Miniaturized hydrogen production and hydrogen supplementation integrated device and unmanned aerial vehicle outfield rapid energy supplementation method

Abstract

The invention discloses a miniaturized hydrogen production and supplementation integrated device and an unmanned aerial vehicle external field rapid energy supplementation method, and relates to the technical field of electromagnetic induction heating and hydrogen production. The miniaturized hydrogen production and supplementation integrated device comprises a parameter acquisition and optimization module, and the parameter acquisition and optimization module is used for acquiring induction coil parameters in the miniaturized device and initial attribute data of a magnetic material; the magnetic material configuration module is used for obtaining magnetic conductivity and thermal conductivity data of a magnetic material under the condition that the temperature is higher than a preset threshold value according to the enhanced electromagnetic field intensity distribution model, and if the magnetic conductivity is lower than the preset threshold value, the magnetic material configuration module is used for configuring the magnetic material; if yes, adjusting the material component proportion, and determining an optimized magnetic material configuration scheme; according to the miniaturized hydrogen production and hydrogen supplement integrated device and the unmanned aerial vehicle outfield rapid energy supplement method, the energy efficiency and the operation stability of the miniaturized device are remarkably improved, and an innovative solution is provided for efficient hydrogen production.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic induction heating and hydrogen production technology, specifically to a miniaturized integrated hydrogen production and replenishment device and a method for rapid energy replenishment of unmanned aerial vehicles (UAVs) in the field. Background Technology

[0002] Electromagnetic induction heating technology holds significant importance in the field of energy conversion and utilization. It achieves efficient energy transfer through the interaction of electromagnetic fields and conductive materials, providing a crucial pathway for clean energy production, particularly in high-energy-consuming scenarios such as hydrogen production, where it demonstrates significant energy-saving potential. The hydrogen production process requires precise temperature control to ensure reaction efficiency, and electromagnetic induction heating, with its rapid response and localized heating characteristics, has become a core direction driving the development of green hydrogen production technologies.

[0003] However, the application of existing technologies in miniaturized devices still faces numerous challenges, limiting their widespread adoption in practical scenarios. Currently, most hydrogen production heating solutions rely on traditional resistance heating or combustion heating, methods that are insufficient in energy transfer efficiency and temperature uniformity. Resistance heating often leads to uneven heat distribution, localized overheating or underheating, affecting reactant conversion rates. Combustion heating, due to fuel dependence and emissions issues, contradicts the concept of green hydrogen production. Furthermore, these solutions struggle to achieve rapid response and precise temperature control in miniaturized devices, especially in dynamic hydrogen replenishment systems, where temperature fluctuations directly impact hydrogen mixing efficiency, resulting in energy waste and a decrease in reaction rate. In electromagnetic induction heating technology, the design of compact induction coils has become a primary technical challenge. Induction coils need to generate a sufficiently strong electromagnetic field within a limited space to drive the magnetic material to heat up rapidly. However, coil miniaturization leads to insufficient electromagnetic field strength, reduced heating efficiency, and difficulty in meeting the high-temperature environment required for hydrogen production reactions. At a deeper level, the selection of magnetic materials and their compatibility with the coil further exacerbate the problem. Magnetic materials need to maintain stable magnetic permeability and thermal conductivity under strong electromagnetic fields. However, existing materials are prone to magnetic degradation under high temperature or high frequency conditions, resulting in uneven heating and affecting the mixing effect of hydrogen and hydrogen production products in the hydrogen replenishment system. Summary of the Invention

[0004] The purpose of this invention is to provide a miniaturized integrated hydrogen production and replenishment device and a rapid field refueling method for unmanned aerial vehicles (UAVs), thereby solving the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a miniaturized integrated hydrogen production and replenishment device, comprising:

[0006] The parameter acquisition and optimization module collects the parameters of the induction coil and the initial property data of the magnetic material in the miniaturized device, and uses a genetic algorithm to optimize the geometric parameters of the coil to obtain an enhanced electromagnetic field intensity distribution model.

[0007] The magnetic material configuration module obtains the magnetic permeability and thermal conductivity data of the magnetic material under the condition that the temperature is higher than the preset threshold, based on the enhanced electromagnetic field intensity distribution model. If the magnetic permeability is lower than the preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme.

[0008] The heat distribution identification module extracts thermal conductivity indicators from the optimized magnetic material configuration scheme. Based on the temperature fluctuation data in the hydrogen production process, it uses a support vector machine to classify areas with uneven heat distribution and determine the location of potential heating deviations.

[0009] The thermal field simulation and path planning module obtains dynamic temperature coordination requirement data based on the determined heating deviation position, and uses the finite element simulation method to calculate the thermal field distribution under coil and material matching to obtain uniform heating path planning.

[0010] The heating control module extracts the path node temperature values ​​from the uniform heating path planning. If the node temperature value exceeds the threshold required for high-temperature environment, it iteratively adjusts the coil current frequency to determine the corrected heating control parameters.

[0011] The energy optimization module integrates hydrogen mixing effect simulation data with the corrected heating control parameters and uses a genetic algorithm to optimize the reaction efficiency index, resulting in the final energy utilization efficiency improvement model.

[0012] Preferably, the parameter acquisition and optimization module acquires the parameters of the induction coil and the initial property data of the magnetic material in the miniaturized device, and optimizes the geometric structure parameters of the coil using a genetic algorithm to obtain an enhanced electromagnetic field intensity distribution model, including:

[0013] The initial geometric parameters of the induction coil and the initial property data of the magnetic material in the miniaturized device are obtained. The initial electromagnetic field intensity distribution is calculated using the finite element simulation tool to obtain the first electromagnetic field distribution data.

[0014] Based on the first electromagnetic field distribution data, a genetic algorithm is used to optimize the number of coil turns, coil radius, and coil length of the induction coil to obtain an optimized second set of geometric parameters;

[0015] If the magnetic field uniformity of the second geometric parameter set is greater than the preset threshold, the electromagnetic field intensity distribution is recalculated based on the second geometric parameter set and the initial properties of the magnetic material using a finite element simulation tool to obtain the second electromagnetic field distribution data.

[0016] By comparing the magnetic field uniformity and intensity values ​​of the second electromagnetic field distribution data with those of the first electromagnetic field distribution data, it is determined whether the second electromagnetic field distribution data meets the objective function value requirements, thus obtaining the enhanced electromagnetic field intensity distribution model.

[0017] Preferably, the magnetic material configuration module acquires the magnetic permeability and thermal conductivity data of the magnetic material under high-temperature conditions based on the enhanced electromagnetic field intensity distribution model. If the magnetic permeability is lower than a preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme, including:

[0018] The magnetic permeability and thermal conductivity data of the magnetic material are obtained from experimental data under conditions where the temperature is higher than a preset threshold. The magnetic field intensity distribution is calculated using finite element simulation software to obtain the first magnetic permeability and first thermal conductivity data.

[0019] If the first permeability is lower than a preset threshold, the composition ratio of the magnetic material is adjusted by an iterative optimization algorithm, and the composition ratio is randomly sampled by the Monte Carlo method to determine the first composition ratio scheme.

[0020] Based on the first component ratio scheme, the material properties under high temperature conditions were recalculated using thermodynamic simulation software to obtain the second magnetic permeability and second thermal conductivity data;

[0021] By comparing the second magnetic permeability with a preset threshold, it is determined whether the requirements are met. If they are met, the first component ratio scheme is stored in the database to obtain the final material configuration data.

[0022] Preferably, the heat distribution identification module extracts thermal conductivity indicators from the optimized magnetic material configuration scheme, and, based on temperature fluctuation data during the hydrogen production reaction, uses a support vector machine to classify areas of uneven heat distribution and determine potential heating deviation locations, including:

[0023] Thermal conductivity data were obtained from the optimized magnetic material configuration scheme. The temperature fluctuation data during the hydrogen production process were meshed using finite element analysis tools to obtain the temperature distribution matrix.

[0024] Based on the temperature distribution matrix, a support vector machine classification algorithm is used to divide the uneven heat distribution regions and determine the set of uneven heat distribution regions.

[0025] Spatial coordinate data are extracted from the set of uneven heat regions, and a three-dimensional interpolation algorithm is used to finely locate the heating deviation position to determine the boundary of the deviation region.

[0026] If the boundary of the deviation region exceeds a preset threshold, the thermal conductivity of the deviation region is adjusted using a thermal flow simulation tool to obtain an optimized heat distribution configuration.

[0027] Preferably, the thermal field simulation and path planning module, based on the determined heating deviation location, obtains dynamic temperature coordination requirement data, and uses the finite element simulation method to calculate the thermal field distribution under coil and material matching, thereby obtaining uniform heating path planning including:

[0028] Based on the location of the heating deviation, dynamic temperature coordination requirement data is obtained from a pre-established temperature coordination database. Data mining algorithms are used to extract temperature distribution features related to coil parameters and material matching to obtain the requirement data set.

[0029] If the temperature distribution characteristics in the required data set meet the preset heat conduction efficiency threshold, then the finite element simulation method is used to calculate the thermal field distribution by combining coil parameters and material matching data, and the thermal field distribution matrix is ​​obtained.

[0030] Based on the thermal field distribution matrix, the heating area is spatially discretized using a grid partitioning technique to extract key nodes for uniform heating and determine the initial planning of the uniform heating path.

[0031] The initial plan is optimized by a path planning algorithm. The coil parameters are adjusted by combining temperature distribution and heat conduction efficiency to obtain the uniform heating path plan.

[0032] Preferably, the heating control module extracts the path node temperature values ​​from the uniform heating path planning. If the node temperature value exceeds the high-temperature environment requirement threshold, iteratively adjusts the coil current frequency to determine the corrected heating control parameters, including:

[0033] Based on the pre-established path planning data, the temperature sensor data mapping method is used to obtain the temperature value of each path node for its coordinates, thus obtaining a node temperature dataset.

[0034] For the node temperature dataset, a threshold judgment logic is used. If the temperature value is higher than the preset high temperature environment threshold, the path node corresponding to the temperature value is selected to obtain the set of nodes that need to be adjusted.

[0035] For each node in the set of nodes that need adjustment, an iterative optimization algorithm is used to gradually change the coil current frequency, obtain the adjusted temperature value, and determine the set of frequency parameters that meet the requirements of high-temperature environment.

[0036] Based on the set of frequency parameters, a parameter mapping method is used to convert the set of frequency parameters into heating control parameters, resulting in a corrected control parameter dataset.

[0037] Preferably, the energy optimization module, through the corrected heating control parameters, integrates hydrogen mixing effect simulation data and uses a genetic algorithm to optimize the reaction efficiency index, obtaining the final energy utilization efficiency improvement model, including:

[0038] An initial dataset of hydrogen mixing effect is obtained from pre-established simulation data. The heating control parameters and hydrogen mixing ratio in the initial dataset are normalized using a data integration method to obtain an integrated mixing effect dataset.

[0039] Based on the integrated hybrid effect dataset, a genetic algorithm is used to iteratively optimize the reaction efficiency index, determine whether the reaction efficiency index has reached a preset threshold, and obtain an optimized set of control parameters.

[0040] If the optimized set of control parameters meets the preset threshold, the heating control parameters are adjusted by the parameter correction method to obtain the corrected heating control parameters and the updated reaction efficiency index.

[0041] Based on the updated reaction efficiency index, an energy utilization efficiency calculation method is used, combined with the corrected heating control parameters and the hydrogen mixing effect data, to obtain the final energy utilization efficiency improvement result.

[0042] Preferably, it also includes a system feedback coordination module, which obtains real-time feedback data from the hydrogen replenishment system based on the final energy utilization efficiency improvement model. If the feedback data shows a deviation in the mixing effect, the selection of magnetic materials is updated retrospectively to determine the overall system coordination. Specifically, this includes:

[0043] Real-time feedback data is obtained from the hydrogen replenishment system, and the mixing effect related parameters are extracted through the data acquisition frequency control module. If the mixing effect related parameters do not reach the preset threshold, the mixing effect deviation data is determined.

[0044] Based on the mixing effect deviation data, the real-time feedback data is processed by a real-time data analysis module to extract key features affecting the mixing effect and determine the magnetic material performance parameters that need to be traced back.

[0045] For the performance parameters of the magnetic material, a pre-established material database is used to query magnetic materials that meet the performance requirements, and an optimized magnetic material selection scheme is obtained.

[0046] The optimized magnetic material selection scheme is integrated with the real-time feedback data through the system optimization and adjustment module. If the integrated data meets the system coordination requirements, the adjusted system operating parameters are obtained.

[0047] Preferably, it also includes a deployment configuration module, which extracts coordination indicators from the determined overall system coordination, generates deployment configurations for miniaturized device deployment scenarios through data fusion methods, and obtains a stable operation plan for the hydrogen production process, specifically including:

[0048] Operating parameters are obtained from sensor data of miniaturized devices, and the temperature, pressure, and flow data are weighted and averaged using data fusion methods to obtain overall coordination indicators.

[0049] If the overall coordination index is lower than a preset threshold, the device operating parameters are adjusted through a parameter optimization algorithm to generate a first deployment configuration;

[0050] Based on the first deployment configuration and combined with environmental adaptability data, the hydrogen production process is checked for data consistency through a real-time monitoring module to obtain the second deployment configuration.

[0051] Using the second deployment configuration, combined with a pre-established hydrogen production stability model, the operating status is evaluated through logical judgment methods to determine the final stable hydrogen production operation scheme.

[0052] A method for rapid field refueling of a miniaturized hydrogen production and refueling drone, employing the aforementioned miniaturized integrated hydrogen production and refueling device, the method comprising:

[0053] The parameters of the induction coil and the initial property data of the magnetic material in the UAV's field power replenishment device were collected. The geometric parameters of the coil were optimized by a genetic algorithm to obtain the enhanced electromagnetic field intensity distribution model.

[0054] Based on the enhanced electromagnetic field intensity distribution model, the magnetic permeability and thermal conductivity data of the magnetic material under high temperature conditions are obtained. If the magnetic permeability is lower than the preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme.

[0055] Thermal conductivity indices were extracted from the optimized magnetic material configuration scheme. Based on the temperature fluctuation data during the hydrogen production reaction, support vector machines were used to classify areas with uneven heat distribution and identify potential heating deviation locations.

[0056] Based on the determined heating deviation location, obtain dynamic temperature coordination requirement data, use the finite element simulation method to calculate the thermal field distribution under coil and material matching, and obtain uniform heating path planning;

[0057] The path node temperature values ​​are extracted from the uniform heating path planning. If the node temperature value exceeds the threshold required for high temperature environment, the coil current frequency is iteratively adjusted to determine the corrected heating control parameters.

[0058] By integrating hydrogen mixing effect simulation data with the corrected heating control parameters, and using a genetic algorithm to optimize the reaction efficiency index, the final energy utilization efficiency improvement model is obtained.

[0059] Based on the final energy utilization efficiency improvement model, real-time feedback data of the hydrogen replenishment system is obtained. If the feedback data shows a deviation in the mixing effect, the selection of magnetic materials is updated retrospectively to determine the overall system coordination.

[0060] By extracting coordination indicators from the overall system coordination assessment, and using data fusion methods to generate deployment configurations for the deployment scenario of miniaturized UAV field refueling devices, a stable operation scheme for the hydrogen production and refueling process is obtained.

[0061] As can be seen from the above technical solution, the present invention has the following beneficial effects:

[0062] This miniaturized integrated hydrogen production and replenishment device and its rapid UAV-based field refueling method utilizes data collected from induction coil parameters and initial properties of magnetic materials. A genetic algorithm is employed to optimize the coil geometry, constructing an enhanced electromagnetic field distribution model. Data on magnetic permeability and thermal conductivity at high temperatures is obtained; if the permeability falls below a threshold, the material composition is adjusted for optimal configuration. The invention uses support vector machines to classify uneven heat distribution areas, identify heating deviations, and combines finite element method (FEM) simulations to calculate the thermal field distribution and plan a uniform heating path. If a node temperature exceeds a threshold, the coil current frequency is iteratively adjusted to correct heating control parameters. Simulated data on hydrogen mixing effects is integrated, and a genetic algorithm is used to optimize reaction efficiency, ultimately forming an energy utilization efficiency improvement model. This invention uses real-time feedback data to retrospectively update material selection and combines data fusion to generate deployment configurations, ensuring stable operation of the hydrogen production process. Overall, the technology significantly improves the energy efficiency and operational stability of the miniaturized device, providing an innovative solution for efficient hydrogen production. Attached Figure Description

[0063] Figure 1 This is a module connection diagram of the miniaturized integrated hydrogen production and replenishment device of the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] like Figure 1 As shown, the present invention provides a technical solution: a miniaturized integrated hydrogen production and replenishment device, comprising:

[0066] The parameter acquisition and optimization module collects the parameters of the induction coil and the initial property data of the magnetic material in the miniaturized device, and uses a genetic algorithm to optimize the geometric parameters of the coil to obtain an enhanced electromagnetic field intensity distribution model.

[0067] The magnetic material configuration module obtains the magnetic permeability and thermal conductivity data of the magnetic material under the condition that the temperature is higher than the preset threshold, based on the enhanced electromagnetic field intensity distribution model. If the magnetic permeability is lower than the preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme.

[0068] The heat distribution identification module extracts thermal conductivity indicators from the optimized magnetic material configuration scheme. Based on the temperature fluctuation data in the hydrogen production process, it uses a support vector machine to classify areas with uneven heat distribution and determine the location of potential heating deviations.

[0069] The thermal field simulation and path planning module obtains dynamic temperature coordination requirement data based on the determined heating deviation position, and uses the finite element simulation method to calculate the thermal field distribution under coil and material matching to obtain uniform heating path planning.

[0070] The heating control module extracts the path node temperature values ​​from the uniform heating path planning. If the node temperature value exceeds the threshold required for high-temperature environment, it iteratively adjusts the coil current frequency to determine the corrected heating control parameters.

[0071] The energy optimization module integrates hydrogen mixing effect simulation data through the corrected heating control parameters and uses a genetic algorithm to optimize the reaction efficiency index, thus obtaining the final energy utilization efficiency improvement model.

[0072] The system feedback coordination module obtains real-time feedback data from the hydrogen replenishment system based on the final energy utilization efficiency improvement model. If the feedback data shows a deviation in the mixing effect, the selection of magnetic materials is updated retrospectively to determine the overall system coordination.

[0073] The deployment configuration module extracts coordination indicators from the overall system coordination assessment. For miniaturized device deployment scenarios, it generates deployment configurations through data fusion methods to obtain a stable operation plan for the hydrogen production process.

[0074] The core principle of this implementation lies in achieving adaptive coordination between hydrogen production and replenishment processes through a multi-level optimization algorithm and a thermal field coupling model. The parameter acquisition and optimization module utilizes a genetic algorithm to globally optimize the geometric parameters of the induction coil (including the number of turns, inner and outer diameters, and spacing) to form an electromagnetic field distribution enhancement model, ensuring magnetic field uniformity. The magnetic material configuration module dynamically adjusts the composition ratio of magnetic materials (such as iron-based, nickel-based, or cobalt-based alloys) based on experimental data and high-temperature simulation results to optimize the matching relationship between their permeability and thermal conductivity. The thermal distribution identification module classifies the temperature time-series data of the hydrogen production reactor based on the support vector machine (SVM) algorithm, identifying thermal deviation regions. Subsequently, the thermal field simulation and path planning module combines the finite element method (FEM) to simulate the coil heating process, generating a thermal field equipotential surface distribution map and planning the optimal heating path. The heating control module adaptively adjusts the current frequency and power density according to this path to achieve efficient and uniform heating. The energy optimization module, based on this, introduces a multi-objective optimization strategy using a genetic algorithm, obtaining the optimal energy efficiency parameters through comprehensive analysis of the hydrogen reaction rate and energy consumption ratio. The system feedback coordination module takes real-time sensor data as input and compares it with the output of the prediction model. If a deviation occurs, it triggers a linkage update mechanism for material and control parameters. Finally, the deployment and configuration module generates a system deployment plan based on the fused multi-dimensional operational data to achieve dynamic and stable control of the hydrogen production reaction process.

[0075] This device achieves synergistic optimization of hydrogen production and replenishment processes by introducing genetic algorithms, support vector machines, and finite element thermal field simulation, significantly improving system energy utilization efficiency and heating uniformity. Compared with traditional devices, this invention can automatically identify thermal deviation areas and adjust the heating path, reducing local overheating or energy waste and improving hydrogen production rate stability by approximately 20%-30%. Furthermore, through optimization of the high-temperature characteristics of magnetic materials and a real-time feedback adjustment mechanism, the system possesses strong adaptability and long-term operational reliability. The overall device has a compact structure, is suitable for miniaturized applications, and can be widely used in vehicle-mounted hydrogen production units, portable energy systems, and emergency energy replenishment.

[0076] The parameter acquisition and optimization module collects induction coil parameters and initial property data of magnetic materials in the miniaturized device, and uses a genetic algorithm to optimize the coil's geometric parameters to obtain an enhanced electromagnetic field intensity distribution model, including:

[0077] The initial geometric parameters of the induction coil and the initial property data of the magnetic material in the miniaturized device are obtained. The initial electromagnetic field intensity distribution is calculated using the finite element simulation tool to obtain the first electromagnetic field distribution data.

[0078] Based on the first electromagnetic field distribution data, a genetic algorithm is used to optimize the number of coil turns, coil radius, and coil length of the induction coil to obtain an optimized second set of geometric parameters;

[0079] If the magnetic field uniformity of the second geometric parameter set is greater than the preset threshold, the electromagnetic field intensity distribution is recalculated based on the second geometric parameter set and the initial properties of the magnetic material using a finite element simulation tool to obtain the second electromagnetic field distribution data.

[0080] By comparing the magnetic field uniformity and intensity values ​​of the second electromagnetic field distribution data with those of the first electromagnetic field distribution data, it is determined whether the second electromagnetic field distribution data meets the objective function value requirements, thus obtaining the enhanced electromagnetic field intensity distribution model.

[0081] In this embodiment, the parameter acquisition and optimization module operates as a multi-stage electromagnetic field modeling and optimization process based on a combination of finite element simulation calculation and genetic algorithm optimization. Its aim is to obtain the optimal solution for the electromagnetic field intensity distribution through precise adjustment of coil geometric parameters, thereby achieving efficient energy transfer and magnetic field uniformity control in a miniaturized hydrogen production device within a confined space. The specific implementation process is as follows.

[0082] First, the system comprehensively measures the initial geometric parameters of the induction coil using its built-in parameter acquisition unit. The acquired geometric parameters include coil radius, coil length, number of turns, and coil spacing. The coil radius refers to the distance from the coil's central axis to the center of the conductor; this value is determined by the physical dimensions of the device and is generally in the range of 5 mm to 50 mm. The coil length refers to the total axial length of the coil, determined according to the design space, and is generally between 10 mm and 200 mm. The number of turns refers to the number of turns the conductor is wound; its initial value is set according to the required magnetic field coverage, and is generally between 10 and 200 turns. The coil spacing refers to the distance between adjacent conductors; this value affects the magnetic field superposition effect and is generally between 0.5 mm and 5 mm.

[0083] Simultaneously, the system collects initial property data of the magnetic material, including permeability, coercivity, and saturation magnetic flux density. Permeability characterizes the material's response to a magnetic field, and its initial value is obtained through experimental measurements of material samples at high temperatures. Coercivity represents the strength of the external magnetic field required to demagnetize the material and is determined through hysteresis loop testing. Saturation magnetic flux density is the maximum magnetic flux that the material can withstand in a saturated state, and it is measured using a magnetic property testing device.

[0084] After obtaining the above parameters, the system calls the finite element simulation tool to model the coupling model of the induction coil and the magnetic material. The modeling steps include establishing the geometric structure model, defining material properties, setting boundary conditions, and inputting the initial current excitation. The simulation process divides the three-dimensional space into fine mesh cells, calculates the magnetic flux distribution and magnetic field strength within each cell, and thus obtains the first electromagnetic field distribution data for the entire coil region. This data includes the magnetic field strength value and magnetic field direction information at each location in space. Subsequently, the system performs statistical analysis on this data to calculate the uniformity index of the magnetic field strength distribution. Magnetic field uniformity is measured by calculating the ratio of the standard deviation to the mean of the magnetic field strength of each mesh; the smaller the value, the more uniform the magnetic field.

[0085] Next, the system activates the genetic algorithm module to globally optimize the geometric parameters of the coils. The optimization goal of the genetic algorithm is to make the magnetic field uniformity index as close as possible to the preset target value. The algorithm first generates a set of initial samples containing different combinations of coil parameters, with each sample corresponding to a set of coil radius, coil length, and number of turns parameters. The system uses the aforementioned finite element simulation process to calculate the magnetic field distribution under each set of parameters and calculates the magnetic field uniformity index and the average magnetic field strength for each sample. The algorithm then sorts the samples according to the quality of the index and selects the samples with better performance for parameter crossover and mutation operations to generate a new generation of parameter combinations. After each generation of optimization, the system calls the simulation again to calculate the new electromagnetic field distribution and repeats the above evaluation process. After multiple generations of iteration, when the magnetic field uniformity index reaches the preset threshold or the difference between the optimization results of consecutive generations is less than 1%, the system determines that the algorithm has converged and obtains the second set of geometric parameters.

[0086] The preset threshold was determined through experimental calibration. Specifically, the relationship curve between the uniformity of the magnetic field distribution and the efficiency of the hydrogen production reaction under different coil parameter conditions was obtained through experimental fitting. When the uniformity of the magnetic field strength is below 5%, the improvement in reaction rate and energy utilization tends to stabilize. Therefore, 5% is set as the threshold standard for magnetic field uniformity. This value can be adjusted according to the energy requirements and reaction space size of different sized devices, and the value range is generally from 3% to 8%.

[0087] Once the magnetic field uniformity corresponding to the second set of geometric parameters obtained by the algorithm exceeds a threshold standard, the system re-invokes the finite element simulation module, inputs the optimized coil radius, coil length, number of coil turns, and other parameters, and keeps the initial properties of the magnetic material unchanged, and recalculates the electromagnetic field strength. This simulation process outputs the second electromagnetic field distribution data.

[0088] Subsequently, the system compares the second electromagnetic field distribution data with the first electromagnetic field distribution data point by point, calculating the percentage change in magnetic field strength and the improvement in spatial uniformity. If the improvement in magnetic field strength of the second electromagnetic field distribution is greater than 10% and the magnetic field uniformity index is lower than a preset threshold, then the parameter set is determined to meet the objective function requirements, and is ultimately identified as the enhanced electromagnetic field strength distribution model. If the conditions are not met, the system returns the parameter set to the genetic algorithm for further optimization until the convergence criterion is met.

[0089] The magnetic material configuration module, based on the enhanced electromagnetic field intensity distribution model, obtains the magnetic permeability and thermal conductivity data of the magnetic material under high-temperature conditions. If the magnetic permeability is lower than a preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme, including:

[0090] The magnetic permeability and thermal conductivity data of the magnetic material are obtained from experimental data under conditions where the temperature is higher than a preset threshold. The magnetic field intensity distribution is calculated using finite element simulation software to obtain the first magnetic permeability and first thermal conductivity data.

[0091] If the first permeability is lower than a preset threshold, the composition ratio of the magnetic material is adjusted by an iterative optimization algorithm, and the composition ratio is randomly sampled by the Monte Carlo method to determine the first composition ratio scheme.

[0092] Based on the first component ratio scheme, the material properties under high temperature conditions were recalculated using thermodynamic simulation software to obtain the second magnetic permeability and second thermal conductivity data;

[0093] By comparing the second magnetic permeability with a preset threshold, it is determined whether the requirements are met. If they are met, the first component ratio scheme is stored in the database to obtain the final material configuration data.

[0094] In this embodiment, the operation of the magnetic material configuration module is a comprehensive optimization process based on experimental measurement data, finite element simulation analysis, iterative optimization calculations, and Monte Carlo random sampling under high-temperature conditions. Its core objective is to achieve optimal values ​​for the magnetic permeability and thermal conductivity of the magnetic material under high-temperature conditions, matching the enhanced electromagnetic field intensity distribution model. This ensures that the miniaturized hydrogen production and replenishment device achieves a balanced transfer of electromagnetic and thermal energy during high-temperature operation. The entire process includes five stages: parameter acquisition, threshold judgment, component ratio optimization, performance recalculation, and result verification.

[0095] First, the system measures the performance of magnetic materials under experimental conditions where the temperature exceeds a preset threshold. The temperature threshold is determined by measuring the change in magnetic permeability of the magnetic material at different temperatures (300°C, 500°C, 700°C, and 900°C). When the temperature rises to 700°C, the magnetic permeability begins to decrease significantly, and the rate of decrease accelerates considerably. Tests show that the magnetic properties of the material decay by more than 10% after this temperature point; therefore, 700°C is established as the high-temperature threshold temperature standard. This temperature value can be adjusted for different material systems, typically within the range of 600°C to 800°C. Experimental measurements are performed using a standard high-temperature magnetic property tester, outputting magnetic permeability data at high temperatures and thermal conductivity data from a thermal conductivity tester. Magnetic permeability represents the material's ability to respond to magnetic flux under a unit magnetic field strength; its value is obtained by measuring the ratio of magnetic flux to magnetic field strength. Thermal conductivity describes the material's ability to transfer heat energy per unit time under a unit temperature difference; its value is obtained through steady-state heat flow measurement. For each test, no fewer than 10 sets of data were collected, and the average value was taken as the first magnetic permeability and first thermal conductivity data at that temperature.

[0096] Secondly, the system inputs the experimentally obtained first permeability and first thermal conductivity data into the finite element simulation software. The software establishes a three-dimensional calculation model based on the enhanced electromagnetic field intensity distribution model, including the magnetic field region, material geometry, and boundary conditions. The specific steps of the simulation calculation are as follows: the geometry of the magnetic material sample is divided into multiple micro-units, and the local magnetic field strength, magnetic flux density, and heat flux density are calculated for each unit under high-temperature conditions; the average permeability and average thermal conductivity of the entire material region are obtained through cumulative calculation of each unit. The calculation results are output in numerical tabular form, and the system automatically extracts the spatial distribution mean and standard deviation corresponding to the first permeability and first thermal conductivity. If the permeability of the simulation result is lower than the preset threshold set by the system, the system proceeds to the composition optimization stage.

[0097] The preset threshold is determined by comparing the material's permeability at room temperature with the permeability change curve at high temperature. Experimental statistics show that when the permeability at high temperature drops to 80% of the permeability at room temperature, the electromagnetic induction heating efficiency decreases by more than 15%, and the heat transfer delay exceeds 10%. Therefore, 80% of the permeability at room temperature is used as the preset permeability threshold. For example, if the permeability at room temperature is 100 units, the threshold is 80 units. The system uses this value as a benchmark for judgment.

[0098] When the first permeability falls below the threshold, the system executes a composition ratio optimization process. Composition optimization is achieved through an iterative optimization algorithm. The algorithm's execution flow is as follows: First, the basic composition range of the material is set, for example, the mass fraction of iron is between 60% and 80%, the mass fraction of nickel is between 20% and 40%, and the mass fraction of cobalt is between 0% and 5%. Then, the system randomly generates initial samples according to this ratio range, with each sample representing a specific composition ratio scheme. After sample generation, the system uses the Monte Carlo method for random sampling. The number of samplings is set according to the optimization accuracy, typically between 100 and 1000 times. Each sampling generates a new composition combination, and the system records each combination and sequentially inputs it into the thermodynamic simulation software for performance prediction.

[0099] The thermodynamic simulation process consists of three steps. First, it calculates the phase composition changes of each element at high temperatures. Using high-temperature phase diagrams of different elements from a database, it determines whether non-magnetic phases, such as austenite or amorphous phases, are generated under different compositional proportions. If the proportion of non-magnetic phases exceeds 10%, the combination is marked as unacceptable and automatically discarded. Second, the system simulates the change in magnetic domain structure with increasing temperature. By minimizing the energy difference between internal and external magnetic fields, it estimates the impact of domain rearrangement on overall permeability. This calculation is completed automatically within the software, outputting the average permeability value at high temperatures. Third, the system calculates the effect of lattice thermal vibrations on thermal conductivity. Using a statistical model of the average interatomic distance and phonon transport paths in the material's crystal structure, it calculates the effective thermal conductivity value at high temperatures. After these three calculation steps, the system outputs the second permeability and second thermal conductivity data.

[0100] Next, the system compares the second permeability with a preset threshold. If the second permeability is greater than or equal to the threshold, and the thermal conductivity is within the design requirement range (determined by thermal balance experiments, typically from 10 W / m / °C to 30 W / m / °C), then the composition ratio is deemed acceptable. If the second permeability is still below the threshold, the system automatically adjusts the element ratios, fine-tuning them based on the increase in the second permeability. For example, when the permeability is still 10% below the threshold, the system automatically increases the iron ratio by 2% to 5%, and decreases the nickel ratio by the same amount, then performs Monte Carlo random sampling and thermodynamic simulation again, repeating the above calculation process until the threshold condition is met.

[0101] During the iteration process, the system continuously records the magnetic permeability and thermal conductivity values ​​corresponding to each component ratio combination. When the change in magnetic permeability is less than 1% and the change in thermal conductivity is less than 1% for three consecutive sets of data, the system determines that the optimization process has converged and no longer performs new sampling calculations. The component ratio scheme at this time is the first component ratio scheme. The system stores this scheme, along with the corresponding second magnetic permeability, second thermal conductivity, high-temperature test temperature, and simulation data, into the database to form the final material configuration data. The database uses an indexed storage method, allowing subsequent modules to directly call the material configuration scheme by magnetic field model number for device manufacturing and operation control.

[0102] The heat distribution identification module extracts thermal conductivity indicators from the optimized magnetic material configuration scheme. Based on temperature fluctuation data during the hydrogen production reaction, it uses a support vector machine to classify areas of uneven heat distribution and identify potential heating deviation locations, including:

[0103] Thermal conductivity data were obtained from the optimized magnetic material configuration scheme. The temperature fluctuation data during the hydrogen production process were meshed using finite element analysis tools to obtain the temperature distribution matrix.

[0104] Based on the temperature distribution matrix, a support vector machine classification algorithm is used to divide the uneven heat distribution regions and determine the set of uneven heat distribution regions.

[0105] Spatial coordinate data are extracted from the set of uneven heat regions, and a three-dimensional interpolation algorithm is used to finely locate the heating deviation position to determine the boundary of the deviation region.

[0106] If the boundary of the deviation region exceeds a preset threshold, the thermal conductivity of the deviation region is adjusted using a thermal flow simulation tool to obtain an optimized heat distribution configuration.

[0107] In this embodiment, the working principle of the heat distribution identification module is based on the dynamic analysis of the thermal conductivity characteristics of magnetic materials and the temperature field of the hydrogen production reaction process. Through temperature data gridding modeling, classification and identification, spatial interpolation calculation and heat flow simulation feedback, the heating deviation position inside the device is accurately determined, and dynamic correction of uniform heat field distribution is achieved.

[0108] First, the system reads thermal conductivity data from the optimized material scheme output by the magnetic material configuration module. This thermal conductivity index represents the rate of heat transfer per unit temperature difference, with units of watts per meter per degree Celsius. This parameter is obtained through prior thermodynamic simulations, using the average thermal conductivity of the material within the actual operating temperature range of the device (typically 600 to 800 degrees Celsius) as the input value. The system then inputs this index into a finite element analysis tool to define the thermal conductivity properties of each material region.

[0109] Secondly, the system collects temperature fluctuation data in real time during device operation. The number of collection points is determined based on the spatial dimensions of the hydrogen production reaction chamber, typically with no fewer than 100 temperature measurement points set in three-dimensional space to ensure the accuracy of heat distribution sampling. Each temperature measurement point collects a temperature value every second, and after continuous recording for a period of time, a temperature time-series dataset is formed. The system imports this data into a finite element analysis tool to perform three-dimensional meshing of the entire hydrogen production reaction zone. The meshing process discretizes the reaction region into multiple cubic elements, with the side length of each element set according to the device dimensions, generally between 1 mm and 5 mm. The system calculates the mean temperature and temperature gradient within each mesh element, thereby generating a temperature distribution matrix. The temperature distribution matrix stores the temperature data of each mesh node in the space in a row and column format, providing a basis for subsequent classification calculations.

[0110] Next, the system inputs the temperature distribution matrix into the support vector machine (SVM) classification algorithm model. This algorithm model is pre-trained with samples derived from experimentally obtained heat distribution data, including samples from both uniformly heated and unevenly heated states. The system uses the rate of change of the temperature gradient as a feature parameter in the classification calculation. The rate of change of the temperature gradient is calculated by averaging the temperature differences between adjacent grid nodes to obtain the temperature change rate for each node. During classification, the algorithm uses a temperature change rate greater than 1.5 times the overall average rate as the classification threshold. Nodes with a rate of change higher than this threshold are identified as "heat concentration areas," nodes with a rate of change lower than this threshold are identified as "heat deficiency areas," and other nodes are identified as "heat balance areas." Through this classification process, the system identifies a set of regions with uneven heat distribution and forms a set of non-uniform region data. Each non-uniform region data includes its starting coordinates, ending coordinates, average temperature value, and corresponding volume range in three-dimensional space.

[0111] Subsequently, the system extracts three-dimensional coordinate data from the set of non-uniform regions and precisely locates each region. The localization process employs a three-dimensional interpolation algorithm. The interpolation algorithm's calculation steps are as follows: First, it calculates the spatial distance and temperature difference between the boundary nodes of each non-uniform region; then, it estimates the coordinates of the continuous temperature change surface through linear interpolation between nodes. Second, it summarizes the interpolation results to form a spatial isothermal surface. Finally, the system calculates the overlap error between this isothermal surface and the ideal temperature field distribution; the region with the largest error is the heating deviation location. The system uses the outer boundary point of each deviation location as a basis to determine the spatial boundary of the deviation region. This boundary is represented in three-dimensional coordinates, including the coordinates of the region's center point and the distance of the boundary extension.

[0112] To ensure accuracy, the system compares the boundary of the deviation region with a preset threshold. This threshold is determined through experimental calibration. Specifically, under continuous operation of the device, by comparing the reaction rate and temperature field uniformity, it was found that when the boundary size of the deviation region exceeds 10% of the total size of the reaction chamber, the hydrogen production rate decreases by more than 5%. Therefore, this 10% is used as the deviation boundary threshold standard. For example, when the reaction chamber length is 100 mm, if the deviation region length exceeds 10 mm, the system determines it as a serious heating deviation.

[0113] When the boundary of the deviation area exceeds a threshold, the system initiates a thermal flux simulation correction calculation. The correction calculation is performed by the thermal flux simulation tool, and the specific steps are as follows: In the simulation model, the system increases the thermal conductivity parameter of the deviation area by 5% to 10% to simulate the compensation effect of improving local thermal conductivity; simultaneously, it decreases the thermal conductivity parameter of the surrounding area by 2% to 5% to balance the overall heat flux direction. After the simulation runs, the system recalculates the temperature distribution matrix to determine whether the heat concentration area has disappeared and whether the temperature gradient has decreased. When the maximum temperature gradient value is less than 20% of the original value in two consecutive simulations, the heat distribution adjustment is considered complete, and the optimized heat distribution configuration data is output.

[0114] The thermal field simulation and path planning module, based on the determined heating deviation location, obtains dynamic temperature coordination requirement data, and uses the finite element method to calculate the thermal field distribution under coil and material matching, resulting in uniform heating path planning including:

[0115] Based on the location of the heating deviation, dynamic temperature coordination requirement data is obtained from a pre-established temperature coordination database. Data mining algorithms are used to extract temperature distribution features related to coil parameters and material matching to obtain the requirement data set.

[0116] If the temperature distribution characteristics in the required data set meet the preset heat conduction efficiency threshold, then the finite element simulation method is used to calculate the thermal field distribution by combining coil parameters and material matching data, and the thermal field distribution matrix is ​​obtained.

[0117] Based on the thermal field distribution matrix, the heating area is spatially discretized using a grid partitioning technique to extract key nodes for uniform heating and determine the initial planning of the uniform heating path.

[0118] The initial plan is optimized by a path planning algorithm. The coil parameters are adjusted by combining temperature distribution and heat conduction efficiency to obtain the uniform heating path plan.

[0119] In this embodiment, the working principle of the thermal field simulation and path planning module is to determine the uniform heating path inside the miniaturized hydrogen production and replenishment device through four stages: dynamic data matching, finite element thermal field calculation, spatial node extraction and path optimization, so as to ensure continuous heat distribution and smooth temperature gradient, thereby achieving efficient and stable hydrogen production reaction.

[0120] First, the system receives heating deviation location data output by the heat distribution identification module. This data includes the spatial coordinate range of the deviation area, the average temperature of the area, and the direction of the temperature gradient. Based on the coordinate information of the deviation location, the system retrieves the corresponding historical temperature characteristic data from the temperature coordination database. The temperature coordination database was established during the initial operation of the device through multiple sets of experiments and simulation results, and contains information such as different coil parameters, material configurations, temperature response, and heat transfer efficiency. Each record in the database includes parameters such as the number of coil turns, coil radius, coil length, type of magnetic material, heat transfer value, temperature distribution pattern, and energy transfer efficiency.

[0121] The system extracts the temperature coordination records most similar to the working interval corresponding to the deviation location from the database and uses data mining algorithms to analyze the correlation features in these records. The data mining process includes three steps: First, cluster analysis is performed on the temperature distribution data in the database to identify sample sets with the same temperature gradient trend; second, the mapping relationship between coil parameters and temperature distribution is extracted in each set; third, the heat transfer efficiency of each material configuration in a high-temperature environment is calculated. Through the above calculations, the system generates a dynamic temperature coordination requirement dataset, which includes temperature distribution characteristics, coil geometric parameters, and material matching parameter combinations that meet the current deviation location correction requirements.

[0122] Next, the system determines whether the temperature distribution characteristics in the required data set meet the preset thermal conductivity efficiency threshold. This threshold is determined through experimental calibration, specifically the average thermal conductivity efficiency value measured under multiple sets of coil parameter and material matching conditions. When the thermal conductivity efficiency is higher than 80%, heat transfer is uniform and temperature fluctuation is less than 5 degrees Celsius; therefore, 80% is set as the thermal conductivity efficiency threshold standard. If the calculated thermal conductivity efficiency is not lower than this standard, the system proceeds to the next stage of thermal field calculation; if it is lower than the threshold, the system backtracks to the database to reselect adjacent temperature feature samples until the standard is met.

[0123] In the thermal field calculation phase, the system employs the finite element method to establish a coupling model between the coil and the magnetic material. The model establishment steps are as follows: First, the geometric parameters of the induction coil (including the number of turns, radius, spacing, and length) and the configuration parameters of the magnetic material (including permeability and thermal conductivity) are input into the simulation system. Then, the entire device is divided into a three-dimensional spatial mesh, with the mesh size set according to the required calculation accuracy, typically between 1 and 2 millimeters. The system defines the current excitation source and the material's thermal conductivity properties at each mesh node, and calculates the temperature value and heat flow direction of each node by solving the energy transfer equation node by node. After the calculation is completed, the system outputs a thermal field distribution matrix containing temperature information for all nodes. This matrix is ​​a three-dimensional data structure, where each element represents the temperature value at a spatial location.

[0124] Subsequently, the system performs spatial discretization based on the thermal field distribution matrix. Discretization is accomplished using mesh generation technology, with the system traversing all nodes and calculating the temperature difference between adjacent nodes. If the temperature difference between adjacent nodes is less than a set uniformity threshold, these nodes are identified as key nodes in the uniform heating region. The uniformity threshold was determined experimentally; that is, when the temperature difference between nodes is less than 2 degrees Celsius, the fluctuation in the hydrogen generation rate in the reaction zone is less than 3%, therefore 2 degrees Celsius is set as the temperature uniformity threshold. The system extracts the spatial coordinates of all key nodes and forms an initial uniform heating path by connecting the nodes. This path reflects the main direction of heat transfer and the stable region distribution in the reaction zone.

[0125] Next, the system optimizes the initial uniform heating path using a path planning algorithm. The path planning algorithm employs a stepwise search strategy, with the shortest energy transfer path and the lowest temperature gradient change as optimization objectives. The optimization steps include: first, calculating the energy transfer efficiency between adjacent nodes in the current path; second, determining the temperature gradient change rate of each path segment; and third, if the temperature gradient of a local path exceeds a set equilibrium threshold, adjusting the local coil parameters to balance the heat flow. The local coil parameters include the turn spacing, the current frequency, and the coil position. The system automatically modifies these parameters based on simulation feedback. For example, when the temperature in a local area is 10 degrees Celsius below the target value, the system increases the current frequency in that area by 3% to 5% to increase the induction heating power; when the temperature in a local area is too high, the system increases the turn spacing by 1 mm to 2 mm to reduce the magnetic coupling strength.

[0126] The optimization process continues until the temperature gradient fluctuation value of the entire path is less than 5%, and the energy transfer efficiency reaches the threshold requirement. At this point, the system outputs the final uniform heating path planning data. This data includes path node coordinates, energy flow direction between nodes, coil parameter adjustments, and material thermal response characteristics, which can be directly used for real-time temperature regulation by the heating control module.

[0127] The heating control module extracts the path node temperature values ​​from the uniform heating path planning. If the node temperature value exceeds the threshold required for a high-temperature environment, iteratively adjusts the coil current frequency to determine the corrected heating control parameters, including:

[0128] Based on the pre-established path planning data, the temperature sensor data mapping method is used to obtain the temperature value of each path node for its coordinates, thus obtaining a node temperature dataset.

[0129] For the node temperature dataset, a threshold judgment logic is used. If the temperature value is higher than the preset high temperature environment threshold, the path node corresponding to the temperature value is selected to obtain the set of nodes that need to be adjusted.

[0130] For each node in the set of nodes that need adjustment, an iterative optimization algorithm is used to gradually change the coil current frequency, obtain the adjusted temperature value, and determine the set of frequency parameters that meet the requirements of high-temperature environment.

[0131] Based on the set of frequency parameters, a parameter mapping method is used to convert the set of frequency parameters into heating control parameters, resulting in a corrected control parameter dataset.

[0132] In this embodiment, the core working principle of the heating control module is to achieve automated heating regulation of the hydrogen production system through the dynamic mapping relationship between temperature monitoring data and coil current frequency, thereby maintaining the temperature of the heating area within the set high-temperature operating range and preventing local temperature exceedances or energy waste. The entire process includes four stages: temperature acquisition and node mapping, threshold judgment and node selection, frequency iterative optimization, and control parameter generation.

[0133] First, the system establishes a mapping relationship between node coordinates and temperature sensor data based on the uniform heating path planning data output by the previous module. Each node in the path planning data contains spatial coordinate information (i.e., the specific location of the node within the three-dimensional heating area). Temperature sensors are installed in the device structure according to the distribution of path nodes, with each sensor corresponding to one or more nodes. Each temperature sensor collects real-time temperature values ​​once per second, typically measuring from 0°C to 1200°C, with a measurement accuracy of no less than ±1°C. The system uses a data mapping algorithm to map the temperature sensor numbers one-to-one with the coordinates of the path nodes, recording the real-time temperature values ​​of each node in the node temperature dataset. The node temperature dataset is stored in tabular form, with each data entry containing the node number, node coordinates, current temperature value, and timestamp information.

[0134] Secondly, the system executes threshold judgment logic on the node temperature dataset. The high-temperature environment requirement threshold is determined based on the thermal stability and reaction efficiency of the materials under experimental conditions. For example, when the permeability of magnetic materials decreases rapidly above 900 degrees Celsius, and the hydrogen production reaction is most efficient in the range of 750 to 850 degrees Celsius, then 850 degrees Celsius is set as the high-temperature environment threshold. The system sequentially reads the temperature value of each node. If the temperature value of a node is higher than the threshold, it is determined to be an overheated node and added to the set of nodes requiring adjustment. The system also records the coordinates of the node, the corresponding coil segment number, and the current coil current frequency value. If the node temperature value is lower than the threshold, the original control parameters remain unchanged. Through this filtering process, the system obtains a set containing all overheated nodes for subsequent frequency adjustment.

[0135] Next, the system performs frequency iterative optimization calculations for each node in the set of nodes requiring adjustment. This optimization process is based on a response model where "frequency changes affect temperature," employing a combination of step-by-step adjustment and feedback judgment. First, the system uses the current coil current frequency as the initial frequency input, typically within the range of 50 kHz to 200 kHz. The system gradually decreases the frequency in 1 kHz steps, waiting one second after each adjustment to collect the node's temperature feedback value. The system calculates the temperature difference before and after the adjustment and determines whether it is close to the target temperature. The target temperature is the high-temperature environmental threshold minus 5 degrees Celsius, set at 845 degrees Celsius, to ensure temperature fluctuations are controlled within a safe range. When the node temperature fluctuates by less than 1 degree Celsius in two consecutive measurements and the temperature value is lower than the target temperature, the system determines that this frequency is the optimal frequency and records it as the corrected frequency parameter for that node.

[0136] If the node temperature remains higher than the target temperature after 10 consecutive adjustments, the system automatically reduces the step size to 0.5 kHz and continues iterative calculations until the condition is met. This process ensures a smooth frequency adjustment and avoids overcompensation due to excessively large step sizes. For multiple nodes requiring adjustment, the system uses parallel computing to perform optimizations separately, reducing overall computation time. After all optimizations are completed, the system generates a set of frequency parameters containing the optimal frequency value for each node.

[0137] Finally, the system generates a corrected heating control parameter dataset based on the frequency parameter set. The generation process employs a parameter mapping method, mapping the frequency value of each node to control signal parameters. The parameter mapping rules include the frequency-current power correspondence and the frequency-magnetic field strength correction relationship. Specifically, when the frequency decreases, the system automatically increases the coil current amplitude to ensure a constant total energy input to the heating area; when the frequency increases, the current power is correspondingly reduced to prevent excessive magnetic field concentration. The system records the frequency value, current amplitude, heating power correction coefficient, and time control sequence for each node in the control parameter dataset. The control module dynamically adjusts the coil drive signal based on this dataset with a millisecond-level response speed to achieve real-time heating control.

[0138] The energy optimization module, through the revised heating control parameters and integrating hydrogen mixing effect simulation data, uses a genetic algorithm to optimize reaction efficiency indicators, resulting in a final energy utilization efficiency improvement model including:

[0139] An initial dataset of hydrogen mixing effect is obtained from pre-established simulation data. The heating control parameters and hydrogen mixing ratio in the initial dataset are normalized using a data integration method to obtain an integrated mixing effect dataset.

[0140] Based on the integrated hybrid effect dataset, a genetic algorithm is used to iteratively optimize the reaction efficiency index, determine whether the reaction efficiency index has reached a preset threshold, and obtain an optimized set of control parameters.

[0141] If the optimized set of control parameters meets the preset threshold, the heating control parameters are adjusted by the parameter correction method to obtain the corrected heating control parameters and the updated reaction efficiency index.

[0142] Based on the updated reaction efficiency index, an energy utilization efficiency calculation method is used, combined with the corrected heating control parameters and the hydrogen mixing effect data, to obtain the final energy utilization efficiency improvement result.

[0143] In this embodiment, the energy optimization module works by performing multidimensional correlation analysis on the corrected heating control parameters and hydrogen mixing characteristic data, and using a genetic algorithm for iterative search and multi-round optimization calculations to obtain the optimal energy utilization efficiency model of the system under hydrogen production reaction conditions. The entire process includes five stages: data acquisition and integration, parameter normalization processing, genetic algorithm optimization calculation, parameter correction, and energy efficiency calculation.

[0144] First, the system extracts an initial dataset from a pre-established database simulating hydrogen mixing effects. This database was built through multiple simulation experiments during the device design and testing phase, containing information such as hydrogen mixing ratios, reaction rates, hydrogen concentration distribution, and energy consumption data under different heating conditions. Each record in the initial dataset contains three types of key data: first, heating control parameters, including coil current frequency, power density, heating time, and thermal uniformity; second, the hydrogen mixing ratio, i.e., the volume ratio of different gases in the reaction chamber; and third, the corresponding reaction efficiency value. The initial definition of reaction efficiency is the ratio of the volume of hydrogen produced per unit time to the system input energy, expressed in liters per kilojoule (L / kJ). The system selects no fewer than one thousand data points from the database as initial samples to ensure the representativeness and accuracy of the optimization.

[0145] Secondly, the system performs data integration and normalization on the initial dataset. The purpose of the data integration process is to eliminate the dimensional differences between different physical quantities, enabling the heating control parameters and hydrogen mixing ratio to be compared and optimized under the same computational dimension. The specific steps are as follows: First, the heating control parameters (including frequency, power, and heating time) are converted to a range of 0 to 1 through a minimum-maximum linear mapping; second, the hydrogen mixing ratio is normalized according to the total gas volume, so that the sum of the proportions of each gas component equals 1; third, the system normalizes the reaction efficiency values, converting them into relative efficiency values ​​based on the maximum experimental efficiency. After the above processing, the system generates an integrated mixing effect dataset, where the feature values ​​of each record are within the same numerical range, facilitating subsequent genetic algorithm calculations.

[0146] Next, the system initiates a genetic algorithm to iteratively optimize the reaction efficiency index. The optimization goal of the genetic algorithm is to maximize the reaction efficiency index. The specific steps are as follows: First, the system randomly selects several sets of data from the integrated dataset as the initial population. Each set of data contains a combination of heating control parameters and hydrogen mixing ratio. Then, the system calculates the reaction efficiency score for each set of data. The scoring process uses energy utilization efficiency as the main evaluation index. When the efficiency is higher than the average efficiency of the previous generation, the combination is assigned a higher fitness score. Next, the system selects samples based on the fitness scores, choosing the best-performing sets as parent samples. Afterward, the system performs crossover and mutation operations between the parent samples. The crossover process swaps some parameters between the two combinations, and the mutation process randomly adjusts the frequency or mixing ratio value by a small margin. Through crossover and mutation, the system generates new offspring samples. After each generation, the system recalculates the reaction efficiency of all samples and determines whether a preset threshold has been reached. This threshold is determined experimentally; that is, when the reaction efficiency increases by at least 15% compared to the initial state, the energy utilization rate of the system tends to stabilize. Therefore, a 15% efficiency increase is used as the optimization threshold standard. If the reaction efficiency improvement reaches or exceeds the threshold, the optimization stops; otherwise, iteration continues. The entire process typically runs for 50 to 100 generations until the algorithm converges.

[0147] After the algorithm outputs the optimized set of control parameters, the system further refines these parameters. The purpose of this refinement process is to make the optimization results more closely match the actual operating conditions of the device. The refinement steps are as follows: the system inputs the optimized frequency, power, and heating time parameters into the real-time monitoring model of the heating control module, and gradually refines the parameter values ​​based on the current temperature field distribution and power output capability of the equipment. For example, if the optimal frequency output by the algorithm is 200 kHz higher than the maximum frequency allowed by the device's drive circuit, the system automatically limits it to the maximum value range; if the calculated optimal heating time exceeds the reaction time required, the corresponding optimal interval is selected according to the hydrogen production rate curve. After the refinement is completed, the system recalculates the reaction efficiency index to verify the effectiveness of the refined parameters under real operating conditions.

[0148] Finally, the system calculates the final energy utilization efficiency improvement result based on the updated reaction efficiency index. The energy utilization efficiency is calculated as follows: the system measures the total energy consumed and the volume of hydrogen produced by the hydrogen production unit per unit time, and uses the ratio of hydrogen production energy to input energy as the energy utilization efficiency value. During the calculation process, sensors measure the electrical input power and gas flow rate in real time, and the system automatically summarizes the data and calculates the average energy utilization rate. The optimized energy utilization efficiency value is compared with the initial efficiency value; the difference is the improvement margin. If the improvement margin exceeds the set standard value by 10%, the system determines that the energy optimization is successful and stores the corresponding control parameters and hydrogen mixing ratio as the final energy utilization efficiency improvement model. This model not only includes the optimized parameter set and efficiency improvement results but also the system's energy response characteristics under different temperature, power, and mixing ratio conditions, providing a real-time energy optimization basis for the subsequent feedback coordination module.

[0149] The system feedback coordination module, based on the final energy utilization efficiency improvement model, obtains real-time feedback data from the hydrogen replenishment system. If the feedback data shows a deviation in the mixing effect, it retrospectively updates the selection of magnetic materials and determines the overall system coordination, including:

[0150] Real-time feedback data is obtained from the hydrogen replenishment system, and the mixing effect related parameters are extracted through the data acquisition frequency control module. If the mixing effect related parameters do not reach the preset threshold, the mixing effect deviation data is determined.

[0151] Based on the mixing effect deviation data, the real-time feedback data is processed by a real-time data analysis module to extract key features affecting the mixing effect and determine the magnetic material performance parameters that need to be traced back.

[0152] For the performance parameters of the magnetic material, a pre-established material database is used to query magnetic materials that meet the performance requirements, and an optimized magnetic material selection scheme is obtained.

[0153] The optimized magnetic material selection scheme is integrated with the real-time feedback data through the system optimization and adjustment module. If the integrated data meets the system coordination requirements, the adjusted system operating parameters are obtained.

[0154] In this embodiment, the core working principle of the system feedback coordination module is to use an energy utilization efficiency improvement model as a benchmark. Through dynamic analysis of real-time operating data of the hydrogen replenishment system, it identifies the sources of deviation in hydrogen mixing effect and automatically adjusts the selection scheme of magnetic materials, thereby achieving coordinated operation of the entire hydrogen production and replenishment device in three dimensions: energy, temperature, and reaction efficiency. The entire process includes five stages: real-time data acquisition, deviation detection, feature analysis, material backtracking optimization, and system coordination verification.

[0155] First, the system acquires operational feedback data through the real-time monitoring port of the hydrogen replenishment system. Real-time data includes parameters such as hydrogen concentration, gas flow rate, temperature distribution, energy consumption rate, and system pressure. The data acquisition frequency control module performs acquisition tasks at a fixed frequency, typically set to once per second, to ensure data continuity and real-time performance. The system arranges the acquired data in chronological order, forming a real-time feedback data stream.

[0156] The system calculates mixing effect parameters based on the feedback data stream. These parameters characterize the uniformity of hydrogen distribution within the reaction zone and are typically measured by concentration fluctuation rate. The concentration fluctuation rate is determined as follows: the system simultaneously measures hydrogen concentration at multiple points within the reaction chamber, averaging the concentration data from each point over the same time window, and calculating the ratio of the standard deviation to the average concentration. If this ratio is less than 5%, it indicates uniform hydrogen distribution; if it exceeds 5%, it indicates a mixing effect deviation. The system uses 5% as a preset threshold for the mixing effect, determined through experimental calibration. Specifically, when hydrogen concentration fluctuation exceeds this value, the hydrogen production reaction rate decreases by more than 10%, thus this value is used as the judgment standard. If the system detects that the mixing effect parameters have not reached this threshold (i.e., the concentration fluctuation rate is higher than 5%), it generates mixing effect deviation data and proceeds to the next step of processing.

[0157] Subsequently, the system inputs the mixing effect deviation data into the real-time data analysis module for feature extraction. This module's task is to identify the key factors causing the abnormal mixing effect. The analysis process includes three steps: First, the system performs time-series decomposition on the real-time feedback data, distinguishing between steady-state and disturbance intervals; second, it performs correlation analysis on the temperature, flow rate, and energy consumption data within the disturbance interval, calculating the correlation between each parameter and concentration fluctuations; third, based on the correlation degree, it selects the parameter combinations most likely to cause deviations. Typically, when the permeability of magnetic materials decreases or their thermal conductivity weakens at high temperatures, it causes uneven electromagnetic heating, indirectly leading to insufficient hydrogen mixing. The system extracts these influencing factors into key characteristic parameters, including the rate of change of high-temperature permeability, the magnitude of the decrease in thermal conductivity, and specific heat capacity of the magnetic materials. The system defines these characteristic parameters as the performance parameters of the magnetic materials to be traced back.

[0158] After determining the performance parameters of the magnetic materials to be traced, the system initiates a material database search. The material database contains high-temperature performance data for various magnetic materials, such as iron-based alloys, nickel-based alloys, cobalt-based composites, and their permeability, thermal conductivity, and thermal stability parameters under different compositional ratios. The system compares the performance parameters to be traced with the material data recorded in the database item by item, calculating the performance deviation value for each material under high-temperature conditions. If the deviation value is less than a set tolerance range (tolerance range is 5%), the material is considered a qualified candidate material. The system selects the material with the closest permeability to the target value and high thermal conductivity from the candidate list, forming an optimized magnetic material selection scheme. This scheme typically includes information such as material type, composition ratio, temperature stability range, and manufacturing batch number.

[0159] The system then performs coordination verification of materials and operating parameters through the system optimization and adjustment module. The verification process includes the following steps: First, the optimized magnetic material selection scheme is input into the simulation model and integrated with the real-time feedback data of the current system to establish a new electromagnetic thermal field model; then, the new energy distribution and temperature field characteristics are calculated based on the model to determine the overall coordination of the system. The coordination judgment criteria include three items: first, the energy utilization rate improvement is not less than 10%; second, the hydrogen concentration fluctuation rate is less than 5%; and third, the temperature distribution difference in the reaction zone is less than 10 degrees Celsius. The system compares the calculation results item by item. If all conditions are met, the system is determined to have reached a coordinated operating state, and the adjusted system operating parameters are output. If any condition is not met, the system automatically adjusts the proportion of magnetic materials or the heating power allocation, and re-executes the simulation calculation until the conditions are met.

[0160] The deployment configuration module extracts coordination indicators from the assessed overall system coordination. For miniaturized device deployment scenarios, it generates deployment configurations through data fusion methods, resulting in a stable operation plan for the hydrogen production process, including:

[0161] Operating parameters are obtained from sensor data of miniaturized devices, and the temperature, pressure, and flow data are weighted and averaged using data fusion methods to obtain overall coordination indicators.

[0162] If the overall coordination index is lower than a preset threshold, the device operating parameters are adjusted through a parameter optimization algorithm to generate a first deployment configuration;

[0163] Based on the first deployment configuration and combined with environmental adaptability data, the hydrogen production process is checked for data consistency through a real-time monitoring module to obtain the second deployment configuration.

[0164] Using the second deployment configuration, combined with a pre-established hydrogen production stability model, the operating status is evaluated through logical judgment methods to determine the final stable hydrogen production operation scheme.

[0165] In this embodiment, the deployment configuration module works by using multi-source sensor data fusion and dynamic parameter adjustment algorithms to quantify and optimize the overall operational coordination of the integrated hydrogen production and replenishment system in real time. This results in a miniaturized device deployment scheme suitable for different environmental conditions, ensuring long-term temperature stability, gas flow balance, and optimal energy utilization efficiency in the hydrogen production process. The entire process includes five stages: data acquisition and fusion, coordination assessment, parameter optimization configuration, environmental adaptability verification, and stability assessment.

[0166] First, the system acquires real-time operating data from sensors installed in key parts of the miniaturized device. These sensors include temperature sensors, pressure sensors, and flow sensors. Temperature sensors are distributed within the heating chamber, hydrogen production reaction chamber, and gas outlet channel to collect temperature values ​​at different locations; pressure sensors are located at the gas inlet and outlet to measure the internal operating pressure of the system; and flow sensors are positioned on the hydrogen output pipeline to monitor the hydrogen production rate. The system synchronously collects all types of data at a sampling frequency of once per second and records them as a time series.

[0167] Next, the system performs data fusion calculations to obtain an overall coordination index. The data fusion process uses a weighted average method, combining temperature, pressure, and flow rate data according to their respective weights in relation to system stability. The weights are set as follows: temperature weight is 0.5, pressure weight is 0.3, and flow rate weight is 0.2. This weight allocation is determined based on system sensitivity experiments; that is, when the temperature deviation exceeds 5% of the target temperature, system stability decreases significantly; while pressure and flow rate changes within 10% have a smaller impact on stability. During calculation, the system first normalizes the data from each sensor, converting different physical quantities into the same numerical range (between 0 and 1). Then, each data type is multiplied by its corresponding weight and summed to obtain an overall coordination index between 0 and 1. The closer the coordination index value is to 1, the more stable the system operation.

[0168] Subsequently, the system determines whether the overall coordination index is below a preset threshold. This threshold is determined through long-term operational experiments and is generally set to 0.85. When the index value is below 0.85, it indicates that the system has problems such as temperature fluctuations, uneven pressure, or flow deviations. At this time, the system calls a parameter optimization algorithm to adjust the operating parameters. The optimization algorithm adopts a step-by-step incremental strategy, adjusting the heating power, gas inlet pressure, and reaction time sequentially. The adjustment range for each adjustment is set according to the degree of deviation: when the temperature is too low, the heating power is increased by 2%; when the pressure is too low, the inlet pressure is increased by 5%; when the flow rate is too high, the reaction time is shortened by 3%. After each adjustment, the system recalculates the coordination index and determines whether it has improved. When the index value increases by no less than 5% after three consecutive adjustments, the system determines that the optimization is effective and generates the first deployment configuration.

[0169] After generating the first deployment configuration, the system verifies this configuration using environmental adaptability data. This data includes parameters such as ambient temperature, humidity, atmospheric pressure, and power stability, collected by external sensors. The system invokes the real-time monitoring module to verify the consistency between the hydrogen production process's operational data and the environmental data. The verification method involves comparing the trends of temperature, pressure, and flow rate curves at different time points. If the curves change in the same direction and the difference is within the allowable range (usually less than 10%), the data is considered consistent. If the deviation exceeds the range, it indicates that the configuration is not adapted to the current environment, and the system automatically corrects the heating time or power allocation parameters and generates a second deployment configuration.

[0170] Finally, the system evaluates its operational status based on the second deployment configuration and a pre-established hydrogen production stability model. The hydrogen production stability model is a multi-condition logic model built from experimental and historical operational data, defining the system's stable state under different parameter combinations. The model's judgment logic includes three criteria: first, temperature fluctuations do not exceed ±3% of the target temperature; second, pressure change rate does not exceed 5%; and third, hydrogen output flow rate remains within ±5% of the target value. The system inputs the various operational parameters from the second deployment configuration into the model for judgment. If all three criteria are met simultaneously, the system is considered to be operating stably, and this configuration is determined as the final stable hydrogen production operation scheme.

[0171] This scheme includes the finalized heating power, inlet pressure, reaction time, and corresponding environmental adaptability parameters, which can be directly used for deployment in different scenarios, such as vehicle-mounted hydrogen production units, portable energy devices, or fixed experimental systems. During deployment, the system can automatically load the corresponding configuration according to this scheme, achieving plug-and-play stable control of the hydrogen production process.

[0172] A method for rapid field refueling of a miniaturized hydrogen production and refueling drone is also provided, employing the aforementioned miniaturized integrated hydrogen production and refueling device. The method includes:

[0173] The parameters of the induction coil and the initial property data of the magnetic material in the UAV's field power replenishment device were collected. The geometric parameters of the coil were optimized by a genetic algorithm to obtain the enhanced electromagnetic field intensity distribution model.

[0174] Based on the enhanced electromagnetic field intensity distribution model, the magnetic permeability and thermal conductivity data of the magnetic material under high temperature conditions are obtained. If the magnetic permeability is lower than the preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme.

[0175] Thermal conductivity indices were extracted from the optimized magnetic material configuration scheme. Based on the temperature fluctuation data during the hydrogen production reaction, support vector machines were used to classify areas with uneven heat distribution and identify potential heating deviation locations.

[0176] Based on the determined heating deviation location, obtain dynamic temperature coordination requirement data, use the finite element simulation method to calculate the thermal field distribution under coil and material matching, and obtain uniform heating path planning;

[0177] The path node temperature values ​​are extracted from the uniform heating path planning. If the node temperature value exceeds the threshold required for high temperature environment, the coil current frequency is iteratively adjusted to determine the corrected heating control parameters.

[0178] By integrating hydrogen mixing effect simulation data with the corrected heating control parameters, and using a genetic algorithm to optimize the reaction efficiency index, the final energy utilization efficiency improvement model is obtained.

[0179] Based on the final energy utilization efficiency improvement model, real-time feedback data of the hydrogen replenishment system is obtained. If the feedback data shows a deviation in the mixing effect, the selection of magnetic materials is updated retrospectively to determine the overall system coordination.

[0180] By extracting coordination indicators from the overall system coordination assessment, and using data fusion methods to generate deployment configurations for the deployment scenario of miniaturized UAV field refueling devices, a stable operation scheme for the hydrogen production and refueling process is obtained.

[0181] The miniaturized hydrogen production and replenishment UAV's rapid field refueling method of this embodiment is executed continuously through eight stages: "coil electromagnetic optimization, material thermal matching, thermal field identification, path correction, power regulation, energy optimization, system backtracking, and deployment integration," to achieve rapid, safe, and highly energy-efficient operation of hydrogen production and replenishment in the UAV's field environment.

[0182] First, in the UAV's field power replenishment device, the system activates the parameter acquisition unit to measure the geometric parameters of the induction coil (including radius, length, number of turns, and wire spacing) and the initial properties of the magnetic material (including permeability, thermal conductivity, and saturation magnetic flux density). The system acquires this data through an embedded sampling module and inputs it into the calculation unit. A genetic algorithm is used for global optimization of the coil's geometric parameters. The optimization objective is to improve the uniformity of electromagnetic field strength and magnetic flux density. The system first generates a set of parameter samples, performs finite element simulation of the electromagnetic field for each set of parameters, calculates the magnetic field strength distribution, and sorts them according to the magnetic field uniformity index. The algorithm iteratively optimizes through crossover, mutation, and selection operations, ultimately obtaining the parameter set with the most uniform magnetic field strength distribution, forming an enhanced electromagnetic field strength distribution model.

[0183] Secondly, the system calculates the permeability and thermal conductivity of the magnetic material under high-temperature conditions based on the model. The high-temperature experimental temperature range is 600°C to 900°C. If the permeability is lower than 80% of the room temperature value, the system initiates a material composition adjustment procedure. Through iterative optimization algorithms and Monte Carlo sampling methods, multiple simulations are performed on the proportions of major components such as iron, nickel, and cobalt to calculate the changes in permeability and thermal conductivity under each combination. After thermodynamic simulation, the system selects a material scheme with satisfactory permeability and high thermal conductivity to form an optimized magnetic material configuration.

[0184] Third, the system extracts thermal conductivity indicators from the optimized material configuration and combines them with real-time temperature sensing data to identify temperature fluctuation characteristics in the hydrogen production reaction process. The system uses a support vector machine classification algorithm to analyze the temperature distribution matrix, identifying areas with temperature fluctuation rates greater than 5% as areas of uneven heat distribution and determining potential heating deviation locations.

[0185] Fourthly, for the aforementioned deviation areas, the system retrieves the corresponding dynamic temperature coordination requirement data from the temperature coordination database. The system employs the finite element method (FEM) to reconstruct the thermal field distribution under coil-material matching in three-dimensional space. In the simulation, the system divides the space into 1-2 mm spatial mesh nodes, solves for the heat flux density and temperature gradient at each node, and generates a thermal field distribution matrix. Through matrix analysis, the system identifies the path nodes with the most uniform heat distribution, forming an initial uniform heating path. Then, the system uses a path planning algorithm to calculate the shortest energy transfer path and adjusts the local coil parameters according to the heat conduction efficiency, thereby obtaining a uniform heating path plan.

[0186] The fifth step involves the system extracting the temperature values ​​of the path nodes from the path planning. If the temperature of a node exceeds a high-temperature threshold (e.g., 850 degrees Celsius), the system automatically initiates an iterative optimization process, gradually reducing the coil current frequency. The initial frequency adjustment step size is set to 1 kHz, and the temperature is fed back to the control module after each update. When the node temperature decreases and stabilizes within the target temperature range (threshold minus 5 degrees Celsius), the system records the current frequency value as the corrected control parameter. After all nodes have been calculated, the system generates a set of heating control parameters, forming a real-time control signal used to dynamically adjust the coil heating power.

[0187] In the sixth step, after receiving the corrected heating control parameters, the energy optimization module integrates them with the hydrogen mixing effect simulation data. After normalizing the data, the system uses a genetic algorithm to optimize the reaction efficiency index. The algorithm uses maximizing energy utilization as the objective function and iteratively adjusts the heating time, power density, and hydrogen mixing ratio. When the reaction efficiency improves by more than 15% compared to the initial state, the system outputs the final energy utilization efficiency improvement model.

[0188] The seventh step involves the system feedback coordination module monitoring the hydrogen replenishment system's operational status in real time based on this model. The system collects hydrogen concentration, flow rate, and temperature distribution data from sensors and analyzes concentration fluctuations to determine if there are any deviations in the mixing effect. When a deviation is detected, the system backtracks to the magnetic material performance database, selects material schemes with better matching magnetic permeability and thermal conductivity, and updates the device's material configuration, thereby restoring overall system coordination. Coordination is judged by three indicators: energy utilization rate, concentration uniformity, and temperature fluctuation amplitude. When all three indicators simultaneously meet the criteria, the system is considered to be operating in a coordinated manner.

[0189] Step 8: The deployment and configuration module generates the final field operation plan based on the system coordination results. The system integrates UAV environmental data (including ambient temperature, air pressure, wind speed, and humidity), performs weighted averaging on temperature, pressure, and flow sensor data, and calculates the overall coordination index. When the coordination index is higher than 0.85, the system generates a deployment and configuration plan; if it is lower than this value, the system automatically adjusts the reaction time or power allocation and recalculates. The system finally loads the optimized parameters into the device control unit, forming a stable operation plan for UAV field hydrogen production and replenishment, achieving rapid energy replenishment.

[0190] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A miniaturized integrated hydrogen production and replenishment device, characterized in that, include: The parameter acquisition and optimization module collects the parameters of the induction coil and the initial property data of the magnetic material in the miniaturized device, and uses a genetic algorithm to optimize the geometric parameters of the coil to obtain an enhanced electromagnetic field intensity distribution model. The magnetic material configuration module obtains the magnetic permeability and thermal conductivity data of the magnetic material under the condition that the temperature is higher than the preset threshold, based on the enhanced electromagnetic field intensity distribution model. If the magnetic permeability is lower than the preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme. The heat distribution identification module extracts thermal conductivity indicators from the optimized magnetic material configuration scheme. Based on the temperature fluctuation data in the hydrogen production process, it uses a support vector machine to classify areas with uneven heat distribution and determine the location of potential heating deviations. The thermal field simulation and path planning module obtains dynamic temperature coordination requirement data based on the determined heating deviation position, and uses the finite element simulation method to calculate the thermal field distribution under coil and material matching to obtain uniform heating path planning. The heating control module extracts the path node temperature values ​​from the uniform heating path planning. If the node temperature value exceeds the threshold required for high-temperature environment, it iteratively adjusts the coil current frequency to determine the corrected heating control parameters. The energy optimization module integrates hydrogen mixing effect simulation data with the corrected heating control parameters and uses a genetic algorithm to optimize the reaction efficiency index, resulting in the final energy utilization efficiency improvement model.

2. The miniaturized integrated hydrogen production and replenishment device according to claim 1, characterized in that: The parameter acquisition and optimization module acquires induction coil parameters and initial property data of magnetic materials in the miniaturized device, and uses a genetic algorithm to optimize the coil geometric parameters to obtain an enhanced electromagnetic field intensity distribution model, including: The initial geometric parameters of the induction coil and the initial property data of the magnetic material in the miniaturized device are obtained. The initial electromagnetic field intensity distribution is calculated using the finite element simulation tool to obtain the first electromagnetic field distribution data. Based on the first electromagnetic field distribution data, a genetic algorithm is used to optimize the number of coil turns, coil radius, and coil length of the induction coil to obtain an optimized second set of geometric parameters; If the magnetic field uniformity of the second geometric parameter set is greater than the preset threshold, the electromagnetic field intensity distribution is recalculated based on the second geometric parameter set and the initial properties of the magnetic material using a finite element simulation tool to obtain the second electromagnetic field distribution data. By comparing the magnetic field uniformity and intensity values ​​of the second electromagnetic field distribution data with those of the first electromagnetic field distribution data, it is determined whether the second electromagnetic field distribution data meets the objective function value requirements, thus obtaining the enhanced electromagnetic field intensity distribution model.

3. The miniaturized integrated hydrogen production and replenishment device according to claim 1, characterized in that: The magnetic material configuration module acquires the magnetic permeability and thermal conductivity data of the magnetic material under high-temperature conditions based on the enhanced electromagnetic field intensity distribution model. If the magnetic permeability is lower than a preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme, including: The magnetic permeability and thermal conductivity data of the magnetic material are obtained from experimental data under conditions where the temperature is higher than a preset threshold. The magnetic field intensity distribution is calculated using finite element simulation software to obtain the first magnetic permeability and first thermal conductivity data. If the first permeability is lower than a preset threshold, the composition ratio of the magnetic material is adjusted by an iterative optimization algorithm, and the composition ratio is randomly sampled by the Monte Carlo method to determine the first composition ratio scheme. Based on the first component ratio scheme, the material properties under high temperature conditions were recalculated using thermodynamic simulation software to obtain the second magnetic permeability and second thermal conductivity data; By comparing the second magnetic permeability with a preset threshold, it is determined whether the requirements are met. If they are met, the first component ratio scheme is stored in the database to obtain the final material configuration data.

4. The miniaturized integrated hydrogen production and replenishment device according to claim 1, characterized in that: The heat distribution identification module extracts thermal conductivity indicators from the optimized magnetic material configuration scheme. Based on temperature fluctuation data during the hydrogen production reaction, it uses a support vector machine to classify areas of uneven heat distribution and determine potential heating deviation locations, including: Thermal conductivity data were obtained from the optimized magnetic material configuration scheme, and the temperature fluctuation data in the hydrogen production process was meshed using finite element analysis tools to obtain the temperature distribution matrix. Based on the temperature distribution matrix, a support vector machine classification algorithm is used to divide the uneven heat distribution regions and determine the set of uneven heat distribution regions. Spatial coordinate data are extracted from the set of uneven heat regions, and a three-dimensional interpolation algorithm is used to finely locate the heating deviation position to determine the boundary of the deviation region. If the boundary of the deviation region exceeds a preset threshold, the thermal conductivity of the deviation region is adjusted using a thermal flow simulation tool to obtain an optimized heat distribution configuration.

5. A miniaturized integrated hydrogen production and replenishment device according to claim 1, characterized in that: The thermal field simulation and path planning module, based on the determined heating deviation location, obtains dynamic temperature coordination requirement data, and uses the finite element simulation method to calculate the thermal field distribution under coil and material matching, resulting in uniform heating path planning including: Based on the location of the heating deviation, dynamic temperature coordination requirement data is obtained from a pre-established temperature coordination database. Data mining algorithms are used to extract temperature distribution features related to coil parameters and material matching to obtain the requirement data set. If the temperature distribution characteristics in the required data set meet the preset heat conduction efficiency threshold, then the finite element simulation method is used to calculate the thermal field distribution by combining coil parameters and material matching data, and the thermal field distribution matrix is ​​obtained. Based on the thermal field distribution matrix, the heating area is spatially discretized using a grid partitioning technique to extract key nodes for uniform heating and determine the initial planning of the uniform heating path. The initial plan is optimized by a path planning algorithm. The coil parameters are adjusted by combining temperature distribution and heat conduction efficiency to obtain the uniform heating path plan.

6. The miniaturized integrated hydrogen production and replenishment device according to claim 1, characterized in that: The heating control module extracts the path node temperature values ​​from the uniform heating path planning. If the node temperature value exceeds the high-temperature environment requirement threshold, it iteratively adjusts the coil current frequency to determine the corrected heating control parameters, including: Based on the pre-established path planning data, the temperature value of each path node is obtained by using the temperature sensor data mapping method, and a node temperature dataset is obtained. For the node temperature dataset, a threshold judgment logic is used. If the temperature value is higher than the preset high temperature environment threshold, the path node corresponding to the temperature value is selected to obtain the set of nodes that need to be adjusted. For each node in the set of nodes that need adjustment, an iterative optimization algorithm is used to gradually change the coil current frequency, obtain the adjusted temperature value, and determine the set of frequency parameters that meet the requirements of high-temperature environment. Based on the set of frequency parameters, a parameter mapping method is used to convert the set of frequency parameters into heating control parameters, resulting in a corrected control parameter dataset.

7. A miniaturized integrated hydrogen production and replenishment device according to claim 1, characterized in that: The energy optimization module, through the corrected heating control parameters, integrates hydrogen mixing effect simulation data and uses a genetic algorithm to optimize the reaction efficiency index, resulting in a final energy utilization efficiency improvement model including: An initial dataset of hydrogen mixing effect is obtained from pre-established simulation data. The heating control parameters and hydrogen mixing ratio in the initial dataset are normalized using a data integration method to obtain an integrated mixing effect dataset. Based on the integrated hybrid effect dataset, a genetic algorithm is used to iteratively optimize the reaction efficiency index, determine whether the reaction efficiency index has reached a preset threshold, and obtain an optimized set of control parameters. If the optimized set of control parameters meets the preset threshold, the heating control parameters are adjusted by the parameter correction method to obtain the corrected heating control parameters and the updated reaction efficiency index. Based on the updated reaction efficiency index, an energy utilization efficiency calculation method is used, combined with the corrected heating control parameters and the hydrogen mixing effect data, to obtain the final energy utilization efficiency improvement result.

8. A miniaturized integrated hydrogen production and replenishment device according to claim 1, characterized in that, It also includes a system feedback coordination module, which acquires real-time feedback data from the hydrogen replenishment system based on the final energy utilization efficiency improvement model. If the feedback data shows a deviation in the mixing effect, it backtracks and updates the selection of magnetic materials to determine the overall system coordination. Specifically, this includes: Real-time feedback data is obtained from the hydrogen replenishment system, and the mixing effect related parameters are extracted through the data acquisition frequency control module. If the mixing effect related parameters do not reach the preset threshold, the mixing effect deviation data is determined. Based on the mixing effect deviation data, the real-time feedback data is processed by a real-time data analysis module to extract key features affecting the mixing effect and determine the magnetic material performance parameters that need to be traced back. For the performance parameters of the magnetic material, a pre-established material database is used to query magnetic materials that meet the performance requirements, and an optimized magnetic material selection scheme is obtained. The optimized magnetic material selection scheme is integrated with the real-time feedback data through the system optimization and adjustment module. If the integrated data meets the system coordination requirements, the adjusted system operating parameters are obtained.

9. A miniaturized integrated hydrogen production and replenishment device according to claim 1, characterized in that, It also includes a deployment configuration module, which extracts coordination indicators from the assessed overall system coordination, and generates deployment configurations for miniaturized device deployment scenarios through data fusion methods to obtain a stable operation plan for the hydrogen production process, specifically including: Operating parameters are obtained from sensor data of miniaturized devices, and the temperature, pressure, and flow data are weighted and averaged using data fusion methods to obtain overall coordination indicators. If the overall coordination index is lower than a preset threshold, the device operating parameters are adjusted through a parameter optimization algorithm to generate a first deployment configuration; Based on the first deployment configuration and combined with environmental adaptability data, the hydrogen production process is checked for data consistency through a real-time monitoring module to obtain the second deployment configuration. Using the second deployment configuration, combined with a pre-established hydrogen production stability model, the operating status is evaluated through logical judgment methods to determine the final stable hydrogen production operation scheme.

10. A method for rapid field refueling of a miniaturized hydrogen production and refueling drone, employing the miniaturized integrated hydrogen production and refueling device as described in any one of claims 1-9, characterized in that, The method includes: The parameters of the induction coil and the initial property data of the magnetic material in the UAV's field power replenishment device were collected. The geometric parameters of the coil were optimized by a genetic algorithm to obtain the enhanced electromagnetic field intensity distribution model. Based on the enhanced electromagnetic field intensity distribution model, the magnetic permeability and thermal conductivity data of the magnetic material under high temperature conditions are obtained. If the magnetic permeability is lower than the preset threshold, the material composition ratio is adjusted to determine the optimized magnetic material configuration scheme. Thermal conductivity indices were extracted from the optimized magnetic material configuration scheme. Based on the temperature fluctuation data during the hydrogen production process, support vector machines were used to classify areas with uneven heat distribution and identify potential heating deviation locations. Based on the determined heating deviation location, obtain dynamic temperature coordination requirement data, use the finite element simulation method to calculate the thermal field distribution under coil and material matching, and obtain uniform heating path planning; The path node temperature values ​​are extracted from the uniform heating path planning. If the node temperature value exceeds the threshold required for high temperature environment, the coil current frequency is iteratively adjusted to determine the corrected heating control parameters. By integrating hydrogen mixing effect simulation data with the corrected heating control parameters, and using a genetic algorithm to optimize the reaction efficiency index, the final energy utilization efficiency improvement model is obtained. Based on the final energy utilization efficiency improvement model, real-time feedback data of the hydrogen replenishment system is obtained. If the feedback data shows a deviation in the mixing effect, the selection of magnetic materials is updated retrospectively to determine the overall system coordination. By extracting coordination indicators from the overall system coordination assessment, and using data fusion methods to generate deployment configurations for the deployment scenario of miniaturized UAV field refueling devices, a stable operation scheme for the hydrogen production and refueling process is obtained.

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