A fault detection method, electronic device and system of an energy storage cabinet

Abstract

This application provides a fault detection method, electronic device, and system for an energy storage cabinet. The method includes: determining whether alarm conditions are met based on electrical and thermal management information acquired by the energy storage module within the energy storage cabinet according to a preset fault judgment strategy; when alarm conditions are not met, acquiring ventilation and waterproof information of the energy storage cabinet using a second detection module, wherein the ventilation and waterproof information indicates the pressure difference of the airflow before and after the filter and the water accumulation in the drainage groove; determining an environmental health score based on the ventilation and waterproof information; and adjusting the detection sensitivity parameters of the fault judgment strategy when the environmental health score is lower than a preset threshold. Compared to related solutions that only monitor electrical parameters or external temperature, this application considers a new detection dimension regarding the environmental health status of the cabinet after waterproof performance modification, allowing for the early detection of characteristic environmental factors corresponding to ventilation and waterproof factors before electrical faults become apparent.

Description

Technical Field

[0001] This application relates to the technical field of power supply and distribution equipment testing, and in particular to a fault detection method, electronic equipment and system for energy storage cabinets. Background Technology

[0002] An energy storage cabinet is an integrated electrical energy storage device, typically packaged in a standardized cabinet. Internally, it integrates battery modules, a battery management system (BMS), a temperature control system, a fire suppression system, a power conversion system (PCS), and a monitoring and communication unit to store, release, and manage electrical energy. With the rapid development of electrochemical energy storage, the safe and stable operation of energy storage cabinets is of paramount importance.

[0003] Existing fault detection technologies are mostly adapted to conventional energy storage cabinets, failing to consider new energy storage cabinets designed to improve waterproofing, whose cabinet structures differ from general distribution cabinets. Therefore, fault detection and judgment do not take into account the impact of structural modifications made to the energy storage cabinet to improve waterproofing, resulting in a single detection dimension. Taking the energy storage cabinet power abnormality classification fault detection method and system disclosed in patent CN202410799050.4 as an example, it achieves effective identification of system fault types in the first instance by acquiring basic parameters such as cell voltage, temperature, and system current during each charge-discharge cycle, combined with basic system characteristics such as cell charge-discharge voltage and SOC mapping set, system balancing capability, and normal cell attenuation factor. However, its system fault judgment only considers basic parameters such as cell voltage, temperature, and system current, without considering the new detection dimension of the cabinet's environmental health status after waterproofing modifications. Therefore, there is an urgent need to provide a fault detection method, electronic device, and system for energy storage cabinets. Summary of the Invention

[0004] In view of the above-mentioned problems in related technologies, this application provides a fault detection method, electronic equipment and system for energy storage cabinets.

[0005] The objective of this application is achieved through the following technical solution:

[0006] In a first aspect, this application provides a fault detection method for an energy storage cabinet, wherein the ventilation and waterproof door of the energy storage cabinet includes a second front door and a first front door movably connected to the front side of the second front door, the air inlet of the second front door is provided with a filter, and the lower sides of the first front door are provided with drainage grooves; the method includes:

[0007] S200, based on the preset fault judgment strategy, determine whether the alarm conditions are met according to the electrical and thermal management information obtained by the energy storage module in the energy storage cabinet; when the alarm conditions are not met, use the second detection module to obtain the ventilation and waterproof information of the energy storage cabinet, the ventilation and waterproof information is used to indicate the pressure difference of the airflow before and after the filter and the water accumulation in the drainage groove;

[0008] S400, determine the environmental health score based on the ventilation and waterproofing information; when the environmental health score is lower than a preset threshold, adjust the detection sensitivity parameter of the fault judgment strategy.

[0009] In a second aspect, this application also 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 one of the first aspects.

[0010] Thirdly, this application also provides an energy storage cabinet system, including an energy storage cabinet and the electronic equipment described in the second aspect; the energy storage cabinet includes an energy storage cabinet body and a ventilation and waterproof door, the energy storage cabinet body being used to form a receiving space; the ventilation and waterproof door includes:

[0011] The second front cabinet door has an air inlet hole on its surface, and a filter is installed inside the air inlet hole; a differential pressure sensor is installed at the position of the filter to obtain the differential pressure data of the airflow before and after the filter.

[0012] A first front cabinet door is movably connected to the front side of the second front cabinet door. An air intake grille is provided at the bottom of the surface of the first front cabinet door, and drainage grooves are provided on both sides of the lower side of the first front cabinet door. Water level sensors are installed in these drainage grooves to acquire the water level height and the corresponding duration of water accumulation.

[0013] This application provides a fault detection method, electronic device, and system for energy storage cabinets. Considering the structural improvements made to energy storage cabinets in rainy weather due to the waterproofing drawbacks of the air inlet and outlet vents, a dynamic fault detection mechanism driven by environmental perception based on the cabinet door is constructed. This mechanism uses condition-triggered secondary detection and adaptive adjustments to the detection strategy through environmental health quantification. When environmental health deteriorates, the detection sensitivity parameters of the fault judgment strategy can be increased (e.g., lowering the alarm threshold, shortening the judgment time window, increasing the sampling frequency). Therefore, compared to related solutions that only monitor electrical parameters or external temperature, this solution considers the new detection dimension of the cabinet's environmental health status after waterproofing modifications. Before electrical faults become apparent, it detects characteristic environmental factors corresponding to ventilation and waterproofing (filter blockage, water accumulation) in advance and uses this information to adjust the fault judgment strategy. Attached Figure Description

[0014] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0015] Figure 1 This is a flowchart illustrating a fault detection method provided in an embodiment of this application.

[0016] Figure 2 This is a flowchart illustrating another fault detection method provided in an embodiment of this application.

[0017] Figure 3 This is a schematic diagram of the process for obtaining ventilation and waterproofing information of the energy storage cabinet according to an embodiment of this application.

[0018] Figure 4 This is a schematic diagram of the process for updating detection sensitivity parameters provided in the embodiments of this application. Detailed Implementation

[0019] The present application will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments. The implementation process of the present application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation procedures, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application and not for limiting the scope of protection of the present application.

[0020] To facilitate understanding of the technical solution of this application, the technical field involved in this application will be described first:

[0021] Referring to the ventilation and waterproof door disclosed in application number CN202321959917.5, this design addresses the problem of insufficient waterproofing in energy storage cabinets during rainy weather, where rainwater easily enters the cabinet and leads to inadequate waterproofing. The ventilation and waterproof door includes an air inlet, a filter, a drainage groove, an air outlet, and a waterproof ramp. By positioning the air inlet higher than the air inlet grille and filter, rainwater flows downwards and does not enter the cabinet. The waterproof ramp guides rainwater away, preventing it from entering the air outlet. Combined with a fan to guide airflow, this ensures internal dryness. This achieves a high level of waterproofing for the energy storage cabinet, preventing rainwater from entering the interior, ensuring the safety of electrical components, and improving the overall waterproof performance of the energy storage cabinet.

[0022] The fault detection method for energy storage cabinets protected in this application can be applied to energy storage cabinets including those with ventilated and waterproof doors disclosed in CN202321959917.5. Specifically, the energy storage cabinet includes a main body and a ventilated and waterproof door. The main body forms a storage space. This storage space can be divided into multiple storage areas, namely a management module storage area, a temperature control module storage area, a fire protection module storage area, and an energy storage module storage area. The management module storage area houses the energy management module (EMS), battery management module (BMS), and power conversion module (PCS). The temperature control module, fire protection module, and energy storage module storage areas house the temperature control module, fire protection module, and energy storage module. Each energy storage module includes several battery cells; the temperature control module includes a liquid chiller and some heat exchange pipes located in the energy storage module storage area. The power grid is electrically connected to the energy storage module through the PCS. The EMS is communicatively connected to the PCS, BMS, and temperature control module via RS-485 communication. The BMS is also communicatively connected to the energy storage module via RS-485 communication.

[0023] Ventilated and waterproof doors include:

[0024] An energy storage cabinet base is provided, and the upper side of the energy storage cabinet base is connected to the main body of the energy storage cabinet. A top plate is provided on the upper side of the main body of the energy storage cabinet. A second front cabinet door is movably connected to the front side of the main body of the energy storage cabinet, and a second rear cabinet door is connected to the rear side of the main body of the energy storage cabinet. An air outlet grille is provided on the top surface of the second rear cabinet door.

[0025] All air inlets are located on the surface of the second front cabinet door, and filters are installed inside the air inlets.

[0026] The first front cabinet door is connected to the front side of the second front cabinet door, and an air inlet grille is provided at the bottom of the surface of the first front cabinet door. Drainage grooves are provided on both sides of the lower side of the first front cabinet door.

[0027] The first rear cabinet door is connected inside the main body of the energy storage cabinet, near the second rear cabinet door. Several air vents are located on the top surface of the first rear cabinet door, and fans are installed inside the vents. A ventilation cavity is formed between the first and second front cabinet doors, and the horizontal position of the filter element is higher than the horizontal position of the air inlet grille. The filter element can be a filter cotton block. Several waterproof inclined plates are connected to the side of the first rear cabinet door near the air vents. The waterproof inclined plates cover the sides of the air vents.

[0028] The filter element is equipped with a differential pressure sensor to obtain the pressure difference data of the airflow before and after the filter element. It can be assumed that when the pressure difference continues to increase or fluctuates abnormally, the filter element may be clogged.

[0029] The drainage trough is equipped with a water level sensor to obtain the water level height and the corresponding duration of water accumulation, which is then used as water accumulation data. When the drainage trough becomes clogged, the water level may exceed a threshold after a certain period of accumulation. The drainage trough can be connected to external drainage facilities via drain pipes or similar means.

[0030] Furthermore, the aforementioned energy storage cabinet is equipped with electronic devices to integrate the functions of the energy management module and acquire sensor data from various sensors within the cabinet. These devices can directly control the hardware within the cabinet, such as starting or stopping the liquid chiller and controlling circuit breakers; simultaneously, they monitor the equipment's operating status, promptly detect faults, and take appropriate measures (such as alarms and power cut-offs). They can also communicate with an edge server via a communication module, for example, uploading collected data and operating status to the edge server while receiving instructions.

[0031] The fault detection method for the energy storage cabinet provided in this application can be applied to the aforementioned energy storage cabinet, or energy storage cabinets with a double front door structure similar to the aforementioned energy storage cabinet. The fault detection method will be described first, followed by a description of the electronic equipment and its system.

[0032] Example 1.

[0033] See Figure 1 This embodiment provides a fault detection method. The ventilation and waterproof door of the energy storage cabinet includes a second front door and a first front door movably connected to the front side of the second front door. The air inlet of the second front door is equipped with a filter, and drainage grooves are provided on both sides of the lower side of the first front door. The method includes:

[0034] S200, based on the preset fault judgment strategy, determine whether the alarm conditions are met according to the electrical and thermal management information obtained by the energy storage module in the energy storage cabinet; when the alarm conditions are not met, use the second detection module to obtain the ventilation and waterproof information of the energy storage cabinet, the ventilation and waterproof information is used to indicate the pressure difference of the airflow before and after the filter and the water accumulation in the drainage groove;

[0035] Specifically, the electrical and thermal management information of the energy storage module (such as voltage, current, and temperature) is continuously monitored, and a fault judgment is made based on a preset fault judgment strategy. A negative triggering mechanism is used here, which can be understood as the secondary detection module being activated only when the primary detection fails to trigger an alarm.

[0036] S400, determine an environmental health score based on the ventilation and waterproofing information; when the environmental health score is lower than a preset threshold, adjust the detection sensitivity parameters of the fault judgment strategy. The environmental health score is calculated based on the ventilation and waterproofing information, and the calculated score is compared with a preset threshold, taking into account the permeability of the filter (affecting heat dissipation) and drainage efficiency (affecting waterproofing). When the score is lower than the threshold, the environmental health is deemed insufficient.

[0037] The technical solution provided in this embodiment takes into account the structural improvements made to the energy storage cabinet due to the waterproof shortcomings of the air inlet and outlet vents during rainy weather. It constructs a dynamic fault detection mechanism driven by environmental perception based on the cabinet door. This mechanism uses condition-triggered secondary detection and adaptive adjustments to the detection strategy through environmental health quantification. When environmental health deteriorates, the detection sensitivity parameters of the fault judgment strategy can be increased (e.g., lowering the alarm threshold, shortening the judgment time window, increasing the sampling frequency, etc.).

[0038] Therefore, compared to related solutions that only monitor electrical parameters or external temperature, this solution can detect the characteristic environmental factors (filter blockage, water accumulation) corresponding to ventilation and waterproofing factors in advance before electrical faults become apparent, and use this information to adjust the fault diagnosis strategy.

[0039] See Figure 2 In some embodiments, the method further includes:

[0040] S100: Obtains a fault detection command through timed triggering, event triggering, or manual triggering; Based on the fault detection command, sends the electrical and thermal management information of the energy storage cabinet obtained by the first detection module to the edge server; The electrical and thermal management information includes multiple health data collected from the battery.

[0041] Timed triggering, for example, is deterministic sampling based on a preset clock cycle, ensuring a minimum guaranteed monitoring frequency for the energy storage cabinet's status and avoiding missed status checks due to conditional silence. Event triggering, for example, is condition-driven sampling based on anomaly modes. When a certain health data (such as a sudden temperature rise or voltage jump) in the electrical and thermal management information of other energy storage cabinets in the same area corresponding to the edge server exceeds the trigger threshold, the detection process of other energy storage cabinets is immediately initiated. Manual triggering, for example, is manual sampling based on external intervention by maintenance personnel, which sends instructions to the edge server through user equipment. Logically, any of these three triggering mechanisms can generate a fault detection instruction upon activation.

[0042] As an example, once the trigger command takes effect, the first detection module (i.e., the Battery Management System (BMS) and associated sensor network) is immediately invoked to synchronously collect electrical and thermal management information from the energy storage cabinet. After collection, the packaged electrical and thermal management information is sent to the edge server. Uploading is done instantly under trigger commands (timed, event-triggered, or manually triggered), rather than through polling or batch delays, ensuring data timeliness. The uploaded content is a structured data packet containing multiple health data points; the edge server can directly decouple and input this data into the model or perform calculations upon receiving it.

[0043] Compared to related technologies that often employ a single-cycle upload mode, which wastes communication resources under normal conditions (meaningless high-frequency uploads), this solution uses an event-triggered mechanism to achieve detection and upload upon meeting certain conditions. By synchronously acquiring multiple health data points through the first detection module, rather than reporting single parameters, it ensures that the edge server receives a multi-dimensional, correlated snapshot of electrical and thermal management information. This data packaging method avoids the time asynchrony issues caused by multiple uploads, enabling edge-side models or calculation strategies to perform multi-parameter correlation analysis based on the coupling relationships between parameters (such as voltage-temperature correlation, internal resistance-temperature difference co-change), significantly reducing false alarms and false negatives caused by data silos and improving the confidence of the detection results.

[0044] See Figure 3 In some embodiments, S200 includes:

[0045] S201, when at least one of the health data collection items reaches its corresponding warning value, determine whether each of the health data collection items is within its respective warning range within a preset time period starting from the current time or within a preset time period before the current time, and the warning value of each of the health data collection items is lower than its warning range; otherwise, re-execute S100.

[0046] S202, when the health data that reaches its corresponding warning value is within the warning range within a preset time period, and at least one other health data is also within the warning range within a preset time period, the fault detection result of the energy storage cabinet is considered to meet the alarm conditions corresponding to the fault judgment strategy, and an alarm information is sent to the user equipment using the edge server; this process is understood as the first-level detection process.

[0047] S203, if other health data are not within the warning range within a preset time period, it is considered that the cabinet environment detection requirements are met, and the ventilation and waterproof information of the energy storage cabinet is obtained using the second detection module; this process is understood as a secondary detection process.

[0048] This can be understood as follows: when any health data collected by the first detection module (such as temperature or voltage difference) reaches its preset warning value, it does not immediately trigger an alarm, but instead initiates a preset time window. This warning value is a sensitive threshold below the lower limit of the warning range, and its intended purpose is to detect abnormal signs in advance, rather than confirming a fault. At this point, it enters a pending confirmation state to continuously track the parameter and related parameters, rather than making an instantaneous judgment. This adjusts the alarm logic from a single-point threshold exceeding trigger to a composite mode of event-driven and time-based verification.

[0049] As an example, performing multi-parameter correlation verification within the observation window requires, firstly, that the primary parameter triggering the warning must remain within its warning range (i.e., not returning to normal) to eliminate transient pulse interference; secondly, it requires that at least one other independent health data acquisition (such as voltage range verification within a temperature-triggered window) also enter its respective warning range within a certain time. This logic constitutes a dual-condition criterion (i.e., continuous abnormality of the primary parameter and cross-verification of auxiliary parameters). Only when two (or more) conditions are simultaneously met is the fault considered to have multi-dimensional consistency, rather than an isolated, potentially falsely reported anomaly from a single sensor.

[0050] If the above verification passes, the fault detection is deemed to meet the alarm conditions corresponding to the fault judgment strategy. At this point, the edge server immediately sends alarm information to the user device. In this case, the diagnostic decision and alarm action are completed entirely in a closed loop at the edge, without waiting for confirmation from the cloud (cloud server), thus ensuring ultra-low latency emergency response. The alarm information includes, for example, the fault type, suspected location, trigger parameter combination, and timestamp, providing maintenance personnel with structured and actionable decision-making basis.

[0051] If the auxiliary parameters do not fall within the warning range within the window period (i.e., only a single parameter is abnormal), it is not directly determined to be fault-free. Instead, it is determined that the electrical parameter evidence is insufficient, and the second detection module is automatically activated to obtain the ventilation and waterproof information of the energy storage cabinet (such as filter pressure difference ΔP, cabinet water volume H). This transforms an incomplete initial electrical assessment into a deep detection of environmental parameters, forming a closed-loop feedback detection mechanism from insufficient initial electrical assessment to supplementary environmental assessment. The activation of the second detection module is conditional and triggered on demand, avoiding the resource consumption of continuous transmission between information continuously monitored by the edge server, and realizing the precise allocation of detection resources.

[0052] Meanwhile, to achieve hierarchical and continuous anomaly determination of electrical thermal management information in the fault judgment strategy, a hierarchical relationship between warning values ​​and warning ranges is defined for health data acquisition (including cell temperature, individual cell voltage range, internal resistance, etc.). The warning value serves as the trigger threshold for activating the preset time window, and can be set to 80%-90% of the normal operating upper limit of the corresponding health data acquisition. When any data reaches the warning value, the time window is activated and continuous monitoring begins, but an alarm is not directly triggered at this time. The warning range serves as the determination interval for confirming the persistence of the anomaly within the time window.

[0053] For example, for the cell temperature (as the first priority parameter), a warning value of 50℃ can be set, and a 20-second time window can be activated when the warning value is triggered. For the cell temperature (as the second priority parameter), the warning range is [40℃, 45℃), and a fault protection threshold of 45℃ is set to directly trigger protection without time window verification.

[0054] Therefore, through continuous observation within a time window and verification using multi-parameter correlation, anomalies must possess temporal continuity and parameter coupling, effectively filtering out isolated, transient pseudo-anomaly signals and improving the signal-to-noise ratio and reliability of fault alarms. The current solution relies not only on electrical parameters for fault diagnosis but is also prone to missed detections when electrical characteristics are not obvious (such as early thermal runaway or chronic insulation degradation). The technical solution provided in this embodiment automatically extends the detection dimension when electrical parameter evidence is insufficient, introducing environmental parameters such as ventilation and waterproofing cabinets for compensation verification. It provides a two-tier detection architecture of primary detection and compensation detection, capable of capturing early fault signs where electrical parameters lag but environmental parameters are preceding (such as filter blockage leading to heat dissipation degradation), thus advancing the fault detection window. The data acquired by the second detection module (environmental parameter acquisition) is not continuously sent to the edge server but is only acquired on demand when the initial electrical judgment conditions are not met, avoiding data transmission and storage pressure.

[0055] In some embodiments, when at least one of the health data collection items reaches its corresponding warning value, it is determined whether each of the health data collection items is within its respective warning range within a preset time period from the current time, or within a preset time period before the current time, and the warning value of each of the health data collection items is lower than its warning range, including:

[0056] The types of health data collected are prioritized and a hierarchical structure with at least a first priority and a second priority is constructed. For example, the first priority is temperature parameters (cell temperature), and the second priority is voltage consistency parameters (such as individual cell voltage range). The prioritization is based on the strength of the correlation with thermal runaway and electrical safety risks of the energy storage cabinet, or on the prioritization based on the experience of the staff.

[0057] When a parameter of a higher priority (e.g., first priority) reaches its warning value, a preset time window is activated. Within this time window, the system checks, in descending order of priority, whether other health data in the next lower priority (e.g., second priority, third priority) falls within its warning range. If health data in any priority is also detected to be within its warning range, the system stops checking subsequent priorities, indicating that the alarm condition is met. If no health data meeting the condition is found after the entire process, the alarm condition is not met. If the alarm condition is not met, the system proceeds to the step of obtaining ventilation and waterproofing information using the second detection module.

[0058] In this case, when the health data collected reaches its corresponding warning value and is within the warning range for a preset time period, and at least one other health data is also within the warning range for a preset time period, the fault detection result of the energy storage cabinet is considered to meet the alarm conditions corresponding to the fault judgment strategy, which can be understood as:

[0059] When health data that reaches its corresponding warning value is within the warning range for its corresponding first preset duration, and at least one other health data is also within the warning range for its corresponding second preset duration, the fault detection result of the energy storage cabinet is considered to meet the alarm conditions corresponding to the fault judgment strategy. It can be considered that each type of health data corresponds to a duration pair consisting of a first preset duration and a second preset duration, and the first preset duration in the duration pair is shorter than the second preset duration. In this case, an asynchronous confirmation mechanism for rapid response of primary parameters and in-depth verification of secondary parameters is provided through differentiated time window configuration.

[0060] As an example, the same type of health data collection provides a first preset duration (Δt1) and a second preset duration (Δt2). The first preset duration of this health data collection is activated when it serves as the primary trigger parameter (i.e., the parameter that first reaches the warning value). A short duration (e.g., 10-20 seconds) ensures rapid confirmation of the parameter's persistent abnormality, avoiding prolonged observation even when obvious signs of deterioration have appeared. When the second preset duration of this health data collection serves as an auxiliary verification parameter (i.e., other parameters that need cross-verification after being triggered by the primary parameter), a long duration (e.g., 40-60 seconds) requires that the parameter must remain abnormal for a longer period to be considered valid auxiliary evidence, utilizing the time integration effect to filter out instantaneous fluctuations.

[0061] In some embodiments, the second detection module includes a differential pressure sensor for a filter element disposed in the air inlet and a water level sensor disposed in the drainage structure at the bottom of the energy storage cabinet; the ventilation and waterproof information includes a set of airflow differential pressure data within a predetermined time period obtained by the differential pressure sensor and a set of water accumulation status data within a predetermined time period obtained by the water level sensor.

[0062] The step of determining the environmental health score based on the ventilation and waterproofing information includes: inputting the airflow pressure difference data set and the water accumulation status data set into a preset first environmental scoring model, obtaining the score and using it as the environmental health score.

[0063] In related technologies, the single-point threshold method is mostly adopted, which is susceptible to instantaneous fluctuations in air flow and interference from water level measurement noise, resulting in false alarms. In this solution, through the time series accumulation of data groups, dynamic feature analysis is built into the model (such as calculating the 70-second change rate of ΔP and the cumulative integral of H), which can accurately identify the differences between progressive degradation and instantaneous disturbances. The model can also introduce a non-linear saturation function to cap the risk of extreme anomalies and avoid score distortion caused by single spike data.

[0064] In some embodiments, the first environmental score model includes a two-level architecture of a sub-evaluation module and a fusion module. The determination of the environmental health score based on the ventilation and waterproof information may further include:

[0065] Input the two types of data in the ventilation and waterproof information into the corresponding sub-evaluation functions respectively to obtain the sub-scores of each dimension; through a weighted fusion algorithm, synthesize the sub-scores of each dimension into the environmental health score.

[0066] Among them, the sub-evaluation function is a non-linear function for mapping physical quantities to risk indices; for data reflecting backlog or cumulative effects, its corresponding sub-evaluation function is a saturation-type non-linear function; for characteristic data reflecting instantaneous mutations, its corresponding sub-evaluation function includes a gain term for amplifying risks. The saturation-type non-linear function includes the Sigmoid function; the gain term for amplifying risks is configured to take the product of the characteristic data and its change rate as part of the function input.

[0067] In this case, the first environmental score model can be constructed in the form of a lightweight neural network (Edge-NN). The sub-evaluation module includes a dual-branch input layer, and the differential pressure data group (ΔP sequence) and the water accumulation data group (H sequence) are respectively input into two parallel one-dimensional convolutional sub-networks (1D-CNN) or LSTM sub-networks; each sub-network outputs a risk latent vector (such as the blockage risk vector output by the differential pressure branch and the water ingress risk vector output by the water accumulation branch), corresponding to the R of P1 in the subsequent example i Mapping.

[0068] The attention fusion layer of the fusion module can calculate Wi using the weight attention mechanism, and weight and splice the two risk latent vectors; through a fully connected layer and Sigmoid activation, output the final score S. The environmental health score S is a dimensionless value, normalized to the interval [0,1], where S = 1 indicates the optimal environmental state. The preset threshold is set to 0.6 - 0.8, preferably 0.75. When S < St, it is determined that the environmental health is insufficient, and the detection sensitivity parameter is adjusted.

[0069] As an example, based on the original environmental data (ventilation and waterproof information), calculating the environmental health score (S) includes:

[0070] P1, mapping physical quantities to risk coefficients. For each type of raw environmental data, based on its corresponding preset mapping rule, it is converted into a dimensionless risk coefficient (R). i ), where i represents the data category; the mapping rule is predefined according to the engineering safety threshold or normal operating range of the physical quantity, so that when the physical quantity is in the optimal range, the risk coefficient approaches 0, and when the physical quantity approaches or exceeds the danger threshold, the risk coefficient approaches 1 or is greater than 1.

[0071] P2 is used for risk status assessment and cumulative effect modeling. For physical quantities characterizing gradual deterioration, their continuously collected historical sequences are input into the state accumulation model, and the cumulative risk correction factor (C) is output. i The state accumulation model is configured to perform a weighted integral over the time when the physical quantity exceeds a slight threshold, and its output value increases non-linearly with the integral time.

[0072] P3, perform mutation risk amplification. For physical quantities characterizing sudden anomalies, calculate their short-term rate of change; when the absolute value of the rate of change exceeds a preset mutation threshold, generate a risk amplification coefficient (A). i The risk amplification factor is positively correlated with the absolute value of the rate of change.

[0073] P4, perform multi-risk fusion scoring. This involves assigning risk coefficients (R0) to various data types. i ), cumulative risk correction factor (C) i ) and risk amplification factor (A) i Substituting the values ​​into a preset composition function, the environmental health score (S) is calculated; the composition function is expressed as: Among them, W i α is the preset weight for the i-th type of data. i and β i are the influence coefficients of the cumulative factor and the mutation factor, respectively, and f is the final conversion function that maps the comprehensive risk value to a predetermined scoring range.

[0074] In practical applications, W i The value of ΣW is a positive number not greater than 0.8. i =1. For differential pressure data (ΔP), W i ∈ (0.4, 0.6), α i ∈ (0.3, 0.5), β i ∈ (0.5~0.8). This is because filter clogging is mostly gradual, with a moderate cumulative effect, but sudden changes may indicate damage from external forces.

[0075] Regarding the water accumulation data (H), W i ∈ (0.4, 0.6), α i∈ (0.5, 0.8), β i ∈ (1, 2). This is because it is considered that water accumulation has a continuous cumulative hazard, and a sudden rise in water level indicates that a serious flooding accident will occur.

[0076] See Figure 4 In some embodiments, adjusting the detection sensitivity parameter of the fault judgment strategy when the environmental health score is lower than a preset threshold includes:

[0077] S301, obtain the interval identifier of the difference between the sample environment health score and the preset threshold, as well as the detection sensitivity correspondence between the sample detection sensitivity parameters;

[0078] S302, based on the detection sensitivity correspondence, obtain the sample detection sensitivity parameter corresponding to the environmental health score, and use it to update the detection sensitivity parameter.

[0079] In other words, in the technical solution provided in this embodiment, the score difference of the environmental health score is not directly used for calculation, but rather it is categorized into an interval. Therefore, a detection sensitivity correspondence table can be pre-stored on the edge server. This table uses the interval identifier as the primary key and directly maps it to the sample detection sensitivity parameter. When the score difference falls into a certain interval, the corresponding sample detection sensitivity is retrieved from the table and updated.

[0080] This allows complex dynamic adjustment logic to be shifted from runtime calculation to offline configuration. In practical applications, there is no need to solve the adjustment function in real time at runtime; only a single memory address is required, making it suitable for resource-constrained edge server scenarios. The mapping relationship can be maintained and upgraded independently; for example, policy optimization can be achieved by updating the table via OTA without refactoring the firmware, making it more convenient.

[0081] As an example, in the dynamic optimization process of the fault diagnosis strategy, the first step is to determine whether the environmental health score S is lower than the preset environmental risk threshold (i.e., the preset threshold) S. t And generate decision instructions. When S t This indicates an increased environmental risk. The sensitivity parameters of the fault diagnosis strategy are adjusted. For example, the temperature warning value is adjusted from 45°C to 40°C; the observation window is reduced from 30 seconds to 20 seconds. This method achieves the mapping S→f(S)→P, where P represents the strategy parameter. The environmental health score S serves as a feedback variable, directly controlling the system detection sensitivity P.

[0082] ​In some embodiments, the edge server is connected to the user equipment via a short-range communication module, the edge server is connected to the cloud server via a remote communication module, and the cloud server is connected to the user equipment via a remote communication module; before sending alarm information to the user equipment using the edge server, the method includes:

[0083] Determine whether the edge server has established a near-field communication connection with the user equipment. If the connection is established, send alarm information to the user equipment via the edge server; otherwise, send alarm information to the user equipment via the cloud server.

[0084] The edge server establishes a low-latency, high-bandwidth, and low-power direct connection with user devices (such as tablets) via a local area communication module (such as WiFi, Bluetooth, ZigBee, LoRa, etc.). The edge server establishes a long-distance uplink connection with the cloud server via a remote communication module (such as 4G / 5G, Ethernet, fiber optic, etc.). Before sending an alarm, the edge server performs a connection status detection step to check whether the local area communication module has established a valid communication connection with the user device. If the local connection is established, the alarm information is pushed directly through this link without going through the cloud server. If the local connection is not established (e.g., the user device leaves the site, the local area module malfunctions, or the signal is blocked), it automatically switches to the remote channel, uploading the alarm information to the cloud server first, and then the cloud server forwards it to the user device, thus forming a seamless switching fault-tolerant mechanism.

[0085] Compared to related technologies that mostly use a fixed cloud reporting mode, alarm information is completely lost when the cloud server is overloaded, making it impossible for maintenance personnel to detect critical faults. This solution prioritizes near-field direct connection strategies, placing routine scenarios (where user equipment is near the site) first, thus reducing the possibility of cloud server overload.

[0086] In practical applications, if a local connection is established, it indicates that maintenance personnel are on-site, and detailed alarm information can be pushed (such as alarm information including fault type, suspected location, trigger parameter combination, and timestamp). If the local connection is lost, it may mean that maintenance personnel have left the site, and simplified alarm information (such as alarm information only including suspected location and timestamp) is pushed to the user's device via the cloud server. This scene-aware, differentiated push improves the targeting and efficiency of human-computer interaction and reduces response delays caused by information overload.

[0087] Example 2.

[0088] This embodiment provides an electronic device, the specific embodiment of which is consistent with the embodiment described in Embodiment 1 above and the technical effects achieved are the same, and some contents will not be repeated.

[0089] 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.

[0090] Example 3.

[0091] This embodiment provides an energy storage cabinet system, including an energy storage cabinet and the electronic equipment described in Embodiment 2; the energy storage cabinet includes a main body and a ventilation and waterproof door, the main body of which forms a receiving space; the ventilation and waterproof door includes:

[0092] The second front cabinet door has an air inlet hole on its surface, and a filter is installed inside the air inlet hole; a differential pressure sensor is installed at the position of the filter to obtain the differential pressure data of the airflow before and after the filter.

[0093] The first front cabinet door is movably connected to the front side of the second front cabinet door. An air inlet grille is provided at the bottom of the surface of the first front cabinet door, and drainage grooves are provided on both sides of the lower side of the first front cabinet door. The drainage grooves are equipped with water level sensors for obtaining the water level height and the corresponding water accumulation duration.

[0094] It should be noted that in the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple. It is worth noting that "at least one" can also be interpreted as "one or more".

[0095] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are configured to distinguish similar objects and are not necessarily configured to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0096] This application describes the invention from the perspectives of purpose, performance, progress, and novelty, and it meets the functional enhancement and use requirements emphasized by the Patent Law. The above description and drawings are merely preferred embodiments of this application and are not intended to limit this application. Therefore, all structures, devices, features, etc., that are similar to or identical to those of this application, i.e., all equivalent substitutions or modifications made in accordance with the scope of this patent application, shall fall within the scope of protection of this patent application.

Claims

1. A fault detection method for an energy storage cabinet, wherein the ventilation and waterproof door of the energy storage cabinet includes a second front door and a first front door movably connected to the front side of the second front door, the air inlet of the second front door is provided with a filter, and drainage grooves are provided on both sides of the lower side of the first front door; characterized in that, The method includes: S200, based on the preset fault judgment strategy, determine whether the alarm conditions are met according to the electrical and thermal management information obtained by the energy storage module in the energy storage cabinet; when the alarm conditions are not met, use the second detection module to obtain the ventilation and waterproof information of the energy storage cabinet, the ventilation and waterproof information is used to indicate the pressure difference of the airflow before and after the filter and the water accumulation in the drainage groove; S400, determine the environmental health score based on the ventilation and waterproofing information; when the environmental health score is lower than a preset threshold, adjust the detection sensitivity parameter of the fault judgment strategy.

2. The fault detection method according to claim 1, characterized in that, The method further includes: S100: Obtains a fault detection command through timed triggering, event triggering, or manual triggering; Based on the fault detection command, sends the electrical and thermal management information of the energy storage cabinet obtained by the first detection module to the edge server; The electrical and thermal management information includes multiple health data collected from the battery.

3. The fault detection method according to claim 2, characterized in that, S200 includes: When at least one of the health data points reaches its corresponding warning value, determine whether each of the health data points is within its respective warning range within a preset time period from the current time or within a preset time period before the current time, and the warning value of each of the health data points is a sensitive threshold lower than the lower limit of its respective warning range; otherwise, re-execute S100. When the health data collected reaches its corresponding warning value and is within the warning range within a preset time period, and at least one other health data is also within the warning range within a preset time period, the fault detection result of the energy storage cabinet is considered to meet the alarm conditions corresponding to the fault judgment strategy, and an alarm message is sent to the user equipment using the edge server. If other health data are not within the warning range within a preset time period, it is considered that the cabinet environment detection requirements are met, and the ventilation and waterproof information of the energy storage cabinet is obtained using the second detection module.

4. The fault detection method according to claim 3, characterized in that, When at least one of the health data points reaches its corresponding warning value, it is determined whether each of the health data points falls within its respective warning range within a preset time period from the current time, or within a preset time period prior to the current time, including: The types of health data collected are prioritized and a hierarchical structure containing at least a first priority and a second priority is constructed. When a parameter of a higher priority is detected to have reached its warning value, a time window of the preset duration is started. Within this time window, the system checks whether other health data collected in the next lower priority level is within its warning range, in descending order of priority. If health data collected in any priority level is detected to be within its warning range, the system stops judging the subsequent priorities and determines that the alarm condition is met. If no health data that meets the condition is found after the entire process, the system determines that the alarm condition is not met.

5. The fault detection method according to claim 1, characterized in that, The second detection module includes a differential pressure sensor for the filter element installed in the air inlet, and a water level sensor installed in the drainage structure at the bottom of the energy storage cabinet; the ventilation and waterproof information includes a set of airflow differential pressure data within a predetermined time period obtained by the differential pressure sensor, and a set of water accumulation status data within a predetermined time period obtained by the water level sensor. The step of determining the environmental health score based on the ventilation and waterproofing information includes: inputting the airflow pressure difference data set and the water accumulation status data set into a preset first environmental scoring model, obtaining the score and using it as the environmental health score.

6. The fault detection method according to claim 5, characterized in that, When the environmental health score is lower than a preset threshold, the detection sensitivity parameter of the fault judgment strategy is adjusted, including: Obtain the interval identifier of the difference between the sample environmental health score and the preset threshold, as well as the detection sensitivity correspondence between the sample detection sensitivity parameters; Based on the detection sensitivity correspondence, the sample detection sensitivity parameter corresponding to the environmental health score is obtained, and the detection sensitivity parameter is updated using it.

7. The fault detection method according to claim 5, characterized in that, The first environmental scoring model includes a two-level architecture of a sub-assessment module and a fusion module. The step of determining the environmental health score based on the ventilation and waterproofing information further includes: inputting the two types of data in the ventilation and waterproofing information into the corresponding sub-assessment functions to obtain sub-scores for each dimension; and synthesizing the sub-scores for each dimension into the environmental health score through a weighted fusion algorithm.

8. The fault detection method according to claim 3, characterized in that, The edge server is connected to the user equipment via a short-range communication module, the edge server is connected to the cloud server via a remote communication module, and the cloud server is connected to the user equipment via a remote communication module. Before sending alarm information to user equipment using an edge server, the method includes: Determine whether the edge server has established a near-field communication connection with the user equipment. If the connection is established, use the edge server to send alarm information to the user equipment. Otherwise, an alarm message is sent to the user equipment via the cloud server.

9. 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 implement the method as described in any one of claims 1 to 8.

10. An energy storage cabinet system, comprising an energy storage cabinet, characterized in that, It also includes the electronic device as described in claim 9; the energy storage cabinet includes an energy storage cabinet body and a ventilation and waterproof door, the energy storage cabinet body being used to form a receiving space; the ventilation and waterproof door includes: The second front cabinet door has an air inlet hole on its surface, and a filter is installed inside the air inlet hole; a differential pressure sensor is installed at the position of the filter to obtain the differential pressure data of the airflow before and after the filter. The first front cabinet door is movably connected to the front side of the second front cabinet door. An air inlet grille is provided at the bottom of the surface of the first front cabinet door, and drainage grooves are provided on both sides of the lower side of the first front cabinet door. The drainage grooves are equipped with water level sensors for obtaining the water level height and the corresponding water accumulation duration.

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