light cameras, the parallax method is used to calculate the distance between each point on the surface of the object, thereby generating three-dimensional point cloud data of the energy storage cabinet image. Each point in the point cloud data represents a small area on the surface of the energy storage cabinet, and its coordinates contain information in three dimensions: X, Y, and Z. The collection of three-dimensional point cloud features is achieved through high-precision visual computing, ensuring that the spatial form of the external and internal structures of the energy storage cabinet can be accurately described. The obtained three-dimensional point cloud data provides detailed geometric information for subsequent surface feature extraction and structural analysis.

[0048] Step S153: extracting surface features of the energy storage cabinet image based on the three-dimensional point cloud features of the energy storage cabinet image;

[0049] In an embodiment of the present invention, surface features of the energy storage cabinet image are extracted based on the three-dimensional point cloud features of the energy storage cabinet image. This extraction process utilizes the spatial information in the point cloud data and employs three-dimensional reconstruction techniques to model the energy storage cabinet surface. A surface normal estimation algorithm is then used to process the point cloud data, calculating the normal direction of each point on the energy storage cabinet surface and determining the local surface morphology and structure. The point cloud is then segmented using techniques such as region growing algorithms, labeling and classifying different areas of the energy storage cabinet surface to analyze the characteristics of these regions. Next, a surface smoothing algorithm is used to optimize the surface model and remove noise, resulting in a smoother and more accurate representation of the geometric features of the energy storage cabinet surface. In this way, the external surface features of the energy storage cabinet, including structural damage, deformation, or any abnormal changes, are extracted from the three-dimensional point cloud.

[0050] Step S154: detecting the structural condition of the energy storage cabinet based on the surface features of the energy storage cabinet image;

[0051] In an embodiment of the present invention, the structural condition of an energy storage cabinet is detected based on the surface features of the energy storage cabinet image. After extracting the surface features, the system performs a structural condition analysis based on the surface geometry data of the energy storage cabinet. Specifically, the structural stability of the energy storage cabinet is determined by detecting whether the geometric form of the energy storage cabinet surface contains obvious deformation, cracks, damage, or irregular areas. Shape matching and geometric deformation detection algorithms are used to detect surface deformation on the exterior of the energy storage cabinet. For example, a template matching-based method is used to compare the deviation of the surface features of the energy storage cabinet with a standard model and calculate the degree of deformation of each part. If cracks or deformation are found on the surface of the energy storage cabinet, the system automatically marks them and calculates the degree of damage. Based on this information, the system further analyzes whether the energy storage cabinet structure is in a normal state to ensure its stable operation. If an abnormality is found, the system will issue an alarm based on the analysis results to assist maintenance personnel in further inspection and repair.

[0052] Step S155: using an infrared imager to collect the hot spot distribution of the energy storage cabinet;

[0053] In an embodiment of the present invention, an infrared imager is used to collect the distribution of hot spots in the energy storage cabinet. The infrared imager's task in this process is to collect temperature distribution data inside and on the surface of the energy storage cabinet in real time. The infrared imager has a temperature measurement range from -20°C to 300°C, with a minimum temperature resolution of 0.05°C, and is capable of detecting hot spots in the energy storage cabinet with high precision. Through infrared thermal imaging, the system accurately locates hot spots on the surface and inside the energy storage cabinet caused by overload, damage, or battery abnormalities. These hot spots are caused by problems such as overheating of the batteries inside the energy storage cabinet, poor contact, or excessive current. The collected temperature data is processed using a thermal imaging algorithm to generate a thermal distribution map of the energy storage cabinet hot spots, showing temperature changes in different areas. The infrared imaging data will be combined with the data from the energy storage cabinet sensors to provide strong support for subsequent thermal effect analysis.

[0054] Step S156: Calculating the energy storage cabinet hotspot distribution balance according to the energy storage cabinet hotspot distribution;

[0055] In an embodiment of the present invention, the balance of the hot spot distribution of the energy storage cabinet is calculated based on the hot spot distribution of the energy storage cabinet. Based on the hot spot distribution data of the energy storage cabinet collected from the infrared imager, the system analyzes the uniformity of the temperature distribution. Specifically, the system evaluates the balance of the temperature distribution by calculating the temperature difference between different areas of the energy storage cabinet. If the temperature of a certain area is significantly higher than that of the other areas, it indicates that there is abnormal overheating in the area, which is caused by damage to the internal components of the energy storage cabinet or unbalanced load. When calculating the balance, the system also takes into account the load distribution of the energy storage cabinet and changes in the operating environment, and uses statistical methods to analyze the degree of deviation of the temperature distribution.

[0056] Step S157: Determine the energy storage cabinet operation visualization state according to the energy storage cabinet hot spot distribution balance and the energy storage cabinet structure condition.

[0057] In an embodiment of the present invention, the operational visualization status of the energy storage cabinet is determined based on the balanced distribution of hotspots within the energy storage cabinet and the structural condition of the energy storage cabinet. The system combines the structural condition analysis results of the energy storage cabinet with the balanced distribution of hotspots to comprehensively assess the operational status of the energy storage cabinet. If the structure of the energy storage cabinet is severely deformed or damaged, and the hotspots are unevenly distributed, the system determines that the operational status of the energy storage cabinet is abnormal and marks it as a high-risk state. If the energy storage cabinet structure is stable and the hotspots are evenly distributed, the energy storage cabinet is in normal operation. This process utilizes techniques such as decision trees, fuzzy logic, or multidimensional data fusion to comprehensively analyze different data sources and obtain a comprehensive visualization of the overall status of the energy storage cabinet.

[0058] Preferably, the energy storage cabinet stability gradient attenuation trend detection in step S2 includes:

[0059] Collect structural defects of energy storage cabinets based on the visual operation status of the energy storage cabinets;

[0060] In this embodiment of the present invention, the operational status of the energy storage cabinet is visualized using multiple sensors, covering data such as temperature, humidity, current, voltage, and load. This information is also captured using image data from a visible light camera and infrared imager. After image preprocessing, three-dimensional point cloud feature extraction, and surface feature analysis, the image data is used to obtain structural information about the energy storage cabinet, including surface shape, cracks, deformations, and defective areas. Based on this image data, the system uses computer vision algorithms to perform defect detection on the external and internal structures of the energy storage cabinet. Through template matching, deformation detection, and geometric feature analysis, defects such as cracks, dents, and excessive wear are automatically identified on the surface and internal structure of the energy storage cabinet. Defect information is annotated with coordinate points, size, and shape, forming structural defect data for the energy storage cabinet.

[0061] Using the structural defects of the energy storage cabinet to identify the degree of structural deformation of the energy storage cabinet;

[0062] In an embodiment of the present invention, once the structural defect data of the energy storage cabinet is obtained, the system further analyzes the degree of deformation of the energy storage cabinet. By comparing the geometric data of the external and internal structures of the energy storage cabinet, the deformation of the energy storage cabinet during operation is detected. The degree of deformation is quantified by calculating the displacement of each component and the change in the overall geometric shape. Common methods include calculating the percentage of deformation by comparing the geometric differences between the initial model and the current model of the energy storage cabinet. If the degree of deformation of the energy storage cabinet exceeds 0.5%, the system will mark it as abnormal and enter the next step of the detection process. This calculation is achieved by solving the elastic deformation equation of the object and combining it with sensor data. The degree of deformation of the energy storage cabinet structure is evaluated to determine whether it will affect the overall stability of the energy storage cabinet.

[0063] When the deformation degree of the energy storage cabinet structure exceeds 0.5% and the energy storage cabinet structure defects are present, calculate the abnormal growth trend of the local mechanical stress of the energy storage cabinet;

[0064] In an embodiment of the present invention, when the structural deformation of the energy storage cabinet is detected to exceed 0.5%, the system will further analyze the changes in the local mechanical stress of the energy storage cabinet in combination with the structural defects of the energy storage cabinet. The system performs mechanical simulation of the various components of the energy storage cabinet based on finite element analysis (FEA). By inputting the current deformation degree and structural defect information of the energy storage cabinet, the stress distribution in each area of the energy storage cabinet is calculated. In the local deformation area, the system will pay special attention to the abnormal growth trend of mechanical stress, for example, whether the stress in certain areas exceeds the normal value or there is local stress concentration. This process solves the stress-strain relationship and combines it with external load data for calculation, which can identify whether there are stress concentration areas in the energy storage cabinet that may cause damage. If the local mechanical stress shows an abnormal growth trend, the system will mark these areas and indicate potential faults.

[0065] Detect the degree of surface cracks in the energy storage cabinet based on the increase trend of local mechanical stress in the energy storage cabinet exceeding 50MPa and the degree of structural deformation of the energy storage cabinet;

[0066] In an embodiment of the present invention, when the local mechanical stress growth trend exceeds 50 MPa, the system further detects cracks on the energy storage cabinet surface based on this high stress condition. The system uses an infrared imager to collect thermal image data of the cabinet surface. Infrared imagers can accurately capture temperature changes caused by cracks. Cracks often cause uneven heat distribution on the cabinet surface. In thermal images, the temperature at the crack location will increase or decrease abnormally, appearing as a distinct area of temperature variation. This thermal image data is input into an image processing system, which analyzes the location and size of the abnormal temperature area to preliminarily locate the location and distribution of cracks on the cabinet surface. Simultaneously, the system uses high-resolution visible light images to further analyze the cabinet surface. Advanced image processing algorithms (such as edge detection and shape analysis) use visible light images to accurately identify the presence and morphological characteristics of cracks. By extracting crack edge information, the system can accurately measure crack geometric characteristics such as length, width, and depth. Combined with mechanical stress data, the system comprehensively analyzes the crack's geometric parameters and local stress concentration to assess crack severity. If the crack exceeds 2 cm in length, the system will determine the extent of further damage caused by the crack based on the crack size and local stress conditions, and then predict its impact on the overall stability of the energy storage cabinet. If the crack poses a threat to the stability of the energy storage cabinet structure, the system will issue an alarm, indicating that there is a high risk of damage in this area, which may lead to major failure or malfunction of the energy storage cabinet structure, thus affecting the safe operation of the energy storage cabinet.

[0067] When the crack depth on the energy storage cabinet surface exceeds 2 cm, the damage to the cabinet support structure is assessed based on the growth trend of the local mechanical stress in the cabinet.

[0068] In this embodiment of the present invention, when a crack exceeding 2 cm is detected on the energy storage cabinet surface, the system uses various analytical methods to further assess the damage to the cabinet's support structure. The system monitors crack propagation and, based on the cabinet's surface crack location and morphology, develops a crack growth model. This model, based on classical mechanical theory of crack growth, specifically the relationship between stress intensity factor (K) and crack growth rate, analyzes crack growth trends under different loads. The crack location is directly related to the mechanical stress distribution within the cabinet. The system, combined with the cabinet's local stress data, predicts potential crack growth paths. Based on this, the system uses finite element analysis (FEA) to perform numerical simulations to calculate the impact of crack growth on the cabinet's support structure. This simulation allows the system to identify whether cracks will extend to critical areas of the support structure, particularly those subject to high stress, such as connection points, support columns, and load-bearing frames. If cracks extend to these areas, the system assesses their impact on the support structure's strength, using the material's mechanical and geometric properties to determine whether the support structure's load-bearing capacity is threatened. At this point, the system uses the established structural mechanics model to further analyze how the crack affects the overall mechanical response of the support structure, calculate the impact of the local stress concentration caused by the crack on the support structure, and then predict whether it will cause failure or serious damage to the support structure. If the simulation results show that the stress concentration caused by crack propagation causes the support structure to fail, then the system will be considered as a failure.

[0069] The gradient attenuation trend of the energy storage cabinet stability is detected based on the damage of the energy storage cabinet supporting structure.

[0070] In this embodiment of the present invention, the stability gradient attenuation trend of the energy storage cabinet is detected based on damage to the energy storage cabinet's support structure. This requires a detailed analysis of the state of the energy storage cabinet's support structure. The system uses various sensors, image acquisition devices, and stress analysis models to monitor the operation of each key component of the energy storage cabinet in real time. When the support structure is damaged, the system conducts an in-depth analysis of the damage type, extent, and impact. For example, if a portion of the support structure develops cracks, local deformation, or material fatigue, the system performs mechanical simulation of that component using finite element analysis (FEA) to assess the damage's impact on overall stability. Specifically, by analyzing stress concentration in the damaged area of the support structure, it is possible to determine whether the damage will lead to local failure or further extend to other components. Detecting stress concentration areas utilizes stress-strain curves and material mechanical properties to accurately calculate the stress distribution of the energy storage cabinet under specific loads, identify weaknesses, and predict the potential damage these weaknesses will cause under sustained load. Simultaneously, deformation data of the energy storage cabinet components is acquired in real time and compared with the damage to the support structure. In this way, it is possible to determine whether the energy storage cabinet will experience local stress concentration, leading to component deformation or damage, thereby affecting overall stability. If damage to the supporting structure reaches a certain level, or if components are damaged due to excessive stress, the system automatically calculates the overall stability degradation trend of the energy storage cabinet. By comparing stress, deformation, and damage data for each component of the energy storage cabinet, the system can predict the degree of overall structural strength degradation. If the calculation results indicate that damage to the supporting structure has caused a rapid decline in the overall stability of the energy storage cabinet, the system will issue a warning signal, indicating that the energy storage cabinet has entered a dangerous state.

[0071] Preferably, the prediction of abnormal interaction impact state of batteries in the energy storage cabinet described in step S2 includes:

[0072] Calculate the probability of the energy storage cabinet structure tilting according to the stability gradient attenuation trend of the energy storage cabinet;

[0073] In an embodiment of the present invention, the probability of structural tilt of the energy storage cabinet is calculated based on the cabinet's stability gradient decay trend. The stability gradient decay trend data for the energy storage cabinet is obtained through real-time monitoring of the cabinet's mechanical and structural parameters. This data includes the cabinet's deformation, stress distribution, and external environmental factors. By inputting this data into a stability analysis algorithm, the system calculates the probability of the cabinet tilting during operation. Specifically, the system utilizes mechanical models and stability theory to infer the cabinet's tilt potential based on the distribution of stress within the cabinet and changes in external loads, and outputs a tilt probability value.

[0074] Estimate the displacement degree of internal components of the energy storage cabinet based on the tilt probability of the energy storage cabinet structure;

[0075] In this embodiment of the present invention, based on the probability of the energy storage cabinet structure tilting, the system further estimates the displacement of the cabinet's internal components. The cabinet's internal components, including battery packs, wiring, and structural elements, are affected by the cabinet's tilting structure and undergo displacement. The system uses the cabinet's structural model and displacement calculation formula to infer the displacement range of each internal component based on the tilt probability. The calculations take into account the tilt direction and angle, as well as the weight and relative position of the components within the cabinet. This process estimates the displacement of the cabinet's internal components in the event of structural tilt.

[0076] The displacement degree of internal components of the energy storage cabinet and the probability of structural tilt of the energy storage cabinet are used to predict the misalignment trend of batteries inside the energy storage cabinet.

[0077] In this embodiment of the present invention, the system predicts the misalignment trend of batteries within the energy storage cabinet based on the degree of component displacement and the probability of structural tilt. Battery misalignment can lead to short circuits, poor contact, and even damage between batteries. To predict this trend, the probability of battery misalignment is calculated based on the battery's installation position in the energy storage cabinet, its tilt angle, and its physical characteristics (such as battery pack weight, size, and support method). This prediction considers the relationship between the battery displacement caused by structural tilt and the change in relative position between batteries, thereby predicting the risk of battery misalignment.

[0078] The internal structural compression of the energy storage cabinet is calculated using the battery misalignment trend and the displacement degree of the internal components of the energy storage cabinet;

[0079] In this embodiment of the present invention, the structural compression conditions within the energy storage cabinet are calculated based on the battery misalignment trend and component displacement. Battery misalignment creates mechanical compression between the battery and surrounding components, or between the batteries themselves, exacerbating battery damage or affecting normal operation. The system calculates the distances, contact points, and pressure distribution between the battery and components to determine the structural compression conditions within the energy storage cabinet. This process involves complex mechanical models, particularly analysis of contact mechanics and compressive stress, to ensure an accurate assessment of the stress conditions within the energy storage cabinet.

[0080] Estimate the expansion trend of the energy storage cabinet battery based on the compression condition of the internal structure of the energy storage cabinet;

[0081] In an embodiment of the present invention, after assessing the compression conditions within the energy storage cabinet, the system uses various sensor data and mechanical analysis, combined with battery temperature, charge and discharge data, and the degree of mechanical compression, to predict the battery expansion trend. Battery expansion is typically caused by multiple factors, the most common of which is abnormal internal chemical reactions. In particular, overcharging or overdischarging can cause imbalanced chemical reactions within the battery, generating gas or causing volume expansion. Mechanical compression within the energy storage cabinet further exacerbates this phenomenon. The system monitors battery temperature data to determine whether the battery is experiencing abnormal temperature conditions. Increased battery temperature is often caused by overcharging, overdischarging, or excessive internal chemical reactions, all of which lead to increased gas production within the battery, causing expansion. If the battery temperature reaches a certain threshold, combined with the output data from the system's temperature sensors, it is inferred that the chemical reactions within the battery have run away, increasing the risk of expansion. Secondly, the system further assesses the battery's operating status by recording and analyzing battery charge and discharge data in real time, particularly fluctuations in charging current and voltage. Overcharging or overdischarging can lead to uneven distribution of electrolyte within the battery, which in turn causes battery expansion. Especially at high charging voltages, the battery's electrolyte easily decomposes, generating gas, increasing internal pressure, and causing expansion. If the system detects that the battery's charging process exceeds the recommended voltage range or that the battery's depth of discharge is excessive, it will infer an increased probability of battery expansion. In addition to temperature and charge and discharge data, the system also monitors the mechanical compression applied to the batteries within the energy storage cabinet in real time. Battery expansion is closely related to the spatial compression within the energy storage cabinet. If the battery is compressed or confined within the cabinet, the space for battery expansion will be further compressed, resulting in greater internal stress within the battery and increasing the risk of expansion.

[0082] Detect abnormal distribution of battery chemical substances based on the battery expansion trend of the energy storage cabinet;

[0083] In an embodiment of the present invention, when monitoring the expansion trend of batteries in an energy storage cabinet, the system comprehensively analyzes data from the battery's temperature and pressure sensors, as well as current and voltage data, to monitor in real time whether the chemical distribution within the battery is abnormal. The chemical distribution of a battery directly affects its performance and safety. In particular, during the expansion process, the chemical distribution within the battery can undergo uneven changes, leading to performance degradation or potential safety hazards. The system uses temperature sensors to obtain real-time internal temperature data from the battery. During the expansion process, due to pressure fluctuations inside and outside the battery, the temperature in some areas can rise abnormally. This uneven temperature change often indicates uneven chemical reactions within the battery, particularly the level of chemical activity. Next, the system analyzes pressure changes within the battery, combining the output of the pressure sensor. Battery expansion is often accompanied by pressure changes. In particular, during expansion, internal pressure increases unevenly, with some areas experiencing higher pressure, causing a redistribution of chemical substances within the battery, thereby affecting battery performance and lifespan. In addition to temperature and pressure, the system also uses current and voltage data to monitor the battery's operating status. Battery expansion often alters the electrolyte flow path within the battery or causes uneven current distribution within the battery, resulting in abnormal voltage fluctuations, indicating problems with the distribution of chemical substances within the battery. For example, if the battery's electrolyte concentrates in a certain area during expansion, this will lead to uneven chemical reactions in that area, affecting the battery's charge and discharge efficiency and safety. The system analyzes current and voltage data, combined with temperature and pressure changes, to promptly identify abnormal distribution of battery chemical substances. Specifically, the system can monitor whether a certain part of the battery has excessive electrolyte concentration or an area of intense chemical reaction due to expansion, and based on this data, determine whether the battery has serious performance degradation or safety hazards.

[0084] Detect electrical connection faults in the energy storage cabinet based on the compression condition of the internal structure of the energy storage cabinet and the expansion trend of the energy storage cabinet battery;

[0085] In this embodiment of the present invention, the system further monitors and detects electrical connection failures within the energy storage cabinet based on the internal structural compression and battery expansion trends. Battery expansion and internal compression are common causes of electrical connection problems. In particular, when batteries expand, the battery casing is subjected to internal and external pressure, causing deformation of the internal battery structure. This deformation can affect the electrical contact between the batteries, leading to poor contact or short circuits, which in turn affects the normal operation of the batteries. The system monitors the batteries within the energy storage cabinet in real time, acquiring data on battery current and voltage fluctuations, with particular attention paid to the electrical contact points between the batteries. Battery voltage fluctuations are a key indicator of proper electrical connection. When batteries expand, their shape changes, causing the contact points between the batteries to shift or loosen, leading to poor contact or short circuits. The system collects real-time battery current and voltage data and analyzes changes in voltage fluctuations between the batteries, specifically comparing them to the standard fluctuation ranges of voltage and current between the batteries. If abnormal fluctuations occur, potential electrical connection failures can be identified. If expansion leads to poor contact or short circuits at the electrical contact points between the batteries, the battery voltage will exhibit erratic fluctuations, and the current will overload or suddenly change. The system not only detects abnormal fluctuations in battery voltage and current in real time, but also predicts the development of electrical connection failures between batteries based on battery expansion trends and battery compression. If an abnormality in the battery connection is detected, the system further analyzes the changing patterns of current and voltage between the batteries to assess whether the fault may lead to more serious electrical failures, such as unstable power supply to the battery group or overall operational failure of the energy storage cabinet, thereby issuing a timely warning signal.

[0086] The abnormal interaction status of energy storage cabinet batteries is predicted based on the electrical connection failure of the energy storage cabinet and the abnormal distribution of battery chemical substances.

[0087] In an embodiment of the present invention, based on the electrical connection failure of the energy storage cabinet and the abnormal distribution of battery chemical substances, the system will combine multiple factors to perform complex predictive analysis, thereby comprehensively evaluating the abnormal interactive influence state of the batteries inside the energy storage cabinet. When the battery is misaligned, swollen, has an electrical connection failure, or has an abnormal chemical substance distribution, the system will perform real-time monitoring based on the battery temperature, pressure, current, voltage and other data collected by the sensors in the energy storage cabinet. The misalignment of the battery leads to poor contact between the batteries, thereby affecting the electrical connection between the batteries, and further causing electrical short circuits or poor contact failures. Battery expansion is usually caused by factors such as overcharging, over-discharging or aging. The expansion process causes uneven pressure inside the battery, resulting in damage to the battery surface or poor sealing, thereby affecting the chemical reaction efficiency of the battery, and even causing leakage or uneven distribution of internal chemical substances. When expansion is combined with battery misalignment, it aggravates the expansion of cracks on the battery surface, increases friction and mutual influence between batteries, and accelerates battery failure. Battery electrical connection failures are often accompanied by unstable battery voltage and abnormal current fluctuations. These can lead to uneven current distribution across the battery group, causing overcharge or over-discharge of some cells, thereby impacting the performance and safety of the entire energy storage cabinet. Abnormal distribution of battery chemicals, especially when the battery is swollen, can lead to uneven electrolyte distribution, increasing the instability of the battery's internal chemical reactions, negatively impacting the battery's capacity, lifespan, and safety. These abnormalities not only degrade the battery's own performance but also interfere with the energy storage cabinet's battery management system (BMS), affecting the system's monitoring and regulation of battery status. By comprehensively analyzing factors such as battery misalignment, swelling, electrical connection failures, and abnormal chemical distribution, combined with battery health status, environmental changes, and system load, an algorithmic model is used to predict interactions between battery components and potential failures. Through real-time monitoring and data analysis, the system can promptly detect abnormal interactions within the energy storage cabinet's batteries and predict potential failures or safety risks.

[0088] Preferably, the energy storage cabinet battery degradation condition prediction in step S2 includes:

[0089] Perform power transmission stability attenuation analysis on the energy storage cabinet based on the abnormal interaction between the batteries in the energy storage cabinet, and obtain power transmission stability attenuation data;

[0090] In this embodiment of the present invention, an energy storage cabinet's power transmission stability degradation analysis is performed based on the abnormal interaction between the cabinet's batteries. The system collects battery status data from the energy storage cabinet, including information such as battery voltage, current, and temperature, and analyzes the abnormal interaction between the batteries based on factors such as the battery's internal chemical state, degree of expansion, and electrical connection status. This data is input into a power transmission stability model, which assesses power transmission stability degradation based on the battery health, the energy storage cabinet's operating environment, and load conditions. These analysis results generate power transmission stability degradation data, which is used to further determine the health of the energy storage cabinet's battery system.

[0091] Detect the frequency fluctuation characteristics of energy storage cabinet power based on power transmission stability attenuation data;

[0092] In an embodiment of the present invention, based on the power transmission stability attenuation data, the system proceeds to detect the frequency fluctuation characteristics of the energy storage cabinet. The system collects the energy storage cabinet's power output data, particularly the voltage and current signals, and analyzes the frequency fluctuations that occur during power transmission. Frequency domain analysis methods such as Fourier transforms are used to extract the frequency fluctuation characteristics of power transmission. Changes in frequency fluctuation characteristics are often an indicator of battery performance degradation. In particular, when abnormal battery interaction, poor local electrical contact, or uneven chemical reactions occur, the power transmission frequency often exhibits significant fluctuations. Through this process, the system can identify early signs of reduced stability in the power transmission system.

[0093] Predict the growth of transmission power overcurrent risk based on the power frequency fluctuation characteristics of the energy storage cabinet;

[0094] In this embodiment of the present invention, after detecting the characteristics of power frequency fluctuations, the system uses these characteristics to predict the increasing risk of overcurrent in transmitted power. Overcurrent risk is often associated with battery loss, decreased charge and discharge efficiency, and internal electrical interference within the battery. By analyzing the relationship between power frequency fluctuations and current anomalies, the system assesses the probability of current exceeding the specified limit and, therefore, predicts overcurrent conditions that may occur in energy storage cabinets during normal operation. Combining historical and real-time data, the system establishes a model linking current fluctuations and overcurrent risk, accurately predicting overcurrent risks during power transmission.

[0095] Evaluate the electrical interference of the energy storage cabinet batteries based on the growth risk of transmitted power overcurrent;

[0096] In an embodiment of the present invention, the electrical interference of energy storage cabinet batteries is assessed based on the increasing risk of overcurrent during power transmission. During power transmission, batteries can generate electrical interference due to factors such as poor electrical contact, contact point oxidation, transmission line damage, or damage to electrical components. This interference typically manifests as abnormal fluctuations in the battery's internal current or voltage, and can even lead to battery overheating, capacity reduction, increased internal resistance, or performance degradation. By monitoring the battery's current and voltage changes in real time and combining them with the battery's operating status (such as charge and discharge conditions and temperature fluctuations), the system can promptly detect whether the battery is experiencing electrical interference. Abnormal fluctuations in the battery's current and voltage are flagged as electrical interference events. By performing a detailed analysis of the fluctuations in battery voltage and current, the system can assess whether electrical interference exists within the battery. Electrical interference sources typically cause irregular fluctuations in the battery's voltage or current. For example, problems such as poor electrical contact or loose wiring can cause sudden increases or decreases in current. These erratic fluctuations typically manifest as significant fluctuations in the battery's current and voltage curves. The system analyzes the fluctuation amplitude, frequency, and trend of the battery's voltage and current. If the frequency of current and voltage fluctuations is too high and the amplitude is abnormal, the system will infer that there is poor electrical contact or a problem with the electrical components. Battery electrical interference is often accompanied by temperature anomalies, especially near poor contacts inside the battery or damaged electrical components, which can cause local overheating when current passes through. The system combines the battery's temperature sensor data to further confirm the location of the source of electrical interference and analyze whether the local overheating is caused by abnormal current. Through multi-point monitoring data, the system can identify whether the battery has overheated and determine the location of the source of electrical interference through positioning technology. Through comprehensive analysis of battery voltage, current, and temperature, the system can accurately identify and locate the source of electrical interference and assess the impact of electrical interference on the battery.

[0097] Estimate the extent of energy storage cabinet battery loss based on the electrical interference of the energy storage cabinet batteries and the increase in the risk of transmitted power overcurrent;

[0098] In an embodiment of the present invention, the extent of energy storage cabinet battery wear is estimated based on the electrical interference of the cabinet batteries and the increasing risk of overcurrent in the transmitted power. The system continuously monitors key electrical parameters such as battery voltage, current, and temperature, and combines historical data such as battery cycle count and charge / discharge depth to perform a comprehensive analysis to predict battery wear. Electrical interference, particularly electrical noise caused by factors such as current fluctuations, voltage instability, and poor contact, affects the electrochemical reactions within the battery, thereby accelerating battery aging. The system monitors the battery voltage and current in real time, analyzes these fluctuations, identifies the presence of electrical interference, and assesses its potential threat to battery performance. Furthermore, current overload or overcurrent can negatively impact the battery. Long-term overcurrent conditions not only cause the battery's internal temperature to rise, but also accelerate chemical reactions within the battery, further exacerbating wear. The system monitors the risk of overcurrent in the transmitted power and, combined with the battery's electrical response, analyzes whether the battery is experiencing overcurrent and calculates the impact of overcurrent on battery wear. By comparing this with historical battery data, the system assesses the extent of battery wear under specific operating conditions. The number of cycles is a key indicator of battery wear. Each charge-discharge cycle affects the battery's chemical properties. As the number of cycles increases, the battery's capacity gradually decreases, its internal resistance increases, and its charge-discharge efficiency decreases. Furthermore, the depth of charge and discharge also affects the extent of battery wear. Deeper charge and discharge cycles lead to faster battery degradation. The system compares real-time monitoring data with historical data to analyze battery wear trends under current usage conditions.

[0099] Detect the degree of degradation of the energy storage cabinet's charging and discharging efficiency based on the degree of battery loss in the energy storage cabinet;

[0100] In an embodiment of the present invention, the degree of charge and discharge efficiency degradation of an energy storage cabinet is detected based on the degree of battery wear. The system continuously monitors the changes in battery current and voltage during the charge and discharge processes, and analyzes the battery's efficiency degradation in real time based on the battery's discharge capacity and charge level. Charge and discharge efficiency is a key indicator of battery performance, reflecting the efficiency level achieved during energy conversion. Over time, chemical reactions within the battery gradually cause wear, increasing the battery's internal resistance and decreasing its power output capacity, thereby affecting its charge and discharge efficiency. The system collects real-time battery current and voltage data, combines the battery's charge and discharge capacity, and calculates the charge and discharge efficiency. It also analyzes the battery's operating environment, taking into account the impact of factors such as ambient temperature and humidity on charge and discharge efficiency. As battery wear increases, the battery's charge and discharge efficiency gradually decreases, a process typically accompanied by a decrease in battery capacity and an increase in internal resistance. By combining battery charge and discharge data with the degree of battery wear, the system can more accurately assess the battery's health status. When the battery wear is high, the internal resistance of the battery increases, which leads to energy waste during charging and inability to effectively release electrical energy during discharging, resulting in a significant decrease in charging and discharging efficiency.

[0101] The battery degradation of the energy storage cabinet is predicted based on the degradation degree of the energy storage cabinet's charging and discharging efficiency and the degree of battery loss in the energy storage cabinet.

[0102] In embodiments of the present invention, the overall health of batteries is accurately assessed by analyzing multiple data points. The system monitors the charge and discharge efficiency of the energy storage cabinet's batteries to determine the battery's energy conversion efficiency during the charge and discharge process. As batteries age, the chemical reactions within the battery gradually become less efficient, leading to a decrease in charge and discharge efficiency. The system collects real-time data such as the battery's charge current, voltage, and capacity, and combines these data with factors such as the battery's charge and discharge cycles, ambient operating temperature, and depth of discharge to assess the degradation of the battery's charge and discharge efficiency. Battery charge and discharge efficiency is a key indicator of battery health. When the charge and discharge efficiency falls significantly below a predetermined standard, it indicates signs of performance degradation. The system then uses this data to predict the battery's health. In addition to charge and discharge efficiency, the system further assesses the degradation of the energy storage cabinet's batteries by measuring the degree of battery wear. Battery wear typically manifests as a decrease in capacity and an increase in internal resistance, both of which directly impact the battery's power output capability and charge and discharge efficiency. The system collects data such as the battery's voltage, current, temperature, and depth of discharge during use, combining it with the battery's cycle life and historical operating data to assess the degree of battery wear. If battery wear is excessive, the output power will gradually decrease, leading to complete battery failure. By monitoring these parameters, the system can accurately determine the extent of battery wear and predict the battery's performance degradation trend over time. Factors such as electrical interference and overcurrent risk are taken into account for a more comprehensive analysis. During use, batteries are subject to electrical interference and overcurrent. This can be especially problematic when the energy storage cabinet's load fluctuates significantly. Faults or poor contact can occur in the battery's electrical interfaces, further degrading battery performance. By monitoring the battery's voltage and current signals, the system can promptly detect electrical interference or overcurrent events and analyze their potential impact on battery health. For example, overcurrent can cause significant internal heat generation in the battery, accelerating the aging process. The system combines these electrical interference and overcurrent risk analyses to further quantify battery degradation.

[0103] Preferably, step S3 includes the following steps:

[0104] Step S31: estimating the energy storage cabinet battery balancing failure status based on the energy storage cabinet battery degradation status;

[0105] In an embodiment of the present invention, the energy storage cabinet battery balancing failure is estimated based on the energy storage cabinet battery degradation status. During a long period of charge and discharge cycle, the battery inside the energy storage cabinet will experience balancing failure due to the gradual imbalance of parameters such as voltage, current, internal resistance, etc. of each single cell. Balancing failure is usually manifested as a large deviation in the charging state of certain battery cells, resulting in reduced performance of the entire battery pack. During the implementation process, the system analyzes the performance change trend of each battery cell by monitoring the voltage, current, temperature and other parameters of each battery cell in the energy storage cabinet in real time, as well as the battery charging and discharging history. When the system detects that the charging or discharging state of a certain single cell deviates from the predetermined standard, the system will estimate whether there is a risk of balancing failure in the energy storage cabinet battery pack. By calculating the voltage difference of each battery cell and combining the battery degradation status, the system will predict the balancing failure of the battery pack and evaluate the degree and scope of the failure through the model.

[0106] Step S32: Evaluate the health of the energy storage cabinet batteries based on the energy storage cabinet battery balancing failure and energy storage cabinet battery degradation;

[0107] In an embodiment of the present invention, the health of the energy storage cabinet battery is evaluated based on the energy storage cabinet battery equalization failure and the energy storage cabinet battery degradation. Battery health is an important indicator for evaluating the overall performance and remaining service life of the battery pack, and is usually evaluated through changes in battery parameters such as voltage, internal resistance, and capacity. When the energy storage cabinet battery fails to equalize, it usually leads to a decrease in battery health. In implementation, the system analyzes the voltage changes, internal resistance changes, and capacity degradation of each battery cell in combination with the degradation of the energy storage cabinet battery. The system also determines whether the energy storage cabinet battery is over-discharged or over-charged by comparing the voltage differences between different battery cells. By statistically analyzing the health data of all battery cells, the system comprehensively evaluates the overall health of the energy storage cabinet battery. The evaluation process is also dynamically adjusted in combination with the battery's operating environment (such as temperature, humidity) and operating history (such as charging frequency, discharge depth, etc.), and outputs a value representing the battery health.

[0108] Step S33: detecting the battery output power fluctuation according to the battery health of the energy storage cabinet, and detecting the imbalance of the power supply of the energy storage cabinet based on the battery output power fluctuation;

[0109] In an embodiment of the present invention, the battery output power fluctuation is detected according to the health of the energy storage cabinet battery, and the imbalance state of the energy storage cabinet power supply is detected based on the battery output power fluctuation. Battery output power fluctuations usually reflect changes in the health of the battery pack. When the battery health decreases, the battery output power fluctuates, and even a stable power supply cannot be maintained. The system analyzes whether the battery output power is stable by monitoring the output power changes of the energy storage cabinet battery in real time. In a specific implementation, the system collects the output data of the energy storage cabinet battery through current, voltage and power sensors, and evaluates the fluctuation of the output power in combination with the battery health. If the fluctuation of the output power exceeds a preset threshold, the system will mark the energy storage cabinet as being in a power supply imbalance state. This imbalance state indicates that the battery power cannot meet the load demand, or that the battery has abnormal loss during operation.

[0110] Step S34: evaluating the degree of dynamic load overload of the transmission line according to the power supply imbalance state of the energy storage cabinet;

[0111] In an embodiment of the present invention, the degree of dynamic overload on the transmission line is assessed based on the imbalanced power supply of the energy storage cabinet. Fluctuations in battery output power directly affect the power supply of the energy storage cabinet, and imbalanced power supply conditions lead to overload on the power transmission line. The system analyzes the imbalanced power supply of the energy storage cabinet and, in combination with the current, voltage, and load data of the transmission line, assesses the dynamic load of the transmission line. When the power output of the energy storage cabinet is unstable, the system models the load variations of the power transmission line and, based on the line's rated load, load type, and transformer capacity, predicts whether the load exceeds a safety threshold. If the load is too high, the system promptly identifies the potential overload risk and issues an alarm.

[0112] Step S35: predicting the power transmission line loss growth trend according to the dynamic load overload degree of the transmission line.

[0113] In an embodiment of the present invention, the loss growth trend of an electric energy transmission line is predicted based on the degree of dynamic overload of the transmission line. When an electric energy transmission line is in an overloaded operating state, its resistance, voltage drop, temperature and other parameters will undergo abnormal changes. Long-term overload operation will cause the line loss to gradually increase. During implementation, the system uses the current and voltage data of the transmission line, combined with the material properties of the line and temperature sensor data, to establish a loss model under dynamic load overload conditions. Based on the load conditions of the transmission line and the electric energy loss prediction model, the system evaluates the line loss growth trend over a certain period of time. If the line load continues to be overloaded, the system predicts that the line loss will increase over time, leading to line failure or reduced efficiency.

[0114] It is particularly important that step S35 includes the following steps:

[0115] Step S351: predicting the growth trend of the magnetic field around the transmission line according to the dynamic load overload degree of the transmission line;

[0116] In the embodiment of the present invention, when the load on the transmission line increases, the current value in the line will increase. According to the Biot-Savart law and Ampere's circuit law, the magnetic field strength around the line is proportional to the current. To obtain an accurate trend of magnetic field changes, a flux meter, a Hall sensor or a magnetic field detector is used to measure the magnetic field at different positions around the transmission line in real time, and record the changes in the magnetic induction intensity B. At the same time, combined with the rated current value of the transmission line, the instantaneous current changes and the line load curve, the magnetic field growth trend data is obtained by using an integral calculation method, and a time series prediction model is established to calculate the future growth trend of the magnetic field to obtain the magnetic field growth trend data.

[0117] Step S352: estimating proximity effect characteristics based on the growth trend of the magnetic field around the transmission line;

[0118] In an embodiment of the present invention, the proximity effect is caused by the redistribution of current in adjacent conductors due to changes in the magnetic field, resulting in additional current loss. The proximity effect is accurately estimated, and the induced electromotive force between adjacent lines is calculated using the acquired magnetic field growth trend data. According to Maxwell's equations and the law of induction, the change in mutual inductance between the lines is analyzed. The induced current of the transmission line under different current load conditions is detected by a high-frequency current sensor, and the intensity and phase change of the induced current are measured to evaluate the characteristics of the proximity effect. At the same time, an electromagnetic field simulation model is established using finite element electromagnetic simulation software (such as ANSYS Maxwell or COMSOL) to simulate the influence of the proximity effect under different load conditions, and the proximity effect characteristic parameters are extracted to form visual proximity effect characteristic data.

[0119] Step S353: Detecting the attenuation of the conductive cross-sectional area of the transmission line based on the proximity effect characteristics and the growth trend of the magnetic field around the transmission line;

[0120] In an embodiment of the present invention, the proximity effect causes the current within a conductor to concentrate on the side closest to the adjacent conductor, reducing the effective conductive cross-sectional area and increasing the conductor's equivalent resistance. To detect the attenuation of the conductive cross-sectional area, an AC impedance analyzer is used to measure the resistance of the circuit under different frequency and load conditions. Combined with the aforementioned proximity effect characteristic data, the effective change in the conductive cross-sectional area is calculated through analytical calculation. Furthermore, an infrared thermal imager is used to measure the surface temperature distribution of the conductor, analyzing the heating area caused by the current concentration effect. This further verifies the attenuation of the conductive cross-sectional area and obtains conductive cross-sectional area attenuation trend data.

[0121] Step S354: analyzing the increasing trend of the equivalent resistance according to the attenuation of the conductive cross-sectional area of the circuit;

[0122] In the embodiment of the present invention, the increase in equivalent resistance is mainly affected by the attenuation of the conductive cross-sectional area, and is also affected by factors such as temperature change and material aging. The increase trend of the equivalent resistance is accurately analyzed, and based on the conductive cross-sectional area attenuation trend data and combined with the line resistance temperature coefficient, an equivalent resistance change calculation model is established. The four-terminal method is used to measure the dynamic resistance change of the line, record the resistance value under different load conditions, and analyze the change trend of the resistance over time. At the same time, a large current pulse test method is used to simulate the resistance change of the line under extreme load conditions. Combined with the historical operation data of the line, an equivalent resistance increase trend prediction model is established, and the equivalent resistance increase trend data is output.

[0123] Step S355: predicting the growth trend of power transmission line loss based on the equivalent resistance increase trend and the line conductive cross-sectional area attenuation.

[0124] In the embodiment of the present invention, the power loss is mainly caused by the equivalent resistance of the line, and the loss power is calculated by P=I 2 R is calculated. Accurately predict the loss growth trend of power transmission lines. Based on equivalent resistance increase trend data and combined with line current load change records, the loss changes of the line in different time periods are calculated. Thermal simulation analysis software is also used to simulate the temperature rise of the line, assess the additional losses caused by conductor heating, and combine the thermal-electric coupling model to calculate the line loss growth trend. The calculated loss growth trend data is then used.

[0125] Preferably, step S4 includes the following steps:

[0126] Step S41: estimating the thermal effect accumulation of the energy storage cabinet based on the growth trend of power transmission line loss and the degree of dynamic load overload of the transmission line;

[0127] In an embodiment of the present invention, the accumulated thermal effects of an energy storage cabinet are estimated based on the growth trend of power transmission line losses and the degree of dynamic overload on the transmission line. The growth trend of power transmission line losses and the degree of overload directly impact the thermal effects of the energy storage cabinet. High loads and high losses increase the operating temperature of the energy storage cabinet, which in turn affects the performance and service life of the batteries. During implementation, the system calculates the power loss of the line using transmission line parameters such as current and voltage. In conjunction with the dynamic changes in overload, the system uses heat conduction models (such as heat transfer equations) to evaluate the thermal effects caused by line losses and further infer changes in the temperature of the energy storage cabinet. By monitoring temperature sensor data within the energy storage cabinet (such as battery temperature and cabinet casing temperature), the accumulated thermal effects within the cabinet are estimated in real time. If transmission line losses continue to increase or the load continues to be overloaded for an extended period, the system predicts the degree of heat accumulation in the energy storage cabinet and provides early warnings of any temperature anomalies.

[0128] Step S42: detecting abnormal stress in the energy storage cabinet based on the accumulation of thermal effects in the energy storage cabinet;

[0129] In an embodiment of the present invention, abnormal stress conditions in the energy storage cabinet are detected based on the accumulation of thermal effects in the energy storage cabinet. During the long-term use of the energy storage cabinet, the accumulation of thermal effects causes thermal expansion of the materials inside the energy storage cabinet, thereby generating mechanical stress. If the temperature inside the energy storage cabinet is too high, the thermal expansion of components such as batteries and support structures will cause stress concentration, resulting in local damage or deformation. In implementation, the system combines the real-time data of the temperature sensor, stress sensor and deformation sensor in the energy storage cabinet to monitor the stress state inside the energy storage cabinet. By analyzing the stress distribution of each component of the energy storage cabinet, the system can detect abnormal stress conditions caused by the accumulation of thermal effects. If the stress inside the energy storage cabinet exceeds the preset safety threshold, the system will automatically mark it as an abnormal stress state.

[0130] Step S43: predicting the degradation trend of the energy storage cabinet based on the abnormal stress condition in the energy storage cabinet and the accumulation of thermal effects in the energy storage cabinet;

[0131] In an embodiment of the present invention, the degradation trend of the energy storage cabinet is predicted based on the abnormal stress conditions in the energy storage cabinet and the accumulation of thermal effects in the energy storage cabinet. The accumulation of abnormal stress and thermal effects usually accelerates the degradation process of the energy storage cabinet components, especially the life and safety of the battery. During implementation, the system uses the stress distribution data and temperature change data inside the energy storage cabinet, combined with the chemical reaction model of the battery, to predict the degradation trend of the energy storage cabinet components. Specifically, the system models the stress and temperature data of the energy storage cabinet through time series analysis, simulates the degradation process of the energy storage cabinet under different working conditions, and predicts the performance degradation rate caused by thermal effects and abnormal stress. If the temperature in the energy storage cabinet continues to rise or the stress is uneven for a long time, the system predicts that the battery performance of the energy storage cabinet will decline rapidly, the discharge efficiency of the battery will decline, and the capacity of the battery will decrease.

[0132] Step S44: Based on the abnormal stress condition in the energy storage cabinet and the degradation trend of the energy storage cabinet, the energy storage cabinet performance aging is estimated to obtain energy storage cabinet performance aging data;

[0133] In an embodiment of the present invention, the performance aging of the energy storage cabinet is estimated based on the stress abnormality in the energy storage cabinet and the degradation trend of the energy storage cabinet, and the performance aging data of the energy storage cabinet is obtained. The performance aging of the energy storage cabinet is closely related to the stress abnormality and degradation trend. After a battery is used for a long time, its charge and discharge efficiency, cycle life and battery capacity will be affected due to the accumulation of thermal effects and mechanical stress. During the implementation process, the system combines the degradation trend and stress abnormality of the energy storage cabinet, and adopts an aging prediction method based on a physical model (such as the Arrhenius model or the battery degradation model) to calculate the performance aging of the energy storage cabinet. By simulating the charge and discharge cycle of the battery and the temperature changes in the energy storage cabinet, the system obtains the performance aging data of the energy storage cabinet in the future.

[0134] Step S45: Estimate the service life of the energy storage cabinet based on the energy storage cabinet performance aging data and the energy storage cabinet degradation trend to obtain energy storage cabinet service life data.

[0135] In an embodiment of the present invention, the energy storage cabinet service life is estimated based on energy storage cabinet performance aging data and degradation trends, generating energy storage cabinet service life data. The energy storage cabinet service life is an assessment result that comprehensively considers multiple factors, including performance degradation, aging, and stress anomalies. The system estimates the energy storage cabinet service life by analyzing the energy storage cabinet performance aging data and degradation trends, combined with factors such as the battery's charge and discharge cycles, temperature, and stress. Service life prediction methods are typically based on linear regression models or accelerated life testing models, which calculate the remaining service life of the energy storage cabinet by analyzing historical data, temperature, and stress. The system updates these parameters in real time, continuously evaluating the remaining service life of the energy storage cabinet as the energy storage cabinet is used. If the energy storage cabinet's performance aging reaches a set threshold, the system will issue a warning, indicating that the energy storage cabinet is about to enter a state requiring maintenance or replacement, and the system will output the energy storage cabinet service life data.

[0136] It is particularly important that step S43 includes the following steps:

[0137] Step S431: estimating the failure of thermal management of the energy storage cabinet based on the abnormal stress condition in the energy storage cabinet and the accumulation of thermal effects in the energy storage cabinet;

[0138] In an embodiment of the present invention, the thermal management failure of the energy storage cabinet is estimated based on the abnormal stress conditions within the energy storage cabinet and the accumulation of thermal effects within the energy storage cabinet. Abnormal stress within the energy storage cabinet is mainly caused by changes in the temperature gradient, and the accumulation of thermal effects leads to uneven internal temperature distribution, which in turn affects the heat dissipation efficiency of the thermal management system. Temperature data within the energy storage cabinet is collected using a temperature sensor, and the heat distribution is obtained in combination with a thermal imaging device. The thermal conduction model under normal operating conditions is compared to analyze temperature anomalies and heat loss. Furthermore, a pressure sensor is used to measure the mechanical stress within the energy storage cabinet to determine whether the stress concentration area will affect the stability of the heat dissipation components. These data are input into the thermal management simulation system to calculate the heat exchange efficiency and heat dissipation capacity of the energy storage cabinet, and obtain data on the thermal management failure of the energy storage cabinet.

[0139] Step S432: analyzing the stability attenuation of the energy storage cabinet based on the thermal management failure of the energy storage cabinet;

[0140] In an embodiment of the present invention, the stability attenuation of the energy storage cabinet is analyzed based on the failure of the thermal management of the energy storage cabinet. Failure of the thermal management of the energy storage cabinet will cause local overheating, thereby affecting the stability of the battery module, connecting components and the overall structure. An infrared thermal imager is used to detect the surface temperature distribution of the battery, and combined with the data of the battery management system (BMS), the abnormal temperature fluctuation of the battery is analyzed. The operating status of the cooling fan and the electric cooling system of the energy storage cabinet is monitored, and the overall stability decline trend is calculated in combination with the temperature change rate and the distribution of overheating areas. The degree of mechanical deformation caused by thermal expansion is detected by a vibration sensor, and the degree of material fatigue is calculated in combination with the thermal expansion and contraction coefficients. The stability attenuation data of the energy storage cabinet is further analyzed to see whether the structure of the energy storage cabinet has experienced stability attenuation due to thermal runaway, and the stability attenuation data of the energy storage cabinet is obtained.

[0141] Step S433: Detecting a power grid fault condition of the energy storage cabinet according to the stability attenuation condition of the energy storage cabinet;

[0142] In an embodiment of the present invention, the energy storage cabinet grid fault is detected based on the stability degradation of the energy storage cabinet. The stability degradation of the energy storage cabinet leads to problems such as loose electrical connections, power output fluctuations, and short circuits, thereby affecting the interaction between the energy storage cabinet and the grid. High-precision voltage and current sensors are used to monitor the output power parameters of the energy storage cabinet in real time, analyze the voltage fluctuation rate and current harmonic content, and calculate the quality of the power output of the energy storage cabinet in combination with the power factor. The energy storage cabinet power output spectrum is analyzed by short-time Fourier transform (STFT) to detect the presence of abnormal harmonics, thereby determining whether the energy storage cabinet causes interference to the grid. In addition, a relay protection device is used to detect overcurrent and short circuit conditions at the connection point between the energy storage cabinet and the grid, and obtain energy storage cabinet grid fault condition data.

[0143] Step S434: estimating the aging degree of the energy storage cabinet material according to the abnormal stress condition in the energy storage cabinet;

[0144] In an embodiment of the present invention, the aging degree of the energy storage cabinet material is estimated based on the abnormal stress conditions within the energy storage cabinet. During the long-term operation of the energy storage cabinet, the internal materials will age due to the influence of temperature cycles, mechanical stress and current shocks. The ambient temperature, operating current and mechanical stress data are collected by sensors inside the energy storage cabinet, and the life attenuation trend of key components is analyzed in combination with the fatigue life curve of the metal material. Secondly, X-ray tomography (CT) is used to detect the micro crack propagation of the energy storage cabinet shell and internal connecting parts, and the fatigue strength of the stress-bearing area is calculated in combination with finite element analysis (FEA) to evaluate the aging degree of the material. Finally, the electrical properties of the insulating material are measured through a dielectric loss test to determine whether the insulating layer has a leakage risk due to aging, and to obtain the aging degree data of the energy storage cabinet material.

[0145] Step S435: predicting the degradation trend of the energy storage cabinet according to the aging degree of the energy storage cabinet material and the fault condition of the energy storage cabinet power grid.

[0146] In an embodiment of the present invention, the degradation trend of the energy storage cabinet is predicted based on the degree of aging of the energy storage cabinet material and the energy storage cabinet grid fault conditions. Aging of the energy storage cabinet material reduces its mechanical strength, while grid faults result in additional electrical shocks. The combined effect of these two factors accelerates the overall degradation process of the energy storage cabinet. By comprehensively analyzing the energy storage cabinet material aging data, grid fault data, and historical operating conditions, a time series analysis model is constructed to calculate the degradation rate. Furthermore, using the accelerated life test (ALT) method, accelerated temperature and electrical stress experiments are conducted to infer the degradation rate curve of the energy storage cabinet under normal operating conditions. Combined with existing operating data, the degradation trend of the future operating stage is extrapolated to obtain energy storage cabinet degradation trend data.

[0147] The present invention further provides a visualization system for an energy storage cabinet, which is used to execute the visualization method for an energy storage cabinet as described above. The visualization system for an energy storage cabinet includes:

[0148] A visualization state determination module is used to obtain energy storage cabinet data; collect energy storage cabinet image data based on the energy storage cabinet data; and determine the energy storage cabinet operation visualization state based on the energy storage cabinet image data;

[0149] The battery degradation prediction module is used to detect the stability gradient attenuation trend of the energy storage cabinet based on the visual operation status of the energy storage cabinet; predict the abnormal interaction influence status of the energy storage cabinet batteries based on the stability gradient attenuation trend of the energy storage cabinet; and predict the battery degradation status of the energy storage cabinet based on the abnormal interaction influence status of the energy storage cabinet batteries;

[0150] The line loss growth trend prediction module is used to estimate and evaluate the battery health of the energy storage cabinet based on the battery degradation status of the energy storage cabinet; detect the imbalance of the energy storage cabinet power supply based on the energy storage cabinet battery health; evaluate the dynamic load overload level of the transmission line based on the imbalance of the energy storage cabinet power supply; and predict the growth trend of power transmission line loss based on the dynamic load overload level of the transmission line;

[0151] The energy storage cabinet service life estimation module is used to estimate the accumulated thermal effects of the energy storage cabinet based on the growth trend of power transmission line losses and the degree of dynamic load overload of the transmission line; predict the degradation trend of the energy storage cabinet based on the accumulated thermal effects of the energy storage cabinet; and estimate the service life of the energy storage cabinet based on the degradation trend of the energy storage cabinet to obtain the service life data of the energy storage cabinet.

[0152] A computer-readable storage medium stores a computer program, wherein the computer program is used to execute the visualization method for an energy storage cabinet.

[0153] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A visualization method for an energy storage cabinet, characterized in that: The following steps are involved: Step S1: acquiring energy storage cabinet data; collecting energy storage cabinet image data based on the energy storage cabinet data; and determining the energy storage cabinet operation visualization state based on the energy storage cabinet image data. Step S2: Detecting the energy storage cabinet stability gradient attenuation trend based on the energy storage cabinet operation visualization status; predicting the energy storage cabinet battery abnormal interaction impact status based on the energy storage cabinet stability gradient attenuation trend; and predicting the energy storage cabinet battery degradation status based on the energy storage cabinet battery abnormal interaction impact status. Step S3: Evaluate the battery health of the energy storage cabinet based on the estimated degradation of the battery in the energy storage cabinet; detect the imbalance of the power supply of the energy storage cabinet based on the battery health of the energy storage cabinet; evaluate the dynamic load overload degree of the transmission line based on the imbalance of the power supply of the energy storage cabinet; and predict the growth trend of power transmission line loss based on the dynamic load overload degree of the transmission line. Step S4: estimating the accumulated thermal effect of the energy storage cabinet based on the growth trend of the power transmission line loss and the degree of dynamic load overload of the transmission line; and predicting the degradation trend of the energy storage cabinet based on the accumulated thermal effect of the energy storage cabinet; The service life of the energy storage cabinet is estimated based on the degradation trend of the energy storage cabinet to obtain the service life data of the energy storage cabinet.

2. The visualization method for energy storage cabinet according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: setting the visible light camera resolution to 1920×1080 pixels, the minimum light sensitivity to 0.01 lux, and the frame rate to 30 FPS; Step S12: setting the temperature measurement range of the infrared imager to -20°C to 300°C, the minimum temperature resolution to 0.05°C, and the image frame rate to 25Hz; Step S13: Acquire energy storage cabinet data; Step S14: using a visible light camera to collect energy storage cabinet image data; Step S15: Determine the visual operating status of the energy storage cabinet using the infrared imager and the energy storage cabinet image data.

3. The visualization method for energy storage cabinet according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: performing energy storage cabinet image preprocessing on the energy storage cabinet image data to obtain energy storage cabinet image preprocessing data; Step S152: collecting three-dimensional point cloud features of the energy storage cabinet image based on the energy storage cabinet image preprocessing data; Step S153: extracting surface features of the energy storage cabinet image based on the three-dimensional point cloud features of the energy storage cabinet image; Step S154: detecting the structural condition of the energy storage cabinet based on the surface features of the energy storage cabinet image; Step S155: using an infrared imager to collect the hot spot distribution of the energy storage cabinet; Step S156: Calculating the energy storage cabinet hotspot distribution balance according to the energy storage cabinet hotspot distribution; Step S157: Determine the energy storage cabinet operation visualization state according to the energy storage cabinet hot spot distribution balance and the energy storage cabinet structure condition.

4. The visualization method for energy storage cabinet according to claim 1, characterized in that: The energy storage cabinet stability gradient attenuation trend detection in step S2 includes: Collect structural defects of energy storage cabinets based on the visual operation status of the energy storage cabinets; Using the structural defects of the energy storage cabinet to identify the degree of structural deformation of the energy storage cabinet; When the deformation degree of the energy storage cabinet structure exceeds 0.5% and the energy storage cabinet structure defects are present, calculate the abnormal growth trend of the local mechanical stress of the energy storage cabinet; Detect the degree of surface cracks in the energy storage cabinet based on the increase trend of local mechanical stress in the energy storage cabinet exceeding 50MPa and the degree of structural deformation of the energy storage cabinet; When the crack depth on the energy storage cabinet surface exceeds 2 cm, the damage to the cabinet support structure is assessed based on the growth trend of the local mechanical stress in the cabinet. The gradient attenuation trend of the energy storage cabinet stability is detected based on the damage of the energy storage cabinet supporting structure.

5. The visualization method for energy storage cabinet according to claim 1, characterized in that: The prediction of abnormal interaction impact status of the energy storage cabinet batteries in step S2 includes: Calculate the probability of the energy storage cabinet structure tilting according to the stability gradient attenuation trend of the energy storage cabinet; Estimate the displacement degree of internal components of the energy storage cabinet based on the tilt probability of the energy storage cabinet structure; The displacement degree of internal components of the energy storage cabinet and the probability of structural tilt of the energy storage cabinet are used to predict the misalignment trend of batteries inside the energy storage cabinet. The internal structural compression of the energy storage cabinet is calculated using the battery misalignment trend and the displacement degree of the internal components of the energy storage cabinet; Estimate the expansion trend of the energy storage cabinet battery based on the compression condition of the internal structure of the energy storage cabinet; Detect abnormal distribution of battery chemical substances based on the battery expansion trend of the energy storage cabinet; Detect electrical connection faults in the energy storage cabinet based on the compression condition of the internal structure of the energy storage cabinet and the expansion trend of the energy storage cabinet battery; The abnormal interaction status of energy storage cabinet batteries is predicted based on the electrical connection failure of the energy storage cabinet and the abnormal distribution of battery chemical substances.

6. The visualization method for energy storage cabinet according to claim 1, characterized in that: The energy storage cabinet battery degradation prediction in step S2 includes: Perform power transmission stability attenuation analysis on the energy storage cabinet based on the abnormal interaction between the batteries in the energy storage cabinet, and obtain power transmission stability attenuation data; Detect the frequency fluctuation characteristics of energy storage cabinet power based on power transmission stability attenuation data; Predict the growth of transmission power overcurrent risk based on the power frequency fluctuation characteristics of the energy storage cabinet; Evaluate the electrical interference of the energy storage cabinet batteries based on the growth risk of transmitted power overcurrent; Estimate the extent of energy storage cabinet battery loss based on the electrical interference of the energy storage cabinet batteries and the increase in the risk of transmitted power overcurrent; Detect the degree of degradation of the energy storage cabinet's charging and discharging efficiency based on the degree of battery loss in the energy storage cabinet; The battery degradation of the energy storage cabinet is predicted based on the degradation degree of the energy storage cabinet's charging and discharging efficiency and the degree of battery loss in the energy storage cabinet.

7. The visualization method for energy storage cabinet according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: estimating the energy storage cabinet battery balancing failure status based on the energy storage cabinet battery degradation status; Step S32: Evaluate the health of the energy storage cabinet batteries based on the energy storage cabinet battery balancing failure and energy storage cabinet battery degradation; Step S33: detecting the battery output power fluctuation according to the battery health of the energy storage cabinet, and detecting the imbalance of the power supply of the energy storage cabinet based on the battery output power fluctuation; Step S34: evaluating the degree of dynamic load overload of the transmission line according to the power supply imbalance state of the energy storage cabinet; Step S35: predicting the power transmission line loss growth trend according to the dynamic load overload degree of the transmission line.

8. The visualization method for energy storage cabinet according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: estimating the thermal effect accumulation of the energy storage cabinet based on the growth trend of power transmission line loss and the degree of dynamic load overload of the transmission line; Step S42: detecting abnormal stress in the energy storage cabinet based on the accumulation of thermal effects in the energy storage cabinet; Step S43: predicting the degradation trend of the energy storage cabinet based on the abnormal stress condition in the energy storage cabinet and the accumulation of thermal effects in the energy storage cabinet; Step S44: Based on the abnormal stress condition in the energy storage cabinet and the degradation trend of the energy storage cabinet, the energy storage cabinet performance aging is estimated to obtain energy storage cabinet performance aging data; Step S45: Estimate the service life of the energy storage cabinet based on the energy storage cabinet performance aging data and the energy storage cabinet degradation trend to obtain energy storage cabinet service life data.

9. A visualization system for energy storage cabinets, characterized in that: For executing the visualization method for an energy storage cabinet according to claim 1, the visualization system for an energy storage cabinet comprises: A visualization state determination module is used to obtain energy storage cabinet data; collect energy storage cabinet image data based on the energy storage cabinet data; and determine the energy storage cabinet operation visualization state based on the energy storage cabinet image data; The battery degradation prediction module is used to detect the stability gradient attenuation trend of the energy storage cabinet based on the visual operation status of the energy storage cabinet; predict the abnormal interaction influence status of the energy storage cabinet batteries based on the stability gradient attenuation trend of the energy storage cabinet; and predict the battery degradation status of the energy storage cabinet based on the abnormal interaction influence status of the energy storage cabinet batteries; The line loss growth trend prediction module is used to estimate and evaluate the battery health of the energy storage cabinet based on the battery degradation status of the energy storage cabinet; detect the imbalance of the energy storage cabinet power supply based on the energy storage cabinet battery health; evaluate the dynamic load overload level of the transmission line based on the imbalance of the energy storage cabinet power supply; and predict the growth trend of power transmission line loss based on the dynamic load overload level of the transmission line; The energy storage cabinet service life estimation module is used to estimate the accumulated thermal effects of the energy storage cabinet based on the growth trend of power transmission line losses and the degree of dynamic load overload of the transmission line; predict the degradation trend of the energy storage cabinet based on the accumulated thermal effects of the energy storage cabinet; and estimate the service life of the energy storage cabinet based on the degradation trend of the energy storage cabinet to obtain the service life data of the energy storage cabinet.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the visualization method for the energy storage cabinet according to any one of claims 1 to 8 is implemented.

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