Fuel tank hydrogen production data acquisition and analysis device

Abstract

The invention provides a fuel tank hydrogen production data acquisition and analysis device, which adopts a hardware-software-algorithm collaborative architecture to realize hydrogen production whole-process intelligent monitoring and optimization decision making, and comprises a high-precision data acquisition hardware module, an intelligent analysis strategy module and a closed-loop control system, by virtue of the remarkable advantages of accurate data acquisition, scientific analysis strategy, comprehensive software functions and the like, the device promotes intelligent development and application innovation of a fuel tank hydrogen production technology while ensuring safe and efficient operation of a hydrogen production process, and has important significance in promoting technical progress of the hydrogen energy industry.

Description

Technical Field

[0001] This invention relates to the field of hydrogen production equipment technology, specifically to a data acquisition and analysis device for hydrogen production from fuel tanks. Background Technology

[0002] In existing hydrogen production processes using fuel tanks, the lack of data acquisition and analysis capabilities results in poor control over hydrogen production and a lack of technical parameters such as fuel cell remaining capacity and fuel tank status. This invention addresses these technical shortcomings by employing a hardware-software-algorithm collaborative architecture to achieve intelligent monitoring and optimization decision-making throughout the entire process, effectively overcoming the deficiencies of existing technologies. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a data acquisition and analysis device for hydrogen production from fuel tanks.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0005] A data acquisition and analysis device for hydrogen production from fuel tanks employs a hardware-software-algorithm collaborative architecture to achieve intelligent monitoring and optimization decision-making throughout the entire hydrogen production process. It includes a high-precision data acquisition hardware module, an intelligent analysis strategy module, and a closed-loop control system.

[0006] The high-precision data acquisition hardware module integrates a temperature acquisition chip (MAX31856) and an ADC module for pressure signal acquisition, enabling real-time acquisition of temperature and pressure data. It employs a dynamic temperature compensation algorithm to correct errors caused by thermocouple cold junction temperature drift by acquiring the cold junction ambient temperature in real time, significantly improving the accuracy of hydrogen production reaction temperature acquisition. Simultaneously, it utilizes real-time temperature data to eliminate pressure measurement deviations caused by temperature changes, achieving real-time acquisition of hydrogen production reaction temperature and pressure parameters and ensuring the reliability of the basic data.

[0007] The intelligent analysis strategy module, supported by a deep learning model as its core algorithm, constructs an integrated system for hydrogen metering (to estimate power generation efficiency and calculate hydrogen quantity using a power integral algorithm), remaining capacity prediction, and safety risk early warning (combining a deep learning model with historical data stored in the fuel tank's operation records <data storage chip> to construct a safety risk early warning system), including:

[0008] 1. A dynamic prediction model for the remaining capacity of the fuel tank. This model relies on the nonlinear fitting capability of a deep learning model for multi-source parameters and achieves accurate prediction of the remaining capacity in three steps: First, based on electrochemical impedance spectroscopy (EIS) feature extraction technology, core parameters such as ohmic impedance, charge transfer resistance, and diffusion impedance are analyzed to quantify the internal polarization loss of the fuel cell. Combined with the training results of the deep learning model on the mapping relationship between historical EIS data and power generation efficiency, the real-time power generation efficiency curve of the system is calculated. Second, the power integral method is used to substitute the real-time power generation efficiency into the power-hydrogen consumption conversion formula to accurately calculate the hydrogen consumption per unit time. Finally, the real-time temperature and pressure parameters of the fuel tank are coupled, and the correlation deviation of temperature-pressure-hydrogen quantity in the gas equation of state is corrected by the deep learning model. Combined with the consumed hydrogen quantity and the initial hydrogen storage quantity, the remaining capacity of the fuel tank is dynamically estimated.

[0009] 2. A multi-dimensional safety performance assessment system, based on pressure-temperature parameter coupling analysis and combined with the feature recognition and risk prediction capabilities of deep learning models, constructs a multi-modal safety assessment model to achieve graded early warning of safety risks 30 minutes in advance. Specific functions are as follows:

[0010] (1) Adaptive working condition pressure and temperature anomaly identification

[0011] A deep learning training dataset with multiple operating conditions was constructed, incorporating operating condition parameters such as current density, hydrogen excess coefficient, and ambient temperature. The model was trained to learn the normal coupling law and fluctuation range of pressure and temperature under different operating conditions, achieving dynamic adaptation of safety thresholds. During real-time monitoring, the model simultaneously collected pressure, temperature, and operating condition parameters. By extracting features and comparing the coupling relationship of real-time parameters with the normal operating condition benchmark, faults such as sudden pressure rise / fall and temperature exceeding limits were accurately identified, avoiding false alarms and missed alarms caused by a single fixed threshold. At the same time, the monitoring dimensions were expanded by incorporating key parameters such as hydrogen concentration, cooling system flow rate, and stack voltage fluctuations into the model input layer, achieving integrated judgment of "anomaly identification and root cause location".

[0012] (2) Full life cycle thermal runaway risk classification monitoring and early warning

[0013] Establish a full lifecycle operation record for the hydrogen production unit, continuously recording temperature and pressure change curves, as well as key operational data such as the number of repeated start-stop cycles, power output mode, water injection dosage, and start-stop intervals. For five typical thermal runaway triggers in fuel cell systems (① excessive water injection into the hydrogen storage material, triggering a violent exothermic reaction; ② prolonged operation exceeding the rated power limit; ③ continuous operation under high-temperature conditions; ④ frequent repeated starts under low-temperature conditions; ⑤ direct switching to high-power operation after multiple low-power start-stop cycles), a deep learning model is used to mine the correlation between trigger combinations and precursors of thermal runaway in historical failure cases. Risk assessment weights are adjusted based on fuel tank aging characteristics (such as pressure resistance degradation rate and sealing leakage rate). Risks are classified into four levels: "low-medium-high-emergency," with corresponding differentiated early warning strategies: low-risk situations receive a 30-minute advance warning and power reduction suggestions; medium-risk situations trigger enhanced cooling system operation; high-risk situations activate hydrogen supply flow restriction protection; and emergency risks immediately disconnect the load and initiate emergency pressure relief procedures, achieving full-scenario, full-cycle safety protection.

[0014] The closed-loop control system constructs a three-level collaborative mechanism: hardware-level raw data acquisition, strategy-level dynamic analysis, and software-level decision execution. It employs a PID control algorithm with automatic parameter tuning capabilities to build a dual-loop closed-loop control architecture, achieving precise control and automatic optimization of hydrogen production process parameters. This effectively reduces system energy consumption and improves response speed. The specific control logic is as follows:

[0015] 1. Temperature feedback - closed-loop heating power control circuit

[0016] The hardware layer collects temperature data in the reaction chamber of the hydrogen production unit in real time and uploads it to the strategy layer. The strategy layer compares the real-time temperature with the target temperature threshold through a parameter self-tuning PID algorithm and dynamically outputs heating power adjustment commands. After receiving the commands, the software layer drives the heating module to perform power adjustment operations to ensure that the temperature in the fuel tank is stable in the optimal range, avoiding the risk of thermal runaway due to excessive temperature or insufficient hydrogen production reaction rate due to excessively low temperature.

[0017] 2. Pressure feedback-water injection rate control closed-loop circuit

[0018] The hardware layer continuously monitors the real-time pressure data of the reaction between magnesium hydride and water in the hydrogen production unit and feeds it back to the strategy layer. The strategy layer calculates the optimal water injection rate correction parameters based on the parameter self-tuning PID algorithm and the deviation between the real-time pressure value and the safe pressure threshold. The software layer drives the water injection module to adjust the water injection flow rate according to the correction parameters, accurately matching the reaction requirements of magnesium hydride hydrolysis to produce hydrogen, preventing a sudden pressure rise due to an excessively fast water injection rate, or a low hydrogen production efficiency due to an excessively slow water injection rate.

[0019] Preferably, in the above-mentioned fuel tank hydrogen production data acquisition and analysis device, the high-precision data acquisition hardware module adopts a modular layered design architecture, integrating temperature acquisition, pressure acquisition, and a general-purpose data acquisition interface; it supports sensor range and parameter configuration for different types of fuel tanks with varying operating temperature ranges and pressure thresholds, and can flexibly select temperature and pressure sensors with matching ranges according to hydrogen production process requirements, meeting the data acquisition needs of various hydrogen production process scenarios such as magnesium hydride hydrolysis hydrogen production and methanol reforming hydrogen production.

[0020] Preferably, in the above-mentioned fuel tank hydrogen production data acquisition and analysis device, the dynamic prediction model of the fuel tank remaining capacity in the intelligent analysis strategy module relies on the nonlinear fitting capability of the deep learning model for multi-source parameters to achieve accurate prediction of remaining capacity in three steps: First, based on the feature extraction technology of electrochemical impedance spectroscopy (EIS), the real-time power generation efficiency curve of the system is calculated; second, the power integral method is used to substitute the real-time power generation efficiency into the power-hydrogen consumption conversion formula to accurately calculate the hydrogen consumption per unit time; finally, the real-time temperature and pressure parameters of the fuel tank are coupled, and the correlation deviation of temperature-pressure-hydrogen quantity in the gas state equation is corrected by the deep learning model, and the remaining capacity of the fuel tank is dynamically estimated by combining the consumed hydrogen quantity and the initial hydrogen storage quantity.

[0021] Preferably, in the above-mentioned fuel tank hydrogen production data acquisition and analysis device, the multi-dimensional safety performance evaluation system is based on pressure-temperature parameter coupling analysis, combined with the feature recognition and risk prediction capabilities of deep learning models, to construct a multi-modal safety evaluation model, and achieves 30-minute advance graded early warning of safety risks through working condition adaptive pressure and temperature anomaly identification and full life cycle thermal runaway risk graded monitoring.

[0022] Preferably, in the above-mentioned fuel tank hydrogen production data acquisition and analysis device, the closed-loop control system adopts a three-level collaborative mechanism of hardware-level raw data acquisition, strategy-level dynamic analysis, and software-level decision execution. It employs a PID control algorithm with automatic parameter optimization function and builds a dual-loop closed-loop control architecture of temperature feedback-heating power regulation and pressure feedback-water injection rate regulation to achieve precise regulation and automatic optimization of hydrogen production process parameters, effectively reducing system energy consumption and improving response speed.

[0023] Beneficial effects:

[0024] The aforementioned hydrogen production data acquisition and analysis device for fuel tanks adopts a closed-loop architecture of "acquisition-analysis-feedback," achieving intelligent monitoring and optimization decision-making throughout the entire hydrogen production process (data acquisition, analysis, and optimization decision-making) through a hardware-software-algorithm collaborative architecture. A collaborative mechanism across the entire chain, from raw data acquisition to decision execution, is formed through a high-precision data acquisition hardware module and an intelligent analysis strategy module. The high-precision data acquisition hardware module integrates a dedicated temperature acquisition chip and a high-precision ADC module, using a dynamic temperature compensation algorithm to achieve real-time and accurate acquisition of key parameters such as hydrogen production reaction temperature and pressure, ensuring the reliability of the basic data.

[0025] The intelligent analysis strategy module uses deep learning models as the core algorithm to build an integrated system for hydrogen metering, remaining capacity prediction, and safety risk early warning of fuel cell systems. It covers core safety indicators such as thermal runaway early warning and pressure anomaly identification, realizing the intelligent transformation from data to decision-making. It is suitable for intelligent systems that monitor the hydrogen production reaction status, calculate the remaining capacity of fuel tanks, and evaluate safety performance during the hydrogen energy production process.

[0026] The aforementioned fuel tank hydrogen production data acquisition and analysis device, with its significant advantages such as accurate data acquisition, scientific analysis strategies, and comprehensive software functions, not only ensures the safe and efficient operation of the hydrogen production process but also promotes the intelligent development and application innovation of fuel tank hydrogen production technology, which is of great significance to the technological progress of the hydrogen energy industry. Specifically:

[0027] Accurate and reliable data acquisition: The dynamic temperature compensation algorithm ensures sampling accuracy of ±0.2%, which is an order of magnitude higher than that of traditional devices; the modular sensor configuration is adaptable to various hydrogen production process scenarios.

[0028] Intelligent Analysis and Decision Making: Combining the feature recognition and risk prediction capabilities of deep learning models, a multimodal security assessment model is constructed to achieve graded early warning of security risks 30 minutes in advance, reducing the false alarm rate by 40%.

[0029] Closed-loop control dynamic optimization: Adaptive algorithms adjust parameter acquisition frequency and analysis strategies in real time, improving system response speed by 30% and reducing energy consumption by 20%.

[0030] Proactive and efficient safety protection: The multimodal safety assessment model realizes pressure-temperature coupled analysis and builds a proactive safety protection system, which is more accurate and reliable than traditional single-parameter early warning. Attached Figure Description

[0031] Figure 1 Schematic diagram of a hydrogen production data acquisition and analysis device for fuel tanks.

[0032] Figure 2 A schematic diagram of the system architecture deployment for the hydrogen production data acquisition and analysis device for fuel tanks. Detailed Implementation

[0033] The data acquisition and analysis device for hydrogen production from fuel tanks according to the present invention will be described in detail below with reference to the embodiments and accompanying drawings.

[0034] Example 1

[0035] The hydrogen production data acquisition and analysis device for fuel tanks has the following framework:

[0036] 1. System Architecture Deployment

[0037] The device adopts a layered modular design, consisting of a hardware layer, a software layer, and a strategy layer, forming a data acquisition and analysis system. These three layers collaborate through standardized interfaces. The hardware layer includes a high-precision data acquisition module for signal acquisition, data storage, and heating output, while also providing basic data and a control interface. The software layer deploys a closed-loop control system, relying on the basic data and control interface from the hardware layer to achieve closed-loop control functions; it does not receive analysis data from the strategy layer. The strategy layer integrates an intelligent analysis algorithm module, which analyzes and processes data from the software layer to fulfill the functions of the intelligent analysis algorithm module.

[0038] like Figure 1-2 As shown, the high-precision data acquisition module integrates a temperature acquisition chip (MAX31856), a data storage chip (W25Q32), an ADC signal acquisition circuit, and a heating control circuit. The fuel tank pressure signal is processed by the ADC signal acquisition circuit of the data acquisition module and transmitted to the fuel cell controller, which then processes the received signal and restores it to a pressure value. The fuel cell controller implements the fuel tank heating function through the heating control circuit of the data acquisition module. The temperature acquisition chip (MAX31856) converts the analog temperature signal acquired by the thermocouple in the hydrogen production unit into a digital signal and transmits it to the fuel cell controller via the SPI bus. The data storage chip (W25Q32) is used to store data during the operation of the fuel tank. The fuel cell controller interface IO1 is connected to the fuel cell controller and transmits temperature and fuel tank operating data to the data storage chip and temperature acquisition chip on the fuel cell controller via the SPI bus. The data storage chip is used to store fuel tank operating data, and the temperature acquisition chip is used to acquire the fuel tank temperature. The fuel tank interface IO2 is connected to the K-type thermocouple, pressure sensor, and heating device on the fuel tank, serving as a plug-in for heating the fuel tank and acquiring temperature and pressure signals.

[0039] The strategy layer integrates an intelligent analysis algorithm module, including a dynamic prediction model for the remaining capacity of fuel tanks and a multi-dimensional safety performance evaluation system (multi-modal safety evaluation model).

[0040] 2. Implementation of high-precision data acquisition module

[0041] Sensor array configuration: Utilizing a K-type thermocouple temperature sensor (accuracy ±0.1℃) and a piezoresistive pressure sensor (range 0-600KPa, accuracy ±0.2%FS), signal conditioning and analog-to-digital conversion are achieved through a dedicated temperature acquisition chip (MAX31856) and a high-precision ADC module.

[0042] Temperature compensation algorithm:

[0043] (1) Cold junction temperature drift correction algorithm: The dedicated temperature acquisition chip collects the cold junction temperature T0 in real time. The algorithm calls the thermocouple calibration table to calculate the thermoelectric potential E0 corresponding to the cold junction temperature. Combined with the thermoelectric potential Et output by the main temperature measuring end of the thermocouple, the actual temperature Tt of the fuel tank is obtained by converting it through the formula Et total = Et + E0, and the drift error is corrected.

[0044] (2) Pressure measurement deviation elimination algorithm: Substitute the corrected real-time temperature Tt into the pressure sensor temperature compensation formula to correct the original pressure signal P0 (P=P0×[1+α(Tt-Tstandard)], where α is the sensor temperature coefficient and Tstandard is the standard calibration temperature) to eliminate the pressure deviation caused by temperature change.

[0045] 3. Implementation of the intelligent analysis strategy module at the strategy layer

[0046] Remaining capacity prediction model:

[0047] (1) EIS feature extraction and efficiency calculation: EIS data is collected every 10s, and the ohmic impedance (RΩ), charge transfer resistance (Rct), and diffusion impedance (Zw) are analyzed. Combined with the mapping relationship output by the LSTM model, the real-time power generation efficiency η (η = current output voltage × current / (theoretical reversible voltage × current)) is calculated, and the efficiency curve is generated.

[0048] (2) Hydrogen consumption calculation: The fuel controller collects the output voltage and current in real time to calculate the output power P, and calculates the hydrogen consumption Q within time t based on the power integral method: Q=∫(P / (η×H))dt (integration interval 0-t, H is the lower heating value of hydrogen, taken as 120MJ / kg), the integration accuracy is guaranteed by the trapezoidal integration method, and the Q value is updated every 100ms.

[0049] (3) Remaining capacity estimation: The data acquisition and analysis device sensor collects data once every 100ms. The model calls the ideal gas law correction formula: m=P×V / (R×T×Z) (where V is the fuel tank volume, R is the hydrogen gas constant, T is the absolute temperature, and Z is the compressibility factor, which is corrected by the deep learning model based on historical data, and the correction error is ≤0.5%). Combining the initial hydrogen storage m0 and the consumed amount Q, the remaining capacity m_remaining = m0-Q is obtained. The prediction result is output once every 1s. The prediction error is further controlled within 3% by moving average filtering.

[0050] Multimodal security assessment model:

[0051] (1) Adaptive anomaly identification model under working conditions

[0052] The model employs an "attention mechanism + MLP" approach: the input layer contains 5 parameters (pressure, temperature, current density, hydrogen excess coefficient, and ambient temperature); the attention layer assigns a weight of 0.4 to the pressure / temperature parameter, and the sum of the weights of the other parameters is 0.6, highlighting the influence of the core parameters; the MLP layer (3 hidden layers with 64, 32, and 16 neurons respectively) outputs the "normal / abnormal" judgment result and the probability of the abnormal root cause.

[0053] The model training uses cross-validation, mixing 50,000 sets of normal data under multiple operating conditions with 10,000 sets of abnormal simulation data for training. The anomaly identification accuracy is ≥99.2%, and the false alarm rate is ≤0.5%.

[0054] (2) Thermal runaway risk classification model

[0055] Full lifecycle data archive construction: FLASH chip is used to store fuel tank operation data. Each record contains fields such as "timestamp-temperature-pressure-number of start-stops-power mode-water injection volume". The data storage period is consistent with the equipment lifecycle and supports historical data backtracking and querying.

[0056] Trigger weight correction: Initial weights were determined based on the AHP (excessive water injection 0.3, overpower operation 0.25, high temperature operation 0.2, low temperature start-stop 0.15, start-stop switching 0.1). The weights were then corrected by combining the data from 200 failure cases with a deep learning model, which improved the model's sensitivity to identifying high-risk triggers by 20%.

[0057] Risk classification criteria: Low risk (single trigger parameter exceeds the threshold by less than 10%, with no coupling anomalies); Medium risk (single trigger parameter exceeds the threshold by 20%, or two low-risk triggers coexist); High risk (single trigger exceeds the threshold by 30%, or one medium-risk trigger + one low-risk trigger); Emergency risk (any trigger temperature ≥560℃ or pressure ≥180kPa).

[0058] 4. Implementation of closed-loop control system

[0059] Collaborative mechanism: The hardware layer collects data every 100ms, the strategy layer performs dynamic analysis every 200ms, and the software layer updates control commands every 400ms to achieve closed-loop regulation of temperature and pressure parameters.

[0060] (1) Temperature feedback-heating power regulation closed loop

[0061] The hardware layer uses K-type thermocouples deployed inside the reaction chamber of the hydrogen production unit to collect real-time temperature data of the reaction chamber. The strategy layer has a built-in parameter self-tuning PID algorithm that automatically identifies the characteristics of the temperature response curve to determine the initial proportional coefficient Kp, integral time Ti, and derivative time Td. Then, based on the real-time temperature and the target temperature threshold, the PID parameters are dynamically adjusted: when the temperature deviation is ≥3℃, Kp is increased to 1.2 times the initial value to speed up the response; when the temperature fluctuation is ≤0.5℃, Kp is decreased to 0.8 times the initial value and Ti is increased to stabilize the output. Finally, the heating power adjustment command is calculated.

[0062] (2) Pressure feedback-water injection rate control closed loop

[0063] The hardware layer monitors real-time pressure data of the reaction between magnesium hydride and water in the hydrogen production unit via pressure sensors and uploads it synchronously to the strategy layer. The strategy layer dynamically optimizes the PID parameters based on a parameter self-tuning PID algorithm, taking into account the deviation characteristics between the real-time pressure value and the safe pressure threshold: when the pressure is below 28 kPa, the derivative time Td is reduced to improve the water injection rate response; when the pressure is above 32 kPa, the integral time Ti is increased to avoid excessive water injection; the optimal water injection rate correction parameters are obtained through deviation calculation.

[0064] The main functions of the aforementioned fuel tank hydrogen production data acquisition and analysis device include: real-time recording of the hydrogen production reaction status of the fuel tank and dynamic reflection of parameter changes in the hydrogen production process; analysis of the remaining capacity of the fuel tank to provide accurate data support for fuel replenishment and scheduling; evaluation of the fuel tank's safety performance indicators to ensure the safe and reliable operation of the hydrogen production process; and the collection and analysis of data can provide strong data support for the formulation and optimization of fuel cell system reaction strategies, thereby promoting the overall performance improvement of the fuel cell system.

[0065] As can be seen, the aforementioned fuel tank hydrogen production data acquisition and analysis device, through hardware-software-algorithm collaboration, achieves intelligent monitoring and optimization decision-making throughout the entire fuel tank hydrogen production process, ensuring the safe and efficient operation of the hydrogen production process and promoting the intelligent development of fuel tank hydrogen production technology. It has achieved three major technological breakthroughs:

[0066] First, it adopts a "collection-analysis-feedback" closed-loop architecture, which completes the automatic optimization closed-loop control of hydrogen production process parameters through a collaborative mechanism of raw data collection at the hardware layer, dynamic analysis at the strategy layer, and decision execution at the software layer.

[0067] Secondly, we developed an intelligent algorithm for calculating the remaining capacity of fuel tanks. By combining electrochemical impedance spectroscopy feature extraction and power integration algorithms, we can achieve accurate measurement of the remaining hydrogen (measurement error ≤2%), providing an accurate basis for fuel replenishment and scheduling.

[0068] Third, a multimodal safety assessment model (including an adaptive pressure model for operating conditions, a temperature anomaly identification model, and a full life cycle thermal runaway risk classification monitoring and early warning model) is constructed. Through pressure-temperature parameter coupling analysis, early warning of hydrogen production safety risks can be achieved 30 minutes in advance, forming an active safety protection system.

[0069] In terms of technological advantages, the data acquisition module ensures measurement stability through a dynamic temperature compensation algorithm; the analysis strategy module adopts a modular programming design, which supports the dynamic loading of adaptation algorithms according to different fuel tank types, realizing flexible adaptation and optimization of algorithms.

[0070] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and all such improvements and modifications are considered to be within the scope of protection of the present invention.

Claims

1. A data acquisition and analysis device for hydrogen production from fuel tanks, characterized in that: It includes a high-precision data acquisition hardware module, an intelligent analysis strategy module, and a closed-loop control system, among which, The high-precision data acquisition hardware module integrates a temperature acquisition chip and an ADC module for pressure signal acquisition, which is used to acquire temperature and pressure data in real time. It adopts a dynamic temperature compensation algorithm to correct the error caused by the temperature drift of the thermocouple cold junction by acquiring the cold junction ambient temperature in real time. At the same time, it uses real-time temperature data to eliminate pressure measurement deviation caused by temperature changes. The intelligent analysis strategy module, supported by a deep learning model as its core algorithm, includes: 1) Dynamic prediction model for remaining fuel tank capacity: This model relies on the nonlinear fitting capability of deep learning models for multi-source parameters and achieves accurate prediction of remaining capacity in three steps: First, it analyzes core parameters based on electrochemical impedance spectroscopy feature extraction technology, quantifies the internal polarization loss of the fuel cell, and combines the training results of the deep learning model on the mapping relationship between historical EIS data and power generation efficiency to deduce the real-time power generation efficiency curve of the system; Second, it uses the power integral method to substitute the real-time power generation efficiency into the power-hydrogen consumption conversion formula to accurately calculate the hydrogen consumption per unit time; Finally, it couples the real-time temperature and pressure parameters of the fuel tank, corrects the correlation deviation of temperature-pressure-hydrogen quantity in the gas state equation through deep learning model, and dynamically calculates the remaining capacity of the fuel tank by combining the consumed hydrogen quantity and the initial hydrogen storage quantity. 2) A multi-dimensional safety performance evaluation system, based on pressure-temperature parameter coupling analysis and combined with the feature recognition and risk prediction capabilities of deep learning models, constructs a multi-modal safety evaluation model: (1) Adaptive working condition pressure and temperature anomaly identification A multi-condition deep learning training dataset is constructed, incorporating condition parameters. The model is trained to learn the normal coupling pattern and fluctuation range of pressure and temperature under different conditions, achieving dynamic adaptation of safety thresholds. During real-time monitoring, the model simultaneously collects pressure, temperature, and condition parameters. By extracting features and comparing the real-time parameter coupling relationship with the normal operating condition benchmark, faults are accurately identified, avoiding false alarms and missed alarms caused by a single fixed threshold. At the same time, the monitoring dimensions are expanded by incorporating key parameters into the model input layer, achieving integrated judgment of "anomaly identification and root cause location". (2) Full life cycle thermal runaway risk classification monitoring and early warning Establish a full life cycle operation archive for hydrogen production units and continuously record key operation data; use deep learning models to mine the correlation between the combination of causes and the precursors of thermal runaway in historical failure cases for five typical thermal runaway causes of fuel cell systems, and adjust the risk assessment weights in combination with the aging characteristics of fuel tanks; classify risks into four levels: "low-medium-high-emergency" and corresponding differentiated early warning strategies. The closed-loop control system employs a PID control algorithm with automatic parameter optimization to establish a dual-loop closed-loop control architecture. 1) Temperature feedback - closed-loop heating power control circuit The hardware layer collects and uploads temperature data from the reaction chamber of the hydrogen production unit in real time. Through a parameter self-tuning PID algorithm, it compares the real-time temperature with the target temperature threshold and dynamically outputs heating power adjustment commands. After receiving the commands, the software layer drives the heating module to perform power adjustment operations. 2) Pressure feedback-injection rate control closed-loop circuit The hardware layer continuously monitors and feeds back the real-time pressure data of the reaction between magnesium hydride and water in the hydrogen production unit. Based on the parameter self-tuning PID algorithm, and combined with the deviation between the real-time pressure value and the safe pressure threshold, the optimal water injection rate correction parameter is calculated. The software layer drives the water injection module to adjust the water injection flow rate according to the correction parameter, so as to accurately match the reaction requirements of magnesium hydride hydrolysis to produce hydrogen.

2. The fuel tank hydrogen production data acquisition and analysis device according to claim 1, characterized in that: The high-precision data acquisition hardware module adopts a modular and layered design architecture, integrating temperature acquisition, pressure acquisition, and a general data acquisition interface; it supports sensor range and parameter configuration to address the differences in operating temperature range and pressure thresholds of different types of fuel tanks.

3. The data acquisition and analysis device for hydrogen production from fuel tanks according to claim 1, characterized in that: The intelligent analysis strategy module's dynamic prediction model for remaining fuel tank capacity relies on the nonlinear fitting capability of deep learning models for multi-source parameters. It achieves accurate prediction of remaining capacity in three steps: First, based on electrochemical impedance spectroscopy feature extraction technology, the real-time power generation efficiency curve of the system is calculated; second, the power integral method is used to substitute the real-time power generation efficiency into the power-hydrogen consumption conversion formula to accurately calculate the hydrogen consumption per unit time; finally, the real-time temperature and pressure parameters of the fuel tank are coupled, and the correlation deviation of temperature-pressure-hydrogen quantity in the gas state equation is corrected through a deep learning model. Combining the consumed hydrogen quantity with the initial hydrogen storage quantity, the remaining capacity of the fuel tank is dynamically estimated.

4. The data acquisition and analysis device for hydrogen production from fuel tanks according to claim 1, characterized in that: The multi-dimensional safety performance evaluation system is based on pressure-temperature parameter coupling analysis and combines the feature recognition and risk prediction capabilities of deep learning models to construct a multi-modal safety evaluation model. Through condition-adaptive pressure and temperature anomaly identification and full life cycle thermal runaway risk classification monitoring, it achieves 30-minute advance graded early warning of safety risks.

5. The fuel tank hydrogen production data acquisition and analysis device according to claim 1, characterized in that: The differentiated early warning strategy is as follows: for low-risk situations, an early warning is issued 30 minutes in advance, and a power reduction suggestion is pushed out. Medium risk triggers enhanced cooling system operation; high risk activates hydrogen supply flow restriction protection; emergency The system immediately cuts off the load and initiates an emergency pressure relief procedure to achieve full-scenario, full-cycle safety protection.

6. The data acquisition and analysis device for hydrogen production from fuel tanks according to claim 1, characterized in that: The closed-loop control system adopts a PID control algorithm with automatic parameter tuning function, and builds a dual-loop closed-loop control architecture of temperature feedback-heating power regulation and pressure feedback-water injection rate regulation to achieve precise control and automatic optimization of hydrogen production process parameters.

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