An automatic detection system and method for power distribution cabinets, and an electronic device

Abstract

The application provides an automatic detection system and method for a power distribution cabinet, and an electronic device. The method comprises: obtaining operation information of a distributed energy storage power distribution cabinet corresponding to a local controller, and obtaining an operation state based on the operation information; when the operation state is abnormal, generating first prompt information and sending it to a field display device, and sending operation information corresponding to the operation state to a central server; when the operation state is normal within a first preset time length, obtaining identity information of the distributed energy storage power distribution cabinet from the central server, when the identity information indicates that the corresponding distributed energy storage power distribution cabinet is a target power distribution cabinet and the operation states of a plurality of associated power distribution cabinets of the target power distribution cabinet are also normal, performing self-checking on the target power distribution cabinet; and when the self-checking fails, generating second prompt information and sending it to a user device. By obtaining and analyzing the operation information of the distributed energy storage power distribution cabinet in real time on the local controller, the abnormal operation state of the equipment can be found in time.

Description

Technical Field

[0001] This application belongs to the technical field of electrical equipment and automatic detection, and particularly relates to an automatic detection system and method for distribution cabinets, and electronic equipment. Background Technology

[0002] As the requirements for electrical equipment management in power distribution networks increase, traditional distribution cabinet management systems can no longer meet the needs of intelligent and refined management. This is because they typically use a central server to periodically sample each distribution cabinet at a fixed frequency, without considering the differences in data change frequency and business importance.

[0003] In this situation, the traditional power distribution cabinet management system concentrates its functions on the central server. Although it can use the central server to obtain the detection information of multiple power distribution cabinets, there are data silos between the information, which will lead to a large amount of redundant data transmission and storage, increasing system power consumption and communication burden.

[0004] Based on this, this application provides an automatic detection system and method, and electronic equipment for power distribution cabinets, to improve related technologies. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this application is to provide an automatic detection system and method, and electronic equipment for power distribution cabinets.

[0006] The first aspect of this application proposes an automatic detection system for distribution cabinets, including multiple distributed energy storage distribution cabinets and a local controller corresponding to each distributed energy storage distribution cabinet. The distributed energy storage distribution cabinets are communicatively connected to a central server through their respective local controllers, wherein any local controller is configured to:

[0007] Obtain the operation information of the corresponding distributed energy storage distribution cabinet, and obtain the operation status based on the operation information. The operation status includes abnormal and normal.

[0008] When the operating status is normal within a first preset time period, the identity information of the distributed energy storage distribution cabinet is obtained from the central server. When the identity information indicates that the distributed energy storage distribution cabinet is the target distribution cabinet and the operating status of multiple associated distribution cabinets of the target distribution cabinet is also normal, a self-test is performed on the target distribution cabinet. When the self-test fails, a second prompt message is generated and sent to the user equipment. The identity information is obtained by the central server based on the interval operating information and / or local prediction score sent by each distributed energy storage distribution cabinet.

[0009] Preferred methods for obtaining the identity information of the distributed energy storage distribution cabinet from the central server include:

[0010] A request to obtain identity information is sent to the central server. The central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller. The interval operation information is the operation information collected in the most recent N interval periods, and all N interval periods are within a first preset duration corresponding to a period in which the operation status is normal; or,

[0011] The central server sends an identity information acquisition request. The central server determines the identity information based on the local prediction scores sent by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets during a time period corresponding to a first preset duration in which the operating status is normal, and then feeds the identity information back to the local controller.

[0012] Preferably, the method for obtaining the local prediction score includes:

[0013] Using a low-computing-power platform built on a local controller, a prediction scoring model is used to obtain a local prediction score based on the operating information acquired during the time period corresponding to the first preset duration when the operating status is normal.

[0014] Preferably, the central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller, including:

[0015] Obtain the operational similarity between the interval operation information of each distributed energy storage distribution cabinet and its corresponding preset operation information;

[0016] Obtain the actual percentage of distributed energy storage distribution cabinets with an operational similarity lower than the preset similarity;

[0017] Obtain the preset relationship between the quantity ratio and the target distribution cabinet selection ratio; based on the preset relationship, obtain the target distribution cabinet selection ratio corresponding to the actual quantity ratio and use it as the selected ratio, and select one or more distributed energy storage distribution cabinets that meet the selected ratio requirements and have the lowest operation similarity as the target distribution cabinets.

[0018] Preferably, the process of performing a self-test on the target power distribution cabinet includes:

[0019] The system acquires self-test instruction information sent by the central server. The self-test instruction information includes a self-test instruction sequence number and instruction retransmission data. The self-test instruction sequence number is used to indicate the self-test instruction issued by the central server, and the instruction retransmission data is used to indicate the retransmission interval and number of times for the self-test instruction sequence number. The self-test instruction includes specific self-test parameters for battery system testing, electrical connection testing, power regulation function testing, and protection function verification.

[0020] Based on the self-test instruction information, a self-test judgment strategy is obtained; the self-test judgment result of the target power distribution cabinet is obtained through the self-test judgment strategy, and the self-test judgment result is used to indicate whether the target power distribution cabinet has failed the self-test.

[0021] Preferably, when the self-test fails, the local controller is further configured to:

[0022] The adjusted operating settings information is obtained from the central server. The adjusted operating settings information is generated by the central server based on the self-test results sent by the target power distribution cabinet, historical operating data, and the operating settings information before adjustment. The adjusted operating settings information is used to limit the charging and discharging power and temperature control strategy of the target power distribution cabinet.

[0023] Preferably, the operation information of the corresponding distributed energy storage distribution cabinet is obtained, and the operation status is obtained based on the operation information, including:

[0024] The voltage, current, and temperature sensing data, as well as time information, of the corresponding distributed energy storage distribution cabinet are collected and used as a set of sensing data. The collected set of sensing data is cleaned, and extreme data exceeding the data type corresponding to each sensing data are removed before it is used as operating information.

[0025] When the operating status of the distributed energy storage distribution cabinet and its multiple associated distribution cabinets are normal within the previous first preset time period and the self-test is successful, a subset of key parameters is obtained from the operating information according to the preset time interval, and the operating status is obtained by comparing the subset of key parameters with the corresponding preset range.

[0026] Otherwise, all the running information within the first preset time period will be used as the basis for judgment, and the running status will be obtained by comparing the full amount of running information with the corresponding preset range.

[0027] Preferably, all operational information within a first preset time period is used as the judgment criterion, and the operational status is obtained by comparing all operational information with the corresponding preset range, including:

[0028] The system obtains range adjustment information from the central server, updates the preset range stored in the local controller corresponding to each sensor data type in the operation information based on the range adjustment information, and generates an updated preset range; it then uses the updated preset range to judge all the operation information and obtain the operation status.

[0029] The range adjustment information is generated by the central server performing the following operations:

[0030] The number of self-test failures of the distributed energy storage distribution cabinet and its multiple associated distribution cabinets within the previous first preset time period is counted; the proportion of the number of distribution cabinets with self-test failures to the total number of distribution cabinets used to count the number of self-test failures is taken as the abnormality ratio.

[0031] The parameter threshold tightening range corresponding to the abnormal percentage is determined according to the preset adjustment rules; range adjustment information is generated based on the tightening range.

[0032] A second aspect of this application provides an automatic detection method for a power distribution cabinet, the method comprising:

[0033] Obtain the operation information of the distributed energy storage distribution cabinet corresponding to the local controller, and obtain the operation status based on the operation information. The operation status includes abnormal and normal.

[0034] When the operating status is normal within a first preset time period, the identity information of the distributed energy storage distribution cabinet is obtained from the central server. When the identity information indicates that the distributed energy storage distribution cabinet is the target distribution cabinet and the operating status of multiple associated distribution cabinets of the target distribution cabinet is also normal, a self-test is performed on the target distribution cabinet. When the self-test fails, a second prompt message is generated and sent to the user equipment. The identity information is obtained by the central server based on the interval operating information and / or local prediction score sent by each distributed energy storage distribution cabinet.

[0035] A third aspect of this application provides an electronic device comprising a memory and at least one processor, the memory storing a computer program and the processor executing the computer program to enable the electronic device to perform the method as described in any of the second aspects.

[0036] This application provides an automatic detection system and method for distribution cabinets, as well as electronic equipment. By acquiring and analyzing the operating information of distributed energy storage distribution cabinets in real time on a local controller, abnormal operating states of the equipment can be detected promptly. In abnormal situations, rapid response and centralized management are achieved through on-site prompts and information reporting to a central server. After the equipment has been operating normally for a period of time, the central server determines the target distribution cabinet according to preset rules and triggers a self-test operation. The self-test process is executed according to the self-test instructions issued by the central server to ensure that the equipment performance meets the requirements.

[0037] The beneficial effects of the technical solution provided in this application are as follows: Firstly, by acquiring and analyzing the operating information of the distributed energy storage distribution cabinet in real time on the local controller, the drawbacks of continuous sampling of each distribution cabinet by the central server in traditional systems are avoided. Local processing reduces the transmission and storage of a large amount of redundant data, thereby reducing system power consumption and communication burden. Secondly, the distributed energy storage distribution cabinet plays an energy buffering and regulating role in the power grid. The automatic detection system can monitor its status in real time and promptly detect anomalies, such as insufficient power or overcharging / over-discharging caused by battery failure, ensuring the normal operation of the energy storage distribution cabinet, thereby improving grid stability and reducing power outages caused by energy storage equipment failures. Furthermore, by distributing control functions to each local controller, even if individual distributed energy storage distribution cabinets or their local controllers fail, it will not affect the normal operation of other parts of the entire system, effectively reducing the risk of system failure and improving system reliability. At the same time, the real-time monitoring and rapid response capabilities of the local controllers can promptly detect and handle abnormal situations, preventing further escalation of the fault. When facing complex situations such as battery failures and electrical connection problems that may occur in distributed energy storage systems, decentralized control ensures the stable operation of the power grid. Furthermore, the central server can formulate optimal energy management strategies based on real-time grid operation data and the status information of each distributed energy storage distribution cabinet. For example, it can coordinate the discharge of each energy storage distribution cabinet during peak grid load periods and rationally schedule charging during off-peak periods to achieve peak shaving and valley filling, thereby improving the economy and stability of grid operation. When the operating status is normal within a first preset time period, a pre-judgment of the self-check action is performed based on the operating status of multiple associated distributed energy storage distribution cabinets, breaking down data silos and achieving efficient data utilization. Attached Figure Description

[0038] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Throughout the drawings, the same reference numerals denote the same components. Obviously, the drawings described below are merely some embodiments described in this application, and those skilled in the art can obtain other drawings based on these drawings.

[0039] Figure 1This is a flowchart illustrating an automatic detection method for a power distribution cabinet, provided as an embodiment of this application.

[0040] Figure 2 This is a schematic diagram of a process for obtaining identity information provided in an embodiment of this application. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. It should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application. Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in this application.

[0042] With the development of intelligent electrical equipment and the promotion of smart grids, higher demands are placed on the real-time monitoring and fault early warning capabilities of power distribution systems. Therefore, related technologies have been improved to some extent.

[0043] For example, CN116191667A discloses an intelligent detection and protection smart distribution cabinet, which includes a voltage detection module, a current detection module, a central control module, a network communication module, a load calculation module, a power distribution management module, a fault prediction module, and a display module. The detection method of this invention is a fault prediction method. First, the smart distribution cabinet parameters are configured, and the failure rate of the target smart distribution cabinet within a first preset time period is input. A prediction is then made based on a preset time series analysis model. Finally, the prediction result of the preset time series analysis model is determined as the target failure rate of the target smart distribution cabinet after a second preset time period. This method can provide early warning of faults before they occur. However, for the application scenario of distributed energy storage distribution cabinets, the following shortcomings exist: For a single distributed energy storage distribution cabinet, the above technical solution mainly relies on a time series analysis model for fault prediction, which cannot perform more in-depth inspections when the equipment is operating stably, and cannot solve the risk of sudden faults. Furthermore, even with regular self-checks, in actual power grid operation, distributed energy storage distribution cabinets are located in different places, and their operating status is not only affected by their own factors, but also by the surrounding power grid environment and the operating conditions of other distribution cabinets. The relevant technical solutions focus on predicting the failure rate of a single distribution cabinet by collecting information from a single distribution cabinet, without fully considering the mutual influence between distribution cabinets, thus affecting the accuracy of automatic fault detection.

[0044] For example, CN115603451A discloses a real-time monitoring system for power distribution cabinets, including a central server, indoor power distribution cabinet monitoring units, and outdoor power distribution cabinet monitoring units. This system collects real-time operating data from the power distribution cabinets through the indoor and outdoor monitoring units and uploads the monitored data to the central server in real time. It monitors multiple power distribution cabinets through a single central server. However, this technical solution does not consider a mechanism for self-checking and fault warning based on the status judgment of the associated power distribution cabinets, and therefore cannot reduce the risk of failure caused by sudden problems.

[0045] In other words, the relevant technologies lack an effective collaborative mechanism for distributed monitoring and centralized management, making it difficult to conduct in-depth inspections and achieve fault prevention in distributed energy storage distribution cabinet scenarios when the equipment's operating status is stable for extended periods. Therefore, to address the above problems, this invention provides an automatic detection and fault early warning method based on the operating information of distributed energy storage distribution cabinets.

[0046] The technical solutions of the embodiments of this application and how the technical solutions of the embodiments of this application solve the above-mentioned technical problems will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the various embodiments or technical features described below can be arbitrarily combined to form new embodiments. The order of description of the embodiments below is not intended to limit the preferred order of embodiments. The same or similar concepts or processes may not be described again in some embodiments. Obviously, the described embodiments are some embodiments of the embodiments of this application, but not all embodiments.

[0047] Method implementation examples.

[0048] See Figure 1 This embodiment provides an automatic detection method for power distribution cabinets. The power distribution cabinet is a distributed energy storage power distribution cabinet installed in an automatic detection system. The automatic detection system includes multiple distributed energy storage power distribution cabinets and a local controller corresponding to each distributed energy storage power distribution cabinet. The distributed energy storage power distribution cabinet communicates with a central server through its corresponding local controller. The method can run on the local controller. The local controller can be a controller used to provide low-computing power calculations. This application does not limit the communication method between the power distribution cabinet, the local controller, and the central server. For example, local communication can be industrial fieldbus communication, and remote communication can be 4G / 5G wireless communication.

[0049] The method includes:

[0050] S101, Obtain the operation information of the distributed energy storage distribution cabinet corresponding to the local controller, and obtain the operation status based on the operation information. The operation status includes abnormal and normal.

[0051] S102, when the operating status is abnormal, generate a first prompt message and send it to the on-site display device, and send the operating information corresponding to the operating status to the central server;

[0052] S103, when the operating status is normal within a first preset time period, the identity information of the distributed energy storage distribution cabinet is obtained from the central server. When the identity information indicates that the distributed energy storage distribution cabinet is the target distribution cabinet and the operating status of multiple associated distribution cabinets of the target distribution cabinet is also normal, a self-test is performed on the target distribution cabinet. When the self-test fails, a second prompt message is generated and sent to the user equipment. The identity information is obtained by the central server based on the interval operating information and / or local prediction score sent by each distributed energy storage distribution cabinet.

[0053] Each distributed energy storage distribution cabinet is equipped with a local controller to acquire real-time operational information (such as data corresponding to parameters like battery voltage, current, power, and temperature). The local controller determines the operating status based on this information; if parameters exceed their preset ranges, it is considered abnormal; otherwise, it is considered normal. Once an abnormality is detected, the local controller generates an initial alert (i.e., a fault alarm) and sends it to the field display device, such as a screen or audible and visual alarm, to alert on-site personnel. Simultaneously, abnormal operational information (including specific fault parameters) is sent to the central server. The central server can be considered as collecting abnormal data from each distributed energy storage distribution cabinet, performing centralized analysis such as fault type statistics and fault cause analysis, providing a basis for subsequent maintenance. This achieves intelligent detection of the power grid's distribution cabinets.

[0054] When the distributed energy storage distribution cabinet operates normally within a first preset time period (e.g., 15 days, 30 days), the local controller obtains its identity information from the central server. If the identity information shows a specific target distribution cabinet, and its associated distribution cabinets are operating normally, the local controller performs a self-test on that target distribution cabinet. The self-test may include deep charge / discharge tests on the energy storage battery, electrical connection checks, etc. If the self-test fails, it indicates a potential fault risk. In this case, a second warning message (i.e., a fault warning message) is generated and sent to the user equipment to facilitate advance maintenance arrangements. It can be assumed that the preset self-test time (the time period corresponding to the second preset time period) in related technologies includes the time periods corresponding to the multiple first preset time periods mentioned in this technical solution. The self-test process is executed according to the self-test command information issued by the central server to ensure that the equipment performance meets the requirements.

[0055] As an example, the generation of self-test instruction information is based on the device number of the target distribution cabinet in the central server. The central server maintains a pre-set self-test instruction table, which contains specific self-test instruction information for distribution cabinets with different device numbers. When the central server determines that a distributed energy storage distribution cabinet is the target distribution cabinet, it searches for and selects the corresponding self-test instruction information from the self-test instruction table according to the device number of the target distribution cabinet. The self-test instruction information is sent to the corresponding local controller or the display device corresponding to the local controller to guide the execution of specific self-test operations. Based on the pre-set self-test instruction table, the standardization and specificity of the self-test process can be ensured, meeting the self-test requirements of different distribution cabinets.

[0056] The above technical solution offers the following advantages: Firstly, by acquiring and analyzing the operational information of distributed energy storage distribution cabinets in real time on local controllers, the drawbacks of continuous sampling of each distribution cabinet by the central server in traditional systems are avoided. Local processing reduces the transmission and storage of a large amount of redundant data, thereby reducing system power consumption and communication burden. Secondly, distributed energy storage distribution cabinets play a role in energy buffering and regulation in the power grid. The automatic detection system can monitor their status in real time, promptly detect anomalies, such as insufficient power or overcharging / over-discharging caused by battery failure, ensuring the normal operation of the energy storage distribution cabinets, thereby improving grid stability and reducing power outages caused by energy storage equipment failures. Thirdly, by distributing control functions to various local controllers, even if individual distributed energy storage distribution cabinets or their local controllers fail, it will not affect the normal operation of other parts of the entire system, effectively reducing the risk of system failure and improving system reliability. Simultaneously, the real-time monitoring and rapid response capabilities of the local controllers can promptly detect and handle abnormal situations, preventing further escalation of faults. When facing complex situations such as battery failures and electrical connection problems that may occur in distributed energy storage systems, decentralized control ensures the stable operation of the power grid. Furthermore, the central server can formulate optimal energy management strategies based on real-time grid operation data and the status information of each distributed energy storage distribution cabinet. For example, it can coordinate the discharge of each energy storage distribution cabinet during peak grid load periods and rationally schedule charging during off-peak periods to achieve peak shaving and valley filling, thereby improving the economy and stability of grid operation. When the operating status is normal within a first preset time period, a pre-judgment of the self-check action is performed based on the operating status of multiple associated distributed energy storage distribution cabinets, breaking down data silos and achieving efficient data utilization.

[0057] In an exemplary embodiment, the method for obtaining the identity information of the distributed energy storage distribution cabinet from the central server (i.e., the identity information is obtained by the central server based on the interval operation information and / or local prediction score sent by each distributed energy storage distribution cabinet) includes:

[0058] A request to obtain identity information is sent to the central server. The central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller. The interval operation information is the operation information collected in the most recent N interval periods, and all N interval periods are within the time period corresponding to a first preset duration in which the operation status is normal; and / or,

[0059] The central server sends an identity information acquisition request. The central server determines the identity information based on the local prediction scores sent by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets during a time period corresponding to a first preset duration in which the operating status is normal, and then feeds the identity information back to the local controller.

[0060] In practical applications, when all distributed energy storage distribution cabinets are operating normally, the central server can, after receiving the identity information acquisition request, select the operating information collected from the distributed energy storage distribution cabinets corresponding to the associated distribution cabinets in the most recent N time intervals.

[0061] The local controller sends an identity information retrieval request to the central server. Upon receiving the request, the central server analyzes the operational information collected and transmitted by the local controller from the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets over the most recent N time intervals (all of which are within a first preset duration of normal operation). The central server judges the operational information according to preset judgment rules, thereby obtaining the identity information of the distributed energy storage distribution cabinet, and then feeds back the identity information to the corresponding local controller.

[0062] Alternatively, the central server can make a judgment based on the local prediction scores sent by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets within a first preset time period when they are operating normally. Based on the judgment results, the central server obtains the identity information of the distributed energy storage distribution cabinet and feeds it back to the local controller.

[0063] The above technical solutions offer the following advantages: In Method 1, the central server determines identity information based on operational information across multiple time intervals. Compared to judging based on a single moment or a small amount of data, this multi-time-period data-driven comprehensive analysis method more comprehensively and accurately reflects the actual operating status and characteristics of the distributed energy storage distribution cabinet, thereby improving the accuracy of identity information acquisition. Simultaneously, it avoids the drawbacks of continuous sampling of each distribution cabinet by the central server in traditional systems, achieving a balance between accuracy and low data volume. In Method 2, the local prediction score is based on a pre-assessment of various factors, including the operating status of the distributed energy storage distribution cabinet. The central server combines the local prediction scores of multiple related distribution cabinets to determine identity information, fully considering the potential mutual influences and overall operating conditions between distribution cabinets. This also helps improve the accuracy and reliability of identity information acquisition, ensuring that only under appropriate circumstances will a cabinet be identified as the target distribution cabinet for subsequent self-inspection and other operations.

[0064] The associated distribution cabinets are a pre-set set of relationships between distributed energy storage distribution cabinets, which can be associated according to spatial location. In practical applications, when a distributed energy storage distribution cabinet is in an abnormal operating state, or the number of abnormal occurrences exceeds a preset number, it is removed from the associated distribution cabinet relationships of each distributed energy storage distribution cabinet, so that it is not used for subsequent target distribution cabinet judgment.

[0065] In practical applications, the two judgment methods mentioned above can be executed in parallel to obtain two intermediate identity identifiers for each distributed energy storage distribution cabinet. If the two intermediate identity identifiers are different (one is for the target distribution cabinet and the other is for a non-target distribution cabinet), then when the proportion of the number of different intermediate identity identifiers to the total number of identity identifiers is below a preset proportion (e.g., 10%), the overall judgment is considered to be relatively consistent, and the identity identifier based on the local prediction score is sent to the local controller; otherwise, the identity identifier based on the operation information is sent to the local controller.

[0066] This can be understood as simultaneously employing two judgment methods to determine the identity information of distributed energy storage distribution cabinets: one based on operational information and the other based on local prediction scoring. These two methods independently evaluate each distributed energy storage distribution cabinet, resulting in two corresponding identity identifiers. The two identity identifiers obtained by each distributed energy storage distribution cabinet through the two judgment methods are compared to check for inconsistencies (one for the target cabinet and one for a non-target cabinet). The number of cases with inconsistent identity identifiers across all distributed energy storage distribution cabinets is counted, and the proportion of such cases to the total number of identity identifiers is calculated and compared with a preset proportion.

[0067] If the inconsistency rate is below the preset percentage: it indicates that the overall judgment is relatively consistent. In this case, the judgment result based on the local prediction score can be considered more stable and forward-looking. Therefore, the identity identifier obtained based on the local prediction score is sent to the local controller. If the inconsistency rate reaches or exceeds the preset percentage: it indicates that the judgment result based on the operational information is more reliable because the operational information directly reflects a more accurate picture of the current actual operating conditions. Therefore, the identity identifier obtained based on the operational information is sent to the local controller.

[0068] The above technical solution offers the following advantages: By executing two judgment methods in parallel, it comprehensively considers the characteristics and advantages of both operational information and local prediction scoring. Operational information directly reflects the current actual operating status of the distributed energy storage distribution cabinet, while local prediction scoring provides a certain degree of forward-looking assessment based on historical data and prediction models. In most cases (i.e., when the inconsistency rate is low), identity identification based on local prediction scoring can take into account both the current state and future trends, providing a more comprehensive basis for judgment; while when the inconsistency rate is high, identity identification based on operational information ensures that the judgment closely matches the current reality, avoiding erroneous judgments caused by errors or uncertainties in the prediction model. It avoids the potential risks associated with relying on only one judgment method. If judgment is based solely on operational information, some long-term potential problems or trends may be overlooked; while relying solely on local prediction scoring may be limited by model accuracy and data quality. By combining the two methods and making decisions based on the inconsistency rate, a more reliable judgment result can be selected under different circumstances, improving the accuracy and reliability of identity information judgment. When the inconsistency rate is low, sending identity identifiers based on local prediction scores can reduce frequent identity changes caused by instantaneous fluctuations or short-term anomalies in operational information, making the system's decision-making more stable and contributing to the long-term stable operation of distributed energy storage distribution cabinets. Depending on the inconsistency rate, the system can flexibly choose to send identity identifiers based on either local prediction scores or operational information, enabling it to better adapt to different operating environments and conditions. For example, when the system is operating smoothly and the prediction model is accurate, it can rely more on local prediction scores; while when the system's operating state is complex and variable or the prediction model requires further optimization, judgments based on operational information can promptly reflect the actual situation, ensuring the system's adaptability and stability.

[0069] In one exemplary embodiment, the local predicted score is obtained by means of:

[0070] Using a low-computing-power platform built on a local controller, a prediction scoring model is used to obtain a local prediction score based on the operating information acquired during the time period corresponding to the first preset duration when the operating status is normal.

[0071] At the local controller, when the distributed energy storage distribution cabinet is operating normally and continues for a period corresponding to the first preset duration, the local controller will acquire the cabinet's operating information in real time or periodically. This operating information includes key parameters such as the voltage, current, power, and temperature of the energy storage battery, reflecting the actual operating status of the distribution cabinet during that period. A low-computing-power platform is built on the local controller to run a simplified predictive scoring model. This predictive scoring model can be considered specifically designed for efficient operation in resource-constrained environments, enabling effective analysis and processing of operating information without requiring powerful computing resources. The predictive scoring model on the low-computing-power platform uses the collected operating information as input data. The model performs calculations and evaluations based on the operating information according to preset algorithms and rules. This involves trend analysis of operating parameters, comparison with historical data, and identification of potential failure modes. By analyzing the model, a local predictive score can be obtained, which reflects the potential failure risk or performance change trend of the distributed energy storage distribution cabinet in the future. The generated local prediction scores are stored in the local controller and sent to the central server according to certain rules (such as timed sending or triggering conditions mentioned above). The central server can collect the local prediction scores of each distributed energy storage distribution cabinet.

[0072] The above technical solution has the following advantages: Since the local prediction score is generated at the local controller, it is only necessary to send the score result to the central server instead of transmitting a large amount of raw operating information. This reduces the amount of data communication within the system and is especially suitable for scenarios with a large number of distributed energy storage distribution cabinets and limited network bandwidth. It reduces the requirements for communication bandwidth and improves the communication efficiency and reliability of the system.

[0073] At the same time, it reduces potential delays and packet loss during data transmission, ensuring that the central server can obtain key information (local prediction score) from each distributed energy storage and distribution cabinet more promptly. This enables faster centralized management and decision-making, improving the overall system's response speed and operational efficiency.

[0074] Furthermore, the local controller can analyze the operating information and generate predictive scores locally and in a timely manner, without waiting for instructions or data processing from the central server. The distributed energy storage distribution cabinet can more quickly make a preliminary assessment of its future operating status. When potential problems occur, it can take local response measures (such as adjusting operating parameters) more quickly, which improves the real-time performance and autonomy of the system and enhances the system's ability to prevent and respond to faults.

[0075] The technical solution provided in this embodiment makes full use of the computing resources of the low-computing-power platform on the local controller. By running a simplified prediction and scoring model on the low-computing-power platform, the necessity of equipping each local controller with a high-performance computing device is avoided, reducing hardware costs and energy consumption. At the same time, this also allows for a more rational allocation of computing resources, leaving complex, global data analysis tasks to the central server, while the local controllers focus on processing key local information, thus improving the resource utilization efficiency of the entire system.

[0076] As an example, the methods for obtaining the local predicted score include:

[0077] A U-Net-based predictive scoring model is used to obtain local predicted scores, built on a low-computing-power platform of the local controller. Specifically, the U-Net model, with its encoder-decoder structure, can efficiently process operational information and accurately predict the operating status of the distribution cabinet. The encoder extracts key features from the operational information, such as the changing trends and patterns of parameters like voltage, current, power, and temperature; the decoder generates predicted scores based on these features, accurately reflecting the operating status of the distribution cabinet. In this application, the simplified and optimized U-Net model can run efficiently on a low-computing-power platform. By adjusting the network depth and reducing the complexity of convolutional layers, the computational requirements are reduced while maintaining prediction accuracy, enabling rapid execution on the local controller. Furthermore, the U-Net model, through a skip connection mechanism, can better capture local and global features in the operational information. This characteristic allows it to effectively identify potential fault modes and performance change trends when processing relatively complex operational data, providing a more accurate basis for local predicted scores.

[0078] By running a U-Net-based prediction and scoring model on a low-computing-power platform, the local controller can analyze operational information and generate prediction scores locally and in a timely manner. This process eliminates the need to wait for instructions or data processing from the central server, achieving rapid response. The generated local prediction scores can be sent to the central server according to certain rules for higher-level analysis and decision-making, such as identifying target distribution cabinets and optimizing system configurations, thereby improving the intelligence level and operational efficiency of the entire distributed energy storage distribution cabinet automatic detection system.

[0079] As another example, building upon the previous example, the downsampling and corresponding upsampling structures are reduced to 2-3 layers, effectively reducing the number of network parameters and computational complexity while still retaining a certain level of feature extraction and fusion capabilities. Skip connections are retained between the shallow encoder and decoder to pass the main features, while some skip connections in deeper layers are discarded, thereby reducing computational and storage overhead. The number of convolutional kernels in each convolutional operation is reduced. For example, the original model might use 64 convolutional kernels in the first layer, which can be reduced to 32, lowering the computational cost and parameter storage requirements of that layer while maintaining a certain level of feature extraction capability.

[0080] See Figure 2 In one exemplary embodiment, the central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller, including:

[0081] S201, obtain the operational similarity between the interval operation information of each distributed energy storage distribution cabinet and its corresponding preset operation information; obtain the actual proportion of distributed energy storage distribution cabinets with operational similarity lower than the preset similarity.

[0082] S202, obtain the preset relationship between the quantity ratio and the target distribution cabinet selection ratio; according to the preset relationship, obtain the target distribution cabinet selection ratio corresponding to the actual quantity ratio and use it as the selected ratio, and select one or more distributed energy storage distribution cabinets that meet the selected ratio requirement and have the lowest operational similarity as target distribution cabinets. Meeting the selected ratio requirement means that the proportion of the number of selected target distribution cabinets to the number of the distributed energy storage distribution cabinets and their associated multiple distributed energy storage distribution cabinets is not less than the minimum number of the selected ratio.

[0083] The central server calculates the operational similarity between the interval operation information (including timestamps) of each distributed energy storage distribution cabinet and its corresponding preset operation information. Operational similarity can be expressed as a percentage; for example, if the interval operation information of a distribution cabinet is completely consistent with the preset operation information (on the same time scale), the operational similarity is 100%; if the difference is significant, the similarity is low. Statistical analysis is performed on the operational similarity of all distributed energy storage distribution cabinets to identify the number of cabinets with operational similarity lower than the preset similarity. The proportion of distribution cabinets with similarity lower than the preset value to the total number of distributed energy storage distribution cabinets is calculated, i.e., the actual proportion. For example, if there are 10 distributed energy storage distribution cabinets, and one of them has an operational similarity lower than the preset similarity, then the actual proportion is 10%.

[0084] The central server pre-stores the preset relationship between the quantity ratio and the target distribution cabinet selection ratio, as well as preset operating information. Both the preset relationship and the preset operating information can be set based on historical data, system operating experience, or a twin network-based algorithm model, and this application does not impose any restrictions on them. For example, when the actual quantity ratio is between 5% and 20%, the target distribution cabinet selection ratio is 10%; when the actual quantity ratio is between 20% and 50%, the target distribution cabinet selection ratio is 20%. The actual ratio and the target distribution cabinet selection ratio are positively correlated. Based on the calculated actual quantity ratio, the corresponding preset relationship is found, and the corresponding target distribution cabinet selection ratio is determined. Among all distributed energy storage distribution cabinets, they are sorted from low to high operating similarity, and one or more distribution cabinets with the lowest operating similarity that meet the target distribution cabinet selection ratio requirements are selected as the target distribution cabinets.

[0085] The above technical solution offers the following advantages: By comparing the interval operation information of each distributed energy storage distribution cabinet with preset operation information, it is possible to accurately identify equipment whose operating conditions may deviate from the ideal state. This method avoids the rough assessment of equipment in traditional management methods, allowing maintenance personnel to focus on in-depth inspection and maintenance of equipment showing signs of problems, thus improving the precision of equipment management. Determining the selection ratio of target distribution cabinets based on the actual number ratio and the preset ratio relationship enables differentiated maintenance strategies for equipment groups with different operating conditions. For equipment with low operational similarity that is identified as target distribution cabinets, more detailed maintenance plans can be developed, such as increasing inspection frequency and conducting in-depth diagnostics, achieving personalized equipment maintenance. Using one or more distributed energy storage distribution cabinets with the lowest operational similarity as target distribution cabinets is beneficial because target distribution cabinets are often weak links in the system; this technical solution can reduce potential failure points in the system and improve the overall performance of the distributed energy storage system.

[0086] In an exemplary embodiment, the process of performing a self-test on the target distribution cabinet includes:

[0087] The system acquires self-test instruction information sent by the central server. The self-test instruction information includes a self-test instruction sequence number and instruction retransmission data. The self-test instruction sequence number is used to indicate the self-test instruction issued by the central server, and the instruction retransmission data is used to indicate the retransmission interval and number of times for the self-test instruction sequence number. The self-test instruction includes specific self-test parameters for battery system testing, electrical connection testing, power regulation function testing, and protection function verification.

[0088] Based on the self-test instruction information, a self-test judgment strategy is obtained; the self-test judgment result of the target power distribution cabinet is obtained through the self-test judgment strategy, and the self-test judgment result is used to indicate whether the target power distribution cabinet has failed the self-test.

[0089] The central server sends self-test command information to the target distribution cabinet. This command information includes a self-test command sequence number and command retransmission data. The self-test command sequence number identifies the command issued by the central server, while the command retransmission data specifies the interval and number of retransmissions required if the command sequence number needs to be retransmitted. The self-test command details the test items to be performed, including specific parameters for battery system testing, electrical connection testing, power regulation function testing, and protection function verification. Based on the received self-test command information, the local controller obtains the corresponding self-test judgment strategy. The self-test judgment strategy refers to a series of predefined rules and conditions used to evaluate whether the various indicators corresponding to the target distribution cabinet's self-test meet the standards for normal operation. According to the self-test judgment strategy, the local controller controls the target distribution cabinet to perform a series of self-test operations or obtains the self-test results (indicators) input by the user according to the self-test requirements. During the self-test process, various test data are collected and compared with the standards in the self-test judgment strategy. This comparative analysis yields the judgment result for each self-test item, i.e., whether it meets the preset standards. If all self-test items meet the standards, the target distribution cabinet is deemed to have passed the self-test; if any item fails to meet the standards, the target distribution cabinet is deemed to have failed the self-test. The self-test judgment process can be considered to be executed by the local controller. While the equipment can continue to operate under normal conditions with minor issues in some indicators, sufficient troubleshooting is required when the distributed energy storage distribution cabinet is used as the target distribution cabinet. User equipment refers to the mobile phones, laptops, tablets, etc., used by on-site management personnel. Compared to on-site display devices, which are used by general operators to obtain prompts and understand abnormal states in real time, management personnel using user equipment can, based on the secondary prompts and the overall system operating status and maintenance resources, arrange for professional personnel to troubleshoot and repair the distribution cabinets that failed the self-test in a timely manner.

[0090] The above technical solution has the following advantages: by combining the instructions of the central server and the execution of the local controller, intelligent management of the distributed energy storage distribution cabinet is realized, enabling the entire energy storage system to operate more efficiently.

[0091] In an exemplary embodiment, when the self-test fails, the local controller is further configured to:

[0092] The adjusted operating settings information is obtained from the central server. The adjusted operating settings information is generated by the central server based on the self-test results sent by the target power distribution cabinet, historical operating data, and the operating settings information before adjustment. The adjusted operating settings information is used to limit the charging and discharging power and temperature control strategy of the target power distribution cabinet.

[0093] When the target distribution cabinet fails its self-test, the local controller activates an emergency response mechanism. First, the local controller sends a request to the central server to obtain adjusted operating settings information. Upon receiving the request, the central server analyzes the target distribution cabinet's self-test results, including specific fault points and abnormal parameters. Simultaneously, the central server retrieves the distribution cabinet's historical operating data, reviewing its past operating modes, fault records, and maintenance history. Combining this with the pre-adjustment operating settings information, the central server uses a built-in optimization algorithm to recalculate and generate adjusted operating settings information. This includes setting the upper and lower limits of charging and discharging power, and adjusting parameters of the temperature control strategy. After generation, the central server sends the adjusted operating settings information back to the local controller, which then updates the target distribution cabinet's operating parameters to ensure it operates under the new settings.

[0094] The above technical solution offers the following advantages: First, the generated adjusted operating settings closely match the current actual condition of the equipment, effectively reducing the risk of failure and ensuring the basic functions of the equipment. By limiting charging and discharging power, battery overload or overcharging can be prevented, extending battery life. Second, the adjusted operating settings provide a transitional operating mode, allowing the distribution cabinet to continue operating until the fault is completely resolved. This mechanism not only enhances the system's fault tolerance but also provides maintenance personnel with sufficient response time. Finally, by updating the operating settings, the local controller can more accurately control the operation of the distribution cabinet. The centralized management and optimization analysis functions of the central server enable the entire system to dynamically adjust according to actual conditions, ensuring the stable operation of the distributed energy storage system.

[0095] In an exemplary embodiment, obtaining the operation information of the corresponding distributed energy storage distribution cabinet, and obtaining the operation status based on the operation information, includes:

[0096] The voltage, current, and temperature sensing data, as well as time information, of the corresponding distributed energy storage distribution cabinet are collected and used as a set of sensing data. The collected set of sensing data is cleaned, and extreme data exceeding the data type corresponding to each sensing data are removed before it is used as operating information.

[0097] When the operating status of the distributed energy storage distribution cabinet and its multiple associated distribution cabinets are normal within the previous first preset time period and the self-test is successful, a subset of key parameters is obtained from the operating information according to the preset time interval, and the operating status is obtained by comparing the subset of key parameters with the corresponding preset range.

[0098] Otherwise, all the running information within the first preset time period will be used as the basis for judgment, and the running status will be obtained by comparing the full amount of running information with the corresponding preset range.

[0099] The collected sensor data sets are cleaned to remove extreme data from each sensor data type. Extreme data refers to data that exceeds the normal range, which may be due to sensor malfunction or interference. After removing this data, more accurate operational information is obtained. Cleaning rules include, for example: removing outliers <0V or >150% of the rated voltage; filtering out instantaneous spikes exceeding the circuit breaker's withstand limit.

[0100] If the distributed energy storage distribution cabinet and its associated distribution cabinets have all operated normally and successfully completed self-tests within the previous preset time period, a subset of key parameters can be extracted from the operational information at preset time intervals (e.g., 1 hour, 12 hours, 1 day). These key parameters are then compared to preset normal ranges to determine if the current operational status is normal. Different judgment methods are used depending on the situation. When the operational status is good and self-tests are successful, using a subset of key parameters reduces data processing and improves judgment efficiency. However, when the operational status is unstable or self-tests fail, using the full amount of operational information allows for a more comprehensive assessment of the equipment's operational status and avoids overlooking potential problems.

[0101] In an exemplary embodiment, all operational information within a first preset time period is used as the determination criterion, and the operational status is obtained by comparing the full amount of operational information with the corresponding preset range, including:

[0102] The system obtains range adjustment information from the central server, updates the preset range stored in the local controller corresponding to each sensor data type in the operation information based on the range adjustment information, and generates an updated preset range; it then uses the updated preset range to judge all the operation information and obtain the operation status.

[0103] The range adjustment information is generated by the central server performing the following operations:

[0104] The number of self-test failures of the distributed energy storage distribution cabinet and its multiple associated distribution cabinets within the previous first preset time period is counted; the proportion of the number of distribution cabinets with self-test failures to the total number of distribution cabinets used to count the number of self-test failures is taken as the abnormality ratio.

[0105] The parameter threshold tightening range corresponding to the abnormal percentage is determined according to the preset adjustment rules; range adjustment information is generated based on the tightening range.

[0106] In other words, in this embodiment, the range of the distributed energy storage distribution cabinet and its associated distribution cabinets can be uniformly adjusted. By using a central server to statistically analyze the percentage of anomalies and generate range adjustment information, the local controller can dynamically update the preset range, avoiding judgment errors that may be caused by a fixed preset range and improving the accuracy of operational status judgment.

[0107] As an example, a high percentage of anomalies indicates a greater risk of potential failures in the system. Tightening the parameter threshold by a larger margin reduces the preset range, making it easier for the system to identify anomalies and take preventative measures. For instance, when the percentage of anomalies is between 20% and 30% (inclusive), the parameter threshold is tightened by 2%; when the percentage is between 30% and 40% (inclusive), the threshold is tightened by 2.5%, effectively reflecting changes in the overall health of the system.

[0108] As an example, an automatic detection method for distribution cabinets is provided, wherein the distribution cabinet is a distributed energy storage distribution cabinet installed in an automatic detection system. The automatic detection system includes multiple distributed energy storage distribution cabinets and a local controller corresponding to each distributed energy storage distribution cabinet. Each distributed energy storage distribution cabinet communicates with a central server through its corresponding local controller, and each distributed energy storage distribution cabinet and its corresponding local controller form an independent node. The central server runs a DCS (Distribution Control System). The measurement points of each distribution cabinet are assigned a unique identifier (distribution cabinet number, channel number) in the DCS database, enabling precise data location across cabinets. The method mainly runs on the local controller, and the specific steps are as follows:

[0109] P10, Steps for obtaining runtime information and determining status:

[0110] The local controller acquires real-time operational information from its corresponding distributed energy storage distribution cabinet, including key parameters such as battery voltage, current, power, and temperature. It analyzes this information against preset normal ranges to determine if the operational status is normal. If the operational information exceeds the preset normal range, the current operational status is determined to be abnormal; otherwise, it is determined to be normal.

[0111] P20, Exception handling steps:

[0112] When the operating status is determined to be abnormal, the local controller generates an initial prompt message and sends the message to the field display device, such as a display screen or an audible and visual alarm, to remind on-site personnel to pay attention to and handle the abnormal situation in a timely manner.

[0113] Simultaneously, operational information recording abnormal situations is sent to the central server. The central server collects abnormal operational information from each local controller, performs centralized analysis and processing, such as statistically analyzing fault types and causes, to provide data support for subsequent maintenance work.

[0114] P30, Target distribution cabinet identification and self-test triggering steps:

[0115] When the distributed energy storage distribution cabinet maintains normal operation for a first preset period, the local controller sends an identity information retrieval request to the central server. For example, the first preset period is 15 days, while the conventional self-inspection cycle is 60 days, meaning multiple self-inspection triggers occur within the self-inspection cycle. The 15-day period can be considered the cycle for triggering identity verification after meeting the conditions, and the 60-day period is the mandatory self-inspection cycle.

[0116] After receiving the request, the central server analyzes the operating data of the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets according to the preset judgment rules, determines its identity information, and feeds back the identity information to the corresponding local controller.

[0117] If the identity information received by the local controller indicates that its corresponding distributed energy storage distribution cabinet is the target distribution cabinet, and the operating status of multiple associated distribution cabinets of the target distribution cabinet is also normal, then a self-test operation for the target distribution cabinet is triggered.

[0118] P40, Target Distribution Cabinet Self-Test Procedure:

[0119] The central server sends a self-test command to the target distribution cabinet, which includes the self-test command sequence number and command retransmission data. The self-test command sequence number identifies the specific self-test command, while the command retransmission data specifies the interval and number of retransmissions required when the self-test command needs to be retransmitted.

[0120] The self-test instructions cover multiple specific test items and their parameter requirements, including battery system testing, electrical connection testing, power regulation function testing, and protection function verification.

[0121] The local controller obtains the corresponding self-test judgment strategy based on the received self-test command information. The self-test judgment strategy is a series of predefined rules and conditions used to evaluate whether the various indicators of the target distribution cabinet meet the standards for normal operation.

[0122] According to the self-test judgment strategy, the local controller controls the target distribution cabinet to perform a series of self-test operations, collects various test data, and compares and analyzes them with the standards in the self-test judgment strategy to obtain the judgment result of each self-test item. If all self-test items meet the standards, the target distribution cabinet is judged to have passed the self-test; if any item fails to meet the standards, the self-test is judged to have failed.

[0123] P50, Self-test Failure Handling Steps:

[0124] When the target distribution cabinet fails to perform a self-test, the local controller sends a request to the central server to obtain the adjusted operating settings information.

[0125] Based on the self-inspection results, historical operating data, and operating settings information before adjustment, the central server uses a built-in optimization algorithm to recalculate and generate the adjusted operating settings information.

[0126] The adjusted operating settings are primarily used to limit the charging and discharging power of the target distribution cabinet and adjust the temperature control strategy to reduce the risk of failure and ensure that the equipment can operate safely and stably under the new settings.

[0127] After receiving the adjusted operating settings information, the local controller updates the operating parameters of the target distribution cabinet and continues to operate according to the new settings.

[0128] This automatic detection method, by acquiring and analyzing the operational information of the distributed energy storage distribution cabinet in real time on the local controller, can promptly detect abnormal operating states of the equipment. In abnormal situations, it achieves rapid response and centralized management through on-site prompts and reporting information to the central server. After the equipment has been operating normally for a period of time, the central server determines the target distribution cabinet according to preset rules and triggers a self-test operation. The self-test process is executed according to the self-test instructions issued by the central server, covering multiple key test items to ensure that the equipment performance meets the requirements. If the self-test fails, the central server will generate adjusted operating settings information based on relevant data to limit charging and discharging power and optimize temperature control strategies, ensuring the basic functions and safe operation of the equipment.

[0129] Therefore, compared with the prior art, the automatic detection method for distribution cabinets provided in this application has the following advantages:

[0130] Real-time monitoring of the distributed energy storage distribution cabinet's operational status is achieved by acquiring operational information and determining its operational status in real time through a local controller. This allows for timely detection of anomalies, generation of alerts, and transmission to the central server. The use of a distributed local controller reduces reliance on the central server, lowers communication load, and improves response speed, meeting the monitoring needs of complex and variable power grid environments. Based on operational information, the system determines the operational status and generates a local predictive score. A combined identity verification mechanism using both operational information and local predictive scores improves the accuracy and reliability of identity information acquisition, ensuring accurate location of the target distribution cabinet. Self-checking functions promptly identify and address potential fault risks, improving the reliability and stability of the distribution cabinet, reducing power outages caused by equipment failures, and enhancing power grid operational safety. The local controller utilizes a low-computing-power platform and runs a simplified predictive scoring model, enabling rational resource allocation, reducing hardware costs and energy consumption, and improving system resource utilization efficiency.

[0131] System Implementation Example.

[0132] This embodiment provides an automatic detection system for power distribution cabinets. The specific implementation and technical effects are consistent with the embodiments described in the above method embodiments, and some details will not be repeated. It includes multiple distributed energy storage power distribution cabinets and a local controller corresponding to each distributed energy storage power distribution cabinet. The distributed energy storage power distribution cabinets communicate with a central server through their corresponding local controllers. Each local controller is configured as follows:

[0133] Obtain the operation information of the corresponding distributed energy storage distribution cabinet, and obtain the operation status based on the operation information. The operation status includes abnormal and normal. When the operation status is abnormal, generate a first prompt message and send it to the field display device, and send the operation information corresponding to the operation status to the central server.

[0134] When the operating status is normal within a first preset time period, the identity information of the distributed energy storage distribution cabinet is obtained from the central server. When the identity information indicates that the distributed energy storage distribution cabinet is the target distribution cabinet and the operating status of multiple associated distribution cabinets of the target distribution cabinet is also normal, a self-test is performed on the target distribution cabinet. When the self-test fails, a second prompt message is generated and sent to the user equipment. The identity information is obtained by the central server based on the interval operating information and / or local prediction score sent by each distributed energy storage distribution cabinet.

[0135] In one exemplary embodiment, the method for obtaining the identity information of the distributed energy storage distribution cabinet from the central server includes:

[0136] A request to obtain identity information is sent to the central server. The central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller. The interval operation information is the operation information collected in the most recent N interval periods, and all N interval periods are within the time period corresponding to a first preset duration in which the operation status is normal; and / or,

[0137] The central server sends an identity information acquisition request. The central server determines the identity information based on the local prediction scores sent by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets during a time period corresponding to a first preset duration in which the operating status is normal, and then feeds the identity information back to the local controller.

[0138] In one exemplary embodiment, the local predicted score is obtained by means of:

[0139] Using a low-computing-power platform built on a local controller, a prediction scoring model is used to obtain a local prediction score based on the operating information acquired during the time period corresponding to the first preset duration when the operating status is normal.

[0140] In an exemplary embodiment, the central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller, including:

[0141] Obtain the operational similarity between the interval operation information of each distributed energy storage distribution cabinet and its corresponding preset operation information;

[0142] Obtain the actual percentage of distributed energy storage distribution cabinets with an operational similarity lower than the preset similarity;

[0143] Obtain the preset relationship between the quantity ratio and the target distribution cabinet selection ratio; based on the preset relationship, obtain the target distribution cabinet selection ratio corresponding to the actual quantity ratio and use it as the selected ratio, and select one or more distributed energy storage distribution cabinets that meet the selected ratio requirements and have the lowest operation similarity as the target distribution cabinets.

[0144] In an exemplary embodiment, the process of performing a self-test on the target distribution cabinet includes:

[0145] The system acquires self-test instruction information sent by the central server. The self-test instruction information includes a self-test instruction sequence number and instruction retransmission data. The self-test instruction sequence number is used to indicate the self-test instruction issued by the central server, and the instruction retransmission data is used to indicate the retransmission interval and number of times for the self-test instruction sequence number. The self-test instruction includes specific self-test parameters for battery system testing, electrical connection testing, power regulation function testing, and protection function verification.

[0146] Based on the self-test instruction information, a self-test judgment strategy is obtained; the self-test judgment result of the target power distribution cabinet is obtained through the self-test judgment strategy, and the self-test judgment result is used to indicate whether the target power distribution cabinet has failed the self-test.

[0147] In an exemplary embodiment, when the self-test fails, the local controller is further configured to:

[0148] The adjusted operating settings information is obtained from the central server. The adjusted operating settings information is generated by the central server based on the self-test results sent by the target power distribution cabinet, historical operating data, and the operating settings information before adjustment. The adjusted operating settings information is used to limit the charging and discharging power and temperature control strategy of the target power distribution cabinet.

[0149] In an exemplary embodiment, obtaining the operation information of the corresponding distributed energy storage distribution cabinet, and obtaining the operation status based on the operation information, includes:

[0150] The voltage, current, and temperature sensing data, as well as time information, of the corresponding distributed energy storage distribution cabinet are collected and used as a set of sensing data. The collected set of sensing data is cleaned, and extreme data exceeding the data type corresponding to each sensing data are removed before it is used as operating information.

[0151] When the operating status of the distributed energy storage distribution cabinet and its multiple associated distribution cabinets are normal within the previous first preset time period and the self-test is successful, a subset of key parameters is obtained from the operating information according to the preset time interval, and the operating status is obtained by comparing the subset of key parameters with the corresponding preset range.

[0152] Otherwise, all the running information within the first preset time period will be used as the basis for judgment, and the running status will be obtained by comparing the full amount of running information with the corresponding preset range.

[0153] In an exemplary embodiment, all operational information within a first preset time period is used as the determination criterion, and the operational status is obtained by comparing the full amount of operational information with the corresponding preset range, including:

[0154] The system obtains range adjustment information from the central server, updates the preset range stored in the local controller corresponding to each sensor data type in the operation information based on the range adjustment information, and generates an updated preset range; it then uses the updated preset range to judge all the operation information and obtain the operation status.

[0155] The range adjustment information is generated by the central server performing the following operations:

[0156] The number of self-test failures of the distributed energy storage distribution cabinet and its multiple associated distribution cabinets within the previous first preset time period is counted; the proportion of the number of distribution cabinets with self-test failures to the total number of distribution cabinets used to count the number of self-test failures is taken as the abnormality ratio.

[0157] The parameter threshold tightening range corresponding to the abnormality ratio is determined according to the preset adjustment rules; range adjustment information is generated based on the tightening range.

[0158] Equipment implementation example.

[0159] This embodiment provides an electronic device, the specific embodiment of which is consistent with the embodiment described in the above method embodiment and the technical effects achieved, and some contents will not be repeated.

[0160] The electronic device includes a memory and at least one processor, the memory storing a computer program, and the at least one processor being configured to execute the computer program to implement the method as described in any of the method embodiments.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application.

Claims

1. An automatic detection system for a power distribution cabinet, characterized in that, It includes multiple distributed energy storage distribution cabinets and a local controller corresponding to each distributed energy storage distribution cabinet. The distributed energy storage distribution cabinets communicate with the central server through their respective local controllers, and any one of the local controllers is configured as follows: Obtain the operation information of the corresponding distributed energy storage distribution cabinet, and obtain the operation status based on the operation information. The operation status includes abnormal and normal. When the operating status is normal within a first preset time period, the identity information of the distributed energy storage distribution cabinet is obtained from the central server. When the identity information indicates that the distributed energy storage distribution cabinet is the target distribution cabinet and the operating status of multiple associated distribution cabinets of the target distribution cabinet is also normal, a self-test is performed on the target distribution cabinet. When the self-test fails, a second prompt message is generated and sent to the user equipment. The identity information is obtained by the central server based on the interval operating information and / or local prediction score sent by each distributed energy storage distribution cabinet. The methods for obtaining the identity information of distributed energy storage and distribution cabinets from the central server include: A request to obtain identity information is sent to the central server. The central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller. The interval operation information is the operation information collected in the most recent N interval periods, and all N interval periods are within the time period corresponding to a first preset duration in which the operation status is normal; and / or, The central server sends an identity information acquisition request. The central server judges the identity information based on the local prediction scores sent by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets during the first preset time period when the operating status is normal, and then feeds it back to the local controller. The central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller, including: Obtain the operational similarity between the interval operation information of each distributed energy storage distribution cabinet and its corresponding preset operation information; Obtain the actual percentage of distributed energy storage distribution cabinets with an operational similarity lower than the preset similarity; Obtain the preset relationship between the quantity ratio and the target distribution cabinet selection ratio; based on the preset relationship, obtain the target distribution cabinet selection ratio corresponding to the actual quantity ratio and use it as the selected ratio, and select one or more distributed energy storage distribution cabinets that meet the selected ratio requirements and have the lowest operating similarity as the target distribution cabinets. When the self-test fails, the local controller is also configured to: The adjusted operating settings information is obtained from the central server. The adjusted operating settings information is generated by the central server based on the self-test results sent by the target power distribution cabinet, historical operating data, and the operating settings information before adjustment. The adjusted operating settings information is used to limit the charging and discharging power and temperature control strategy of the target power distribution cabinet.

2. The automatic detection system according to claim 1, characterized in that, The methods for obtaining the local prediction score include: Using a low-computing-power platform built on a local controller, a prediction scoring model is used to obtain a local prediction score based on the operating information acquired during the time period corresponding to the first preset duration when the operating status is normal.

3. The automatic detection system according to claim 1, characterized in that, The process of performing a self-test on the target power distribution cabinet includes: The system acquires self-test instruction information sent by the central server. The self-test instruction information includes a self-test instruction sequence number and instruction retransmission data. The self-test instruction sequence number is used to indicate the self-test instruction issued by the central server, and the instruction retransmission data is used to indicate the retransmission interval and number of times for the self-test instruction sequence number. The self-test instruction includes specific self-test parameters for battery system testing, electrical connection testing, power regulation function testing, and protection function verification. Based on the self-test instruction information, a self-test judgment strategy is obtained; the self-test judgment result of the target power distribution cabinet is obtained through the self-test judgment strategy, and the self-test judgment result is used to indicate whether the target power distribution cabinet has failed the self-test.

4. The automatic detection system according to claim 1, characterized in that, Obtain the operation information of the corresponding distributed energy storage distribution cabinet, and obtain the operation status based on the operation information, including: The voltage, current, and temperature sensing data, as well as time information, of the corresponding distributed energy storage distribution cabinet are collected and used as a set of sensing data. The collected set of sensing data is cleaned, and extreme data exceeding the data type corresponding to each sensing data are removed before it is used as operating information. When the operating status of the distributed energy storage distribution cabinet and its multiple associated distribution cabinets are normal within the previous first preset time period and the self-test is successful, a subset of key parameters is obtained from the operating information according to the preset time interval, and the operating status is obtained by comparing the subset of key parameters with the corresponding preset range. Otherwise, all the running information within the first preset time period will be used as the basis for judgment, and the running status will be obtained by comparing the full amount of running information with the corresponding preset range.

5. The automatic detection system according to claim 4, characterized in that, All operational information within the first preset time period is used as the basis for judgment, and the operational status is obtained by comparing the full amount of operational information with the corresponding preset range, including: The system obtains range adjustment information from the central server, updates the preset range stored in the local controller corresponding to each sensor data type in the operation information based on the range adjustment information, and generates an updated preset range; it then uses the updated preset range to judge all the operation information and obtain the operation status. The range adjustment information is generated by the central server performing the following operations: The number of self-test failures of the distributed energy storage distribution cabinet and its multiple associated distribution cabinets within the previous first preset time period is counted; the proportion of the number of distribution cabinets with self-test failures to the total number of distribution cabinets used to count the number of self-test failures is taken as the abnormality ratio. The parameter threshold tightening range corresponding to the abnormality ratio is determined according to the preset adjustment rules; range adjustment information is generated based on the tightening range.

6. An automatic detection method for a power distribution cabinet, characterized in that, The method includes: Obtain the operation information of the distributed energy storage distribution cabinet corresponding to the local controller, and obtain the operation status based on the operation information. The operation status includes abnormal and normal. When the operating status is normal within a first preset time period, the identity information of the distributed energy storage distribution cabinet is obtained from the central server. When the identity information indicates that the distributed energy storage distribution cabinet is the target distribution cabinet and the operating status of multiple associated distribution cabinets of the target distribution cabinet is also normal, a self-test is performed on the target distribution cabinet. When the self-test fails, a second prompt message is generated and sent to the user equipment. The identity information is obtained by the central server based on the interval operating information and / or local prediction score sent by each distributed energy storage distribution cabinet. The methods for obtaining the identity information of distributed energy storage and distribution cabinets from the central server include: A request to obtain identity information is sent to the central server. The central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller. The interval operation information is the operation information collected in the most recent N interval periods, and all N interval periods are within the time period corresponding to a first preset duration in which the operation status is normal; and / or, The central server sends an identity information acquisition request. The central server judges the identity information based on the local prediction scores sent by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets during the first preset time period when the operating status is normal, and then feeds it back to the local controller. The central server, based on the interval operation information provided by the distributed energy storage distribution cabinet and its associated multiple distributed energy storage distribution cabinets, determines the identity information according to a preset judgment rule and then feeds it back to the local controller, including: Obtain the operational similarity between the interval operation information of each distributed energy storage distribution cabinet and its corresponding preset operation information; Obtain the actual percentage of distributed energy storage distribution cabinets with an operational similarity lower than the preset similarity; Obtain the preset relationship between the quantity ratio and the target distribution cabinet selection ratio; based on the preset relationship, obtain the target distribution cabinet selection ratio corresponding to the actual quantity ratio and use it as the selected ratio, and select one or more distributed energy storage distribution cabinets that meet the selected ratio requirements and have the lowest operating similarity as the target distribution cabinets. When the self-test fails, the method further includes: The local controller obtains the adjusted operating settings information from the central server. The adjusted operating settings information is generated by the central server based on the self-test results sent by the target power distribution cabinet, historical operating data, and the operating settings information before adjustment. The adjusted operating settings information is used to limit the charging and discharging power and temperature control strategy of the target power distribution cabinet.

7. An electronic device, characterized in that, The electronic device includes a memory and at least one processor, the memory storing a computer program, and the processor executing the computer program to enable the electronic device to perform the method as described in claim 6.

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