A safety management method and system for a hydrogen energy storage system

Abstract

The present invention relates to a safety management method and system for a hydrogen energy storage system, and relates to the technical field of energy storage systems. The method includes: obtaining the operation status data of the hydrogen energy storage system; extracting global features from the operation status data; extracting energy propagation features from the operation status data based on the energy transmission path, extracting hydrogen gas propagation features from the operation status data based on the hydrogen gas transmission path, and fusing the energy propagation features and the hydrogen gas propagation features to obtain local features; fusing the local features and the global features to obtain enhanced global features; inputting the enhanced global features and the local features into a preset risk assessment model to obtain a risk assessment result; and generating an alarm message based on the risk assessment result. The present invention enhances the global features by introducing local features based on the two core physical processes of energy propagation and hydrogen gas propagation in the hydrogen energy storage system, improving the risk assessment accuracy and response speed.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage, and particularly to a safety management method and system for a hydrogen energy storage system. Background Art

[0002] A hydrogen energy storage system (HESS) is a core technology for large-scale application of renewable energy, which realizes efficient conversion of electric energy - hydrogen energy - electric energy through electrolytic water hydrogen production, high-pressure hydrogen storage and fuel cell power generation. Its core equipment includes electrolyzers, compressors, hydrogen storage tanks and fuel cells, etc., involving multi-physical field coupling processes such as electric energy conversion and hydrogen transportation. However, the flammable and explosive characteristics of hydrogen energy, the complex interaction between devices, and the strong volatility of renewable energy input make the safe operation of the system face severe challenges.

[0003] In recent years, deep learning technology has been gradually applied to the management of hydrogen energy storage systems. Using networks such as long short-term memory networks and convolutional neural networks, risk assessment is carried out on device sensor data, and then risk warnings are given according to the abnormal monitoring results to ensure device safety. The safety management of hydrogen energy storage systems mainly includes risk assessment and fault location. Traditional risk assessment is to separately collect the operating status data of each device, evaluate the health status of the device according to these operating status data, and evaluate the risk level of the system according to the weight of the device and the health status of the device. This method performs weighted averaging on the health status, resulting in loss of detailed features, and there are problems such as low risk assessment accuracy and response lag, which cannot meet the safety management requirements of hydrogen energy storage systems. At the same time, existing models are mostly pure data-driven and do not incorporate physical laws such as the energy transmission path and hydrogen diffusion process of hydrogen energy storage systems, resulting in problems such as poor model interpretability, low accuracy and response lag, which cannot meet the safety management requirements of hydrogen energy storage systems. Summary of the Invention

[0004] In order to overcome the above technical problems existing in the prior art, the present invention provides a safety management method and system for a hydrogen energy storage system.

[0005] On the one hand, a safety management method for a hydrogen energy storage system is provided, including:

[0006] Obtaining the operating status data of the hydrogen energy storage system;

[0007] Extracting global features from the operating status data;

[0008] Extracting energy propagation features from the operating status data based on the energy transmission path, extracting hydrogen propagation features from the operating status data based on the hydrogen transmission path, and fusing the energy propagation features and the hydrogen propagation features to obtain local features;

[0009] Fuse the local feature and the global feature to obtain an enhanced global feature;

[0010] Input the enhanced global feature and the local feature into a preset risk assessment model to obtain a risk assessment result;

[0011] Generate an alarm message based on the risk assessment result.

[0012] Preferably, extracting global features from the operation status data includes:

[0013] Extract the operation mode and equipment importance from the operation status data;

[0014] Determine the equipment weight based on the operation mode and the equipment importance;

[0015] Fuse the operation status data based on the equipment weight to obtain global features.

[0016] Preferably, extracting hydrogen propagation features from the operation status data based on the hydrogen transmission path includes:

[0017] Construct a hydrogen transmission path based on the hydrogen transmission process in the hydrogen energy storage system;

[0018] Extract the current data and voltage data of the hydrogen transmission path nodes from the operation status data based on the hydrogen transmission path;

[0019] Perform feature reconstruction on the current data and the voltage data to obtain a hydrogen propagation matrix;

[0020] Perform convolution on the hydrogen propagation matrix to obtain hydrogen propagation features.

[0021] Preferably, performing feature reconstruction on the current data and the voltage data to obtain a hydrogen propagation matrix includes:

[0022] Perform FFT analysis on the current data, and determine the harmonic distortion rate according to the FFT analysis result;

[0023] Perform wavelet decomposition on the current data and the voltage data respectively, and determine the current spectrum energy ratio and the voltage spectrum energy ratio according to the wavelet decomposition results;

[0024] Determine the node weights of each hydrogen transmission path node according to the hydrogen transmission path, and construct a hydrogen propagation matrix based on the node weights, the harmonic distortion rate, the current spectrum energy ratio, and the voltage spectrum energy.

[0025] Preferably, extracting hydrogen propagation features from the operation status data based on the hydrogen transmission path includes:

[0026] Construct a hydrogen transmission path based on the transmission process of hydrogen in the hydrogen energy storage system;

[0027] Extract the pressure signal and the flow signal from the operation state data based on the hydrogen transmission path;

[0028] Perform feature reconstruction on the flow signal and the pressure signal to obtain a hydrogen propagation matrix;

[0029] Perform convolution on the hydrogen propagation matrix to obtain hydrogen propagation features.

[0030] Preferably, performing feature reconstruction on the flow signal and the pressure signal to obtain a hydrogen propagation matrix includes:

[0031] Determine the flow rate volatility according to the flow signal, and determine the pressure gradient according to the pressure signal;

[0032] Input the flow signal and the pressure signal into a preset physical information neural network model to obtain a predicted flow rate volatility and a predicted pressure gradient;

[0033] Generate a hydrogen propagation matrix according to the flow rate volatility, the pressure gradient, the predicted flow rate volatility, and the predicted pressure gradient.

[0034] Preferably, fusing the energy propagation feature and the hydrogen propagation feature to obtain a local feature further includes:

[0035] When the system device is in a cluster mode, calculate the load balancing index between devices according to the load rate;

[0036] Generate a load balancing feature based on the load balancing index between devices;

[0037] Fuse the load balancing feature between devices, the energy propagation feature, and the hydrogen propagation feature to obtain a local feature.

[0038] In a second aspect, a safety management system for a hydrogen energy storage system is provided, and the system includes:

[0039] An acquisition module for acquiring operation state data of the hydrogen energy storage system;

[0040] A global feature extraction module for extracting global features from the operation state data;

[0041] A local feature extraction module for extracting an energy propagation feature from the operation state data based on an energy transmission path, extracting a hydrogen propagation feature from the operation state data based on a hydrogen transmission path, and fusing the energy propagation feature and the hydrogen propagation feature to obtain a local feature;

[0042] A global feature enhancement module for fusing the local feature and the global feature to obtain an enhanced global feature;

[0043] A risk assessment module for inputting the enhanced global feature and the local feature into a preset risk assessment model to obtain a risk assessment result;

[0044] An alarm module for generating an alarm message based on the risk assessment result.

[0045] Through the technical solution provided by the present invention, the present invention has at least the following technical effects:

[0046] Based on the two core physical processes of energy propagation and hydrogen propagation in the hydrogen energy storage system, the present invention introduces local features to enhance the global features, adds detailed features of key equipment, significantly improves the risk assessment accuracy and response speed, and can meet the safety management requirements of the hydrogen energy storage system.

[0047] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation section. Description of the Drawings

[0048] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0049] Figure 1 is a flowchart of a safety management method for a hydrogen energy storage system provided by an embodiment of the present invention;

[0050] Figure 2 is a system block diagram of a safety management system for a hydrogen energy storage system provided by an embodiment of the present invention. Specific Implementation

[0051] The following will describe in detail the specific implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiments of the present invention, and is not used to limit the embodiments of the present invention.

[0052] In the embodiments of the present invention, the terms "system" and "network" can be used interchangeably. "Plurality" means two or more. In view of this, in the embodiments of the present invention, "plurality" can also be understood as "at least two". "And / or" describes the association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after, unless otherwise specified. In addition, it should be understood that in the description of the embodiments of the present invention, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0053] Based on the above content, please refer to Figure 1 , the embodiments of the present invention provide a safety management method for a hydrogen energy storage system, and the method includes:

[0054] Step 1, obtaining the operation status data of the hydrogen energy storage system;

[0055] The devices in the hydrogen energy storage system include electrolyzers, compressors, fuel cells, pipelines, valves, power distribution cabinets, cooling systems, etc. A number of sensors are deployed on these devices, which can obtain the corresponding operation status data. For example, the operation status data obtained from the electrolyzer includes input current, input voltage, temperature, pressure, input current, hydrogen concentration, etc., and the operation status data obtained from the compressor includes input current, input voltage, vibration, oil pressure, rotation speed, flow rate, etc. In a possible implementation manner, all the data collected by the sensors, as well as the time information and power recorded by each device, are used as the operation status data for subsequent risk assessment. Although this method can implement the hydrogen energy storage system, there are a large number of sensors set in a hydrogen energy storage system, and there are also many redundant designs, which will lead to a sharp increase in the amount of data, thereby affecting the risk assessment efficiency and the safety management response speed.

[0056] Therefore, in the embodiments of the present invention, all the sensor data on the key devices are selected to be obtained, and partial sensor data on the non-key devices are obtained. Among them, the key devices include electrolyzers, compressors, fuel cells, and cooling systems, and the non-key devices include pipelines, valves, power distribution cabinets, etc. Partial sensor data are selected to be obtained according to the distance between the sensors on the non-key devices and the key devices, which can not only reduce the amount of data but also evaluate the operation of the overall system.

[0057] Furthermore, the operating status data is time series data, and the acquisition process is as follows: obtain the original operating status data with time information, perform time series alignment according to the time information, and perform noise reduction on the aligned data, apply 50Hz notch filtering and 1kHz low-pass filtering to the electrical signals in the data, use wavelet threshold denoising on the mechanical vibration signals of the data, fill missing values ​​in the data after noise reduction, use forward filling for short-term missing values, and fill long-term missing values ​​based on the LSTM prediction model.

[0058] After obtaining multi-source monitoring data, these data need to be analyzed to extract the data features.

[0059] Step 2: extracting global features from the running status data.

[0060] In the embodiment of the present invention, the global feature fuses the operation status data of each device at the device level to obtain the device feature, and then fuses these device features based on the device weight to obtain the global feature.

[0061] In one possible implementation, the ratio of the historical failure frequency of a single device to the average failure frequency of all devices in the hydrogen energy storage system is calculated, which can well reflect the reliability of the device. The ratio of the maintenance cost to the replacement cost of a single device is calculated, which can well reflect the economic efficiency of the device. Then, the importance of the device is comprehensively calculated based on the two ratios, and the device characteristics are fused according to the device weights to obtain global characteristics.

[0062] However, in actual applications, the hydrogen energy storage system has different operating modes, such as full-load hydrogen production mode, energy storage priority mode, emergency standby mode, etc. Key equipment such as electrolyzers, compressors, hydrogen storage tanks, and fuel cells have different importance. For example, in full-load hydrogen production mode, the system is fully engaged in hydrogen production. As the core equipment for electrolyzing water into hydrogen and oxygen, the electrolyzer has an extremely high load. Its operating state directly determines the efficiency and output of hydrogen production, and plays a key role in the hydrogen production process of the entire system. The compressor is responsible for compressing the generated hydrogen to a suitable pressure for storage or transportation. Its load is also at a high level. If the compressor operates abnormally, it will affect the storage and subsequent use of hydrogen. In contrast, the hydrogen storage tank is mainly used to receive and store hydrogen at this time. Although it is also important, its importance is slightly weaker in the current mode compared to the key equipment in the hydrogen production link. Therefore, the present invention considers the operating mode and further adjusts the equipment weight.

[0063] In an embodiment of the present invention, extracting global features from the operating status data includes: extracting the operating mode and the importance of the equipment from the operating status data; determining the equipment weight based on the operating mode and the equipment importance; and fusing the operating status data based on the equipment weight to obtain the global features.

[0064] Specifically, the operation mode can be determined according to the actual operation power of each device during the adoption period. The load rate of the system devices is determined according to the ratio of the actual operation power to the rated operation power. When the load rates are all within the normal range, the current operation mode is determined according to the relationship mapping table between the load rate and the preset load rate operation mode, and then the device weights are adjusted according to the preset device importance adjustment rules for the operation mode to obtain the adjusted device weights. Of course, the operation mode can also be determined according to actual needs, which will not be elaborated in the embodiments of the present invention.

[0065] The embodiments of the present invention can dynamically adjust the device weights according to different operation modes of the hydrogen energy storage system.

[0066] After extracting the global features, the risk situation of the system can be evaluated according to the global features. The global features are extracted by weighted fusion at the device level based on the operation state data. Therefore, it can capture the overall trend and pattern of the data, which helps to comprehensively understand the overall state of the hydrogen energy storage system. However, since the global features are extracted from the entire dataset and the same processing is performed on these data, the detailed features of the global features are lost, and they are relatively insensitive to the anomalies or noises of some individual data points. In a possible implementation manner, the fault statistical data is introduced, and the fault topology map is generated according to the fault statistical data, and the fault topology map is fused with the global features to enhance the global features. Although this method can realize the cross-device risk conduction assessment, it depends on the statistical correlation of historical faults. In the face of sudden unknown faults, for example, a hydrogen power station is subjected to a transient power grid surge due to lightning strike, and the harmonic distortion rate of the input current of the electrolyzer soars from 5% to 40% within 0.5 seconds and conducts the harmonics to the fuel cell, causing the inverter to burn out. If there is no similar scenario in the historical data, it cannot be identified. Moreover, this method requires a large number of fault samples. Even if there is a similar scenario in the historical data, the data volume in the historical data may not necessarily meet the requirements, that is, this method has problems such as difficult data collection, complex model construction and training processes. In another possible implementation manner, all the operation state data are fused at the sensor level to learn the correlation of the sensor data between different devices, so as to realize the identification of detailed features. However, this method introduces a large amount of data and generates more noise, and the subsequent model training process is also very complex. Therefore, a method for obtaining local features that does not rely on historical statistical faults and a large amount of data is needed to enhance the global features. Since the hydrogen energy storage system mainly involves two core physical processes of energy conversion and hydrogen transmission, the data in these two core physical processes can cover the system operation. Therefore, the embodiments of the present invention propose a local feature extraction method considering energy propagation and hydrogen transmission.

[0067] Step 3: Extract the energy propagation characteristics from the operation state data based on the energy transmission path, extract the hydrogen gas propagation characteristics from the operation state data based on the hydrogen gas transmission path, and fuse the energy propagation characteristics and the hydrogen gas propagation characteristics to obtain local characteristics.

[0068] In a hydrogen energy storage system, the process of electric energy propagation is that the electrolyzer converts electric energy into hydrogen energy, transmits it to the compressor drive motor, and generates electricity through the fuel cell to feed back to the power grid. In a possible implementation manner, extracting the energy propagation characteristics from the operation state data based on the energy transmission path includes: extracting the electrical parameters of the electrolyzer, compressor, and fuel cell from the operation state data. The electrical parameters include the current and voltage at the input end and the current and voltage at the output end of each device. Construct local characteristics based on the extracted electrical parameters, and fuse the local characteristics with the global characteristics to enhance the risk perception ability in the physical process of the global characteristic hydrogen energy storage. Although this method can realize the extraction of the characteristics of the energy propagation process, it does not analyze data such as current and voltage, resulting in insufficient understanding of the dynamic propagation characteristics of electric energy.

[0069] In the embodiment of the present invention, extracting the energy propagation characteristics from the operation state data based on the energy transmission path includes: based on the energy transmission path, constructing an energy transmission path; extracting the current data and voltage data of the energy transmission path nodes from the operation state data based on the energy transmission path; performing feature reconstruction on the current data and the voltage data to obtain an energy propagation matrix; performing convolution on the energy propagation matrix to obtain energy propagation characteristics.

[0070] In the electrolyzer → compressor → fuel cell path, the input end of the electrolyzer is the starting point for converting electric energy into hydrogen energy. Its current and voltage directly affect the electrolysis efficiency and the safety of subsequent equipment. Monitoring here can capture key risks such as rectifier failures and grid disturbances. The current and voltage content at the drive end of the compressor directly relates to the compressor efficiency and mechanical wear, etc. The current and voltage at the output end of the fuel cell determine the quality of the electric energy feedback. Therefore, in the embodiment of the present invention, the current data and voltage data of the electrolyzer, compressor, and fuel cell are extracted from the operation state data based on the energy transmission path.

[0071] In a possible way, based on the transmission process of electric energy in the hydrogen energy storage system, the constructed electric energy transmission path is electrolyzer → compressor → fuel cell. According to the energy transmission path, the current data and voltage data at the input end of the electrolyzer, the current data and voltage data at the input end of the compressor drive motor, and the current data and voltage data at the output end of the fuel cell are extracted from the motion state data. Parameters are calculated according to the current data and the voltage data. According to the current change rate and the voltage change rate, an energy propagation matrix is generated based on the current change rate and the voltage change rate. The energy propagation matrix generated in this way is a statistical feature, and no in-depth analysis is performed on the current data and voltage data, resulting in insufficient details.

[0072] In a possible implementation manner, based on the transmission process of electric energy in the hydrogen energy storage system, the constructed electric energy transmission path is electrolyzer → compressor → fuel cell. According to the energy transmission path, the current data and voltage data at the input end of the electrolyzer, the current data and voltage data at the input end of the compressor drive motor, and the current data and voltage data at the output end of the fuel cell are extracted from the motion state data. The extracted current data is subjected to 1024-point FFT to calculate the fundamental wave and harmonic effective values, and the harmonic distortion rates of the electrolyzer, compressor, and fuel cell are obtained. At the same time, the extracted voltage data is decomposed into 3 layers using the Daubechies4 wavelet basis to obtain multiple frequency bands, the energy proportion of each frequency band is calculated, and an energy path node position encoding is generated based on the energy transmission path. Based on the energy path node position encoding and the energy proportion of the harmonic distortion rate, a corresponding energy feature moment is generated, and then the convolutional network is used to perform convolution on the energy propagation spectrum matrix to obtain the energy propagation feature.

[0073] Since the state of the electrolyzer is more directly affected by the current, for example, current harmonics reflect rectifier problems, while the voltage fluctuation of the fuel cell can better indicate internal or external problems, such as inverter failures or disturbances transmitted by compressor surges. The compressor is voltage-stabilized by an inverter, and its voltage signal has a weak response to mechanical failures (such as surges). In order to reduce the data volume, in a preferred implementation manner, only the current data of the electrolyzer and the compressor is subjected to 1024-point FFT to calculate the fundamental wave and harmonic effective values, and the harmonic distortion rates of the electrolyzer and the compressor are obtained; the current signal at the input end of the compressor drive motor is subjected to wavelet decomposition, and the voltage signal at the output end of the fuel cell is subjected to wavelet decomposition.

[0074] In the embodiments of the present invention, by constructing an energy transmission path, extracting features such as harmonic distortion rate, and fusing convolutional operations, an energy propagation feature is obtained. This feature can make up for the problem of the lack of detailed features in the global feature, making the global feature pay more attention to the physical process of energy transmission.

[0075] In an embodiment of the present invention, hydrogen propagation characteristics are extracted from the operating state data based on the hydrogen transmission path, including: constructing a hydrogen transmission path based on the hydrogen transmission process in the hydrogen energy storage system; extracting the flow rate volatility and pressure gradient from the operating state data based on the hydrogen transmission path; performing feature reconstruction based on the flow rate volatility and the pressure gradient to obtain a hydrogen feature matrix; and performing convolution on the hydrogen feature matrix to obtain hydrogen propagation characteristics.

[0076] In a possible implementation manner, the hydrogen transmission path is electrolyzer → compressor → hydrogen storage tank → fuel cell, covering the entire process of hydrogen generation, compression, storage, and use. Along this path, the outlet flow signal and pressure signal of the electrolyzer, the inlet and outlet flow signals and pressure signals of the compressor, the inlet and outlet flow signals and pressure signals of the hydrogen storage tank, and the inlet pressure signal and flow signal of the fuel cell are extracted from the operating state data. The flow rate volatility is calculated for the flow signal, and the flow rate volatility reflects the impact of instantaneous flow rate changes on the system stability. The pressure gradient is calculated for the pressure signal to quantify the abnormal pressure difference between the upstream and downstream of the pressure gradient pipeline. A hydrogen path node position encoding is generated based on the hydrogen transmission path, and a corresponding hydrogen feature matrix is generated based on the hydrogen path node position encoding, flow rate volatility, and pressure gradient. Then, a convolution network is used to perform convolution on the hydrogen feature matrix to obtain hydrogen propagation characteristics. This method constructs an energy transmission path, extracts the flow rate volatility and pressure gradient, and integrates spatio-temporal convolution operations to obtain hydrogen propagation characteristics. This feature can make up for the lack of detailed features in the global features, making the global features pay more attention to the physical process of hydrogen transmission. However, in practical applications, due to the long transmission pipeline and the great harm of hydrogen leakage, only obtaining the outlet flow signal and pressure signal of the electrolyzer, the inlet and outlet flow signals and pressure signals of the compressor, and the inlet and outlet flow signals and pressure signals of the hydrogen storage tank cannot well characterize the hydrogen transmission process. Therefore, it is necessary to further analyze the pressure signal and flow signal to obtain more feature information.

[0077] In an embodiment of the present invention, feature reconstruction is performed on the flow signal and the pressure signal to obtain a hydrogen propagation matrix, including: determining the flow rate volatility according to the flow signal and determining the pressure gradient according to the pressure signal; inputting the flow signal and the pressure signal into a preset physical information neural network model to obtain a predicted flow rate volatility and a predicted pressure gradient; and generating a hydrogen propagation matrix according to the flow rate volatility, pressure gradient, predicted flow rate volatility, and predicted pressure gradient.

[0078] In a possible implementation manner, a physical information neural network model is pre-constructed, which can determine the flow signals and pressure signals at other key positions on the transmission pipeline based on the pressure signals and flow signals at the entrances and exits of each key device, as well as the hydrogen production efficiency of the electrolyzer and the rotational speed of the compressor. Then, the flow volatility is calculated for the flow signals, and the pressure gradient is calculated for the pressure signals, so as to obtain more hydrogen propagation characteristic information.

[0079] In the embodiment of the present invention, the hydrogen propagation is simulated and predicted through a neural network, so as to obtain more information and make the local details more abundant.

[0080] In a large-scale hydrogen energy storage system, the electrolyzers, compressors, and fuel cells are all arranged in arrays. The uneven load inside these arrays will increase the equipment risks. Therefore, the present invention provides a method for determining local features considering load balance.

[0081] In the embodiment of the present invention, the energy propagation characteristics are extracted from the operation state data based on the energy transmission path, the hydrogen propagation characteristics are extracted from the operation state data based on the hydrogen transmission path, and the energy propagation characteristics and the hydrogen propagation characteristics are fused to obtain local features. It further includes: when the system equipment is in the cluster mode, calculating the load balance index between devices according to the load rate; when the system equipment is in the cluster mode, extracting the operation power from the operation state data; calculating the load rate of the system equipment according to the operation power, and calculating the load balance index between devices according to the load rate; generating the load balance characteristics based on the load balance index between devices; fusing the load balance characteristics between devices, the energy propagation characteristics, and the hydrogen propagation characteristics to obtain local features.

[0082] After obtaining the local features, it is necessary to fuse the local features with the global features. The traditional method usually fuses the local features and the global features through weighted average or simple splicing. Simple linear operations cannot model the complex associations between the global and local features, resulting in misjudgment of cross-device risk conduction.

[0083] Step 5: Fuse the local features and the global features to obtain enhanced global features.

[0084] In the feature fusion stage, the global features and local features are input into the cross-attention module. Among them, the global features serve as the query, and the local features serve as the key and value, and the correlation weights between the features are dynamically calculated through the multi-head attention mechanism. Specifically, the cross-attention mechanism constructs a cross-modal feature association model by taking the global features as the query and the local features as the key and value. The global features represent the overall trend of the system, while the local features capture the dynamic details of the physical process. The cross-attention dynamically assigns weights by calculating the correlation between the two. For example, when the harmonic distortion rate of the electrolytic cell suddenly increases to 20%, the cross-attention automatically increases the weight of the energy propagation features. Even if the global features do not reach the threshold due to mean smoothing, the warning can still be accurately triggered. Conversely, when the system is operating in a steady state, the global features dominate the risk assessment to avoid over-response to noise. The multi-head attention mechanism is further introduced to allow parallel attention to the feature interactions in different subspaces, thereby enhancing the perception of the global features for individual anomalies and cross-device anomalies that occur in the two core physical processes of power energy propagation and hydrogen transmission.

[0085] Step 6: Input the enhanced global features and the local features into a preset risk assessment model to obtain a risk assessment result, and generate an alarm message based on the risk assessment result.

[0086] Specifically, the safety management of the hydrogen energy storage system needs to take into account both global state perception and local anomaly capture. Therefore, the present invention constructs a dual-channel detection network, including a fully enhanced global feature channel and a local feature independent detection channel. Among them, the fully enhanced global feature channel is a bidirectional LSTM network, and the local feature channel is a convolutional neural network. The enhanced global features and local features are independently processed through the two channels to obtain an enhanced global feature score and a local feature score, and then the enhanced global feature score and the local feature score are weighted and summed to obtain a final risk score, and a risk warning is issued according to the final risk score.

[0087] For the same inventive concept, please refer to Figure 2, an embodiment of the present invention further provides a safety management system for a hydrogen energy storage system, the system comprising: an acquisition module for acquiring operation status data of the hydrogen energy storage system; a global feature extraction module for extracting device-level global features and sensor-level global features from the operation status data, fusing the device-level global features and the sensor-level global features to obtain global features; a local feature extraction module for extracting energy propagation features from the operation status data based on an energy transmission path, extracting hydrogen propagation features from the operation status data based on a hydrogen transmission path, fusing the energy propagation features and the hydrogen propagation features to obtain local features; a global feature enhancement module for fusing the local features and the global features to obtain enhanced global features; a risk assessment module for inputting the enhanced global features and the local features into a preset risk assessment model to obtain a risk assessment result; and an alarm module for generating an alarm message based on the risk assessment result.

[0088] It should be understood that the safety management system for a hydrogen energy storage system provided by an embodiment of the present invention and the safety management system for a hydrogen energy storage system provided by the above embodiment are based on the same inventive concept. Therefore, the working processes of the various modules in the safety management system for a hydrogen energy storage system provided by an embodiment of the present invention are the same as those of the safety management method for a hydrogen energy storage system provided by the above embodiment, and will not be elaborated in this embodiment of the present invention.

[0089] The optional embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0090] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without conflict. To avoid unnecessary repetition, the embodiments of the present invention will not separately describe various possible combination methods.

[0091] Those skilled in the art can understand that all or part of the steps for implementing the methods in the above embodiments can be completed by instructing relevant hardware through a program, and the program is stored in a storage medium, including several instructions for causing a single-chip microcomputer, a chip or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disc that can store program codes.

[0092] In addition, any combination can be made among various different embodiments of the embodiments of the present invention, as long as it does not violate the idea of the embodiments of the present invention, and it should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A safety management method for a hydrogen energy storage system, characterized in that: include: Acquiring operating status data of the hydrogen energy storage system; extracting global features from the operating status data; Extracting energy propagation features from the operating state data based on the energy transmission path, extracting hydrogen propagation features from the operating state data based on the hydrogen transmission path, and fusing the energy propagation features and the hydrogen propagation features to obtain local features; The local feature and the global feature are fused to obtain an enhanced global feature; Inputting the enhanced global features and the local features into a preset risk assessment model to obtain a risk assessment result; Generate alarm information based on risk assessment results; Extracting global features from the operating status data includes: extracting the operation mode and the importance of the equipment from the operation status data; Determining a device weight based on the operation mode and the device importance; fusing the operation status data based on the device weight to obtain a global feature; Extracting energy propagation features from the operating status data based on the energy transmission path includes: Based on the energy transmission process of the hydrogen energy storage system, construct an energy transmission path; extracting current data and voltage data of nodes of the energy transmission path from the operating status data based on the energy transmission path; Performing feature reconstruction on the current data and the voltage data to obtain an energy propagation matrix; Convolving the energy propagation matrix to obtain energy propagation features; Extracting hydrogen propagation characteristics from the operating status data based on the hydrogen transmission path includes: Establishing a hydrogen transmission path based on the transmission process of hydrogen in the hydrogen energy storage system; extracting a pressure signal and a flow signal from the operating status data based on the hydrogen transmission path; Performing feature reconstruction on the flow signal and the pressure signal to obtain a hydrogen propagation matrix; Convolving the hydrogen propagation matrix to obtain hydrogen propagation features; The flow signal and the pressure signal are subjected to feature reconstruction to obtain a hydrogen propagation matrix, including: determining a flow rate fluctuation rate according to the flow signal, and determining a pressure gradient according to the pressure signal; Inputting the flow signal and the pressure signal into a preset physical information neural network model to obtain a predicted flow fluctuation rate and a predicted pressure gradient; generating a hydrogen propagation matrix according to the flow fluctuation rate, the pressure gradient, the predicted flow fluctuation rate and the predicted pressure gradient; The energy propagation feature and the hydrogen propagation feature are merged to obtain a local feature, further comprising: When the system device is in cluster mode, extracting operating power from the operating status data; Calculate the load rate of the system equipment according to the operating power, and calculate the load balancing index between the equipment according to the load rate; generating a load balancing feature based on the inter-device load balancing index; The inter-device load balancing feature, the energy propagation feature and the hydrogen propagation feature are integrated to obtain a local feature.

2. A safety management method for a hydrogen energy storage system according to claim 1, characterized in that: The current data and the voltage data are subjected to feature reconstruction to obtain an energy propagation matrix, including: Performing FFT analysis on the current data, and determining the harmonic distortion rate according to the FFT analysis result; Performing wavelet decomposition on the current data and the voltage data respectively, and determining the current spectrum energy proportion and the current spectrum energy proportion according to the wavelet decomposition results; The node weights of the nodes of each energy transmission path are determined according to the energy transmission path, and an energy propagation matrix is ​​constructed based on the node weights, the harmonic distortion rate, the current spectrum energy proportion and the current spectrum energy.

3. A safety management system for a hydrogen energy storage system, characterized in that: The system comprises: An acquisition module, used to acquire operating status data of the hydrogen energy storage system; A global feature extraction module, used to extract global features from the operating status data; A local feature extraction module, used to extract energy propagation features from the operating state data based on the energy transmission path, extract hydrogen propagation features from the operating state data based on the hydrogen transmission path, and fuse the energy propagation features with the hydrogen propagation features to obtain local features; A global feature enhancement module, used for fusing the local feature with the global feature to obtain an enhanced global feature; A risk assessment module, used for inputting the enhanced global features and the local features into a preset risk assessment model to obtain a risk assessment result; An alarm module, used to generate alarm information based on risk assessment results; Extracting global features from the operating status data includes: extracting the operation mode and the importance of the equipment from the operation status data; Determining a device weight based on the operation mode and the device importance; fusing the operation status data based on the device weight to obtain a global feature; Extracting energy propagation features from the operating status data based on the energy transmission path includes: Based on the energy transmission process of the hydrogen energy storage system, construct an energy transmission path; extracting current data and voltage data of nodes of the energy transmission path from the operating status data based on the energy transmission path; Performing feature reconstruction on the current data and the voltage data to obtain an energy propagation matrix; Convolving the energy propagation matrix to obtain energy propagation features; Extracting hydrogen propagation characteristics from the operating status data based on the hydrogen transmission path includes: Establishing a hydrogen transmission path based on the transmission process of hydrogen in the hydrogen energy storage system; extracting a pressure signal and a flow signal from the operating status data based on the hydrogen transmission path; Performing feature reconstruction on the flow signal and the pressure signal to obtain a hydrogen propagation matrix; Convolving the hydrogen propagation matrix to obtain hydrogen propagation features; The flow signal and the pressure signal are subjected to feature reconstruction to obtain a hydrogen propagation matrix, including: determining a flow rate fluctuation rate according to the flow signal, and determining a pressure gradient according to the pressure signal; Inputting the flow signal and the pressure signal into a preset physical information neural network model to obtain a predicted flow fluctuation rate and a predicted pressure gradient; generating a hydrogen propagation matrix according to the flow fluctuation rate, the pressure gradient, the predicted flow fluctuation rate and the predicted pressure gradient; The energy propagation feature and the hydrogen propagation feature are merged to obtain a local feature, further comprising: When the system device is in cluster mode, extracting operating power from the operating status data; Calculate the load rate of the system equipment according to the operating power, and calculate the load balancing index between the equipment according to the load rate; generating a load balancing feature based on the inter-device load balancing index; The inter-device load balancing feature, the energy propagation feature and the hydrogen propagation feature are integrated to obtain a local feature.

4. A processor for running a program, wherein: The program is used to execute the method according to claim 1 or 2 when being executed.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 1 or 2 is implemented.

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