Water electrolysis system configuration optimization method and platform based on multi-source data collaborative calculation

Abstract

The application discloses a water electrolysis system configuration optimization method and platform based on multi-source data collaborative calculation, comprising the following steps: collecting multi-dimensional operation parameters of electrolytic cells, gas-liquid separators and other equipment through a cross-domain data collaborative processing system and transmitting in real time, calling a water electrolysis multi-field coupling simulation model to dynamically simulate an operation state, combining a guided matrix incremental updating algorithm to iteratively adjust configuration parameters, calculating equipment matching through a load adaptation model to determine a configuration feasible region, establishing a mapping relationship between parameters and the operation state through multi-source data cross verification, performing multi-round screening on schemes in the feasible region and dynamically adjusting equipment grouping, connection modes and linkage logic, and finally determining an optimized configuration scheme adapting to different scenes; the platform realizes a closed loop of data acquisition, simulation operation, parameter updating, scheme optimization and execution landing through orderly linkage of six functional units, and guarantees efficient and stable operation of the device under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of multi-source data processing and engineering optimization in water electrolysis technology, and in particular to a method and platform for optimizing the configuration of a water electrolysis system based on multi-source data collaborative computing, multi-physics simulation modeling and intelligent algorithms. Background Technology

[0002] Water electrolysis technology, as a core supporting means for clean energy conversion and storage, is increasingly widely used in fields such as hydrogen production and energy network peak shaving. The operating efficiency and configuration rationality of its devices directly determine the energy conversion efficiency, operational stability, and overall cost control effect. As water electrolysis systems develop towards larger scale, intelligence, and multi-condition adaptability, single-dimensional operating data can no longer meet the requirements for precise configuration. The collaborative utilization of multi-source cross-domain data and the accurate characterization of multi-physics coupling relationships have become key to improving the optimization level of device configuration.

[0003] In existing technologies, configuration optimization for water electrolysis systems largely relies on computer data processing to integrate multi-source data, using computer multi-field coupling simulation models to simulate the device's operating state, and adjusting configuration parameters in conjunction with preset algorithms. For example, some solutions collect basic data such as electrolyzer voltage, current, temperature, and electrolyte concentration, and use computer systems to perform simple statistical analysis to generate a configuration scheme suitable for a single operating condition; other solutions attempt to replicate the coupling effect of electric and temperature fields through computer simulation models, providing theoretical support for parameter adjustment.

[0004] However, the aforementioned existing technologies still have significant shortcomings: On the one hand, limited by the simple logic of computer data processing and the low accuracy of multi-source data fusion, it is difficult to achieve collaborative processing of cross-domain data (such as power grid load data, environmental temperature and humidity data, and historical operating data of the device), resulting in insufficient data support for configuration optimization; on the other hand, existing computer multi-field coupling simulation models mostly focus on a single physical field or a few field couplings, with incomplete simulation dimensions and rudimentary simulation iteration algorithms, failing to accurately capture the dynamic interaction patterns of multiple fields. This makes it difficult to adapt to complex and ever-changing operating conditions due to a lack of accurate computer simulation support for configuration parameter adjustments. Furthermore, some solutions still rely on manual experience to correct configuration parameters, using only simple data recording rather than in-depth analysis and processing by computer systems. This results in strong subjectivity and slow response speed in parameter adjustments, further reducing the stability of device operation and energy conversion efficiency.

[0005] Therefore, how to construct an efficient multi-source data collaborative processing and multi-field coupled simulation modeling scheme, and solve the shortcomings of the existing computer-aided water electrolysis system configuration optimization by optimizing the computer data processing flow and iterative algorithm logic, and achieve accurate and intelligent adaptation of the configuration scheme, has become an urgent technical problem to be solved in the current water electrolysis technology field. This is also the core research direction of this invention. Summary of the Invention

[0006] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and platform for optimizing the configuration of a water electrolysis system based on multi-source data collaborative calculation.

[0007] The technical solution adopted in this invention is a water electrolysis system configuration optimization method based on multi-source data collaborative calculation, comprising the following steps: S1 collects data on the electrolyzer's gas production, gas-liquid separator's processing capacity, gas purity detection value, alkali flow rate, and load fluctuation parameters during the operation of the water electrolysis system through a cross-domain data collaborative processing system, constructs a multi-dimensional data acquisition matrix, and transmits it in real time. S2, call the water electrolysis multi-field coupling simulation model to dynamically simulate the collected data, generate multi-field distribution characteristic data of the device operation status, and iteratively adjust the initial configuration parameter matrix by combining the water electrolysis guidance matrix incremental update algorithm; S3. Based on the load adaptation model of the electrolysis unit, a preliminary calculation is made on the ratio of the number of electrolyzers and separators under different operating conditions to determine the feasible range of configuration parameters. S4, cross-validates multi-source data through a cross-domain data collaborative processing system, extracts calibration feature parameters that affect configuration optimization, and establishes a mapping relationship between parameters and device operating status; S5 combines the incremental update algorithm of the water electrolysis guidance matrix to perform multiple rounds of screening of configuration schemes in the feasible domain, and dynamically adjusts the grouping method of electrolyzers, the connection branches of separators and the linkage logic of auxiliary equipment. S6. Based on the screening results, determine the final configuration quantity and connection method of electrolyzers, gas-liquid separators and auxiliary equipment to form an optimized configuration scheme adapted to different operating scenarios.

[0008] Furthermore, the expression for the multi-field coupled simulation model of water electrolysis in S2 is as follows: ,in: This is the matrix of multi-field coupled simulation results. Let be the weight coefficient for the i-th monitoring point. Let be the electric field gradient vector at the i-th monitoring point. Let be the gas density gradient vector at the i-th monitoring point. For multi-field coupling coefficients, Let i be the temperature field distribution value at the i-th monitoring point. For time variables, Let be the viscosity coefficient of the alkali solution at the i-th monitoring point. This represents the total number of monitoring points.

[0009] Furthermore, the expression for the incremental update algorithm of the water electrolysis guidance matrix in S2 is as follows: ,in: This is the incrementally updated guiding matrix. This is the initial guiding matrix. It is the identity matrix. To update the step size coefficient, For multi-source data incremental matrix, For the data weight matrix, The 2-norm of the data increment matrix. The projection matrix is ​​configured with parameters.

[0010] Furthermore, the expression for the load adaptation model of the electrolysis device in S3 is: ,in: For load adaptability factor, This represents the total gas production of the system. This is the load adjustment coefficient. Let be the influence coefficient of the j-th type of load fluctuation. Let j be the amplitude of the load fluctuation. Number of load fluctuation types This represents the total number of electrolytic cells. This refers to the rated gas production of a single electrolytic cell. The separator processing efficiency coefficient. This is the matching coefficient between the electrolytic cell and the separator.

[0011] Furthermore, the data cross-validation model expression for the cross-domain data collaborative processing system in S4 is as follows: ,in: The cross-validation result value. This represents the actual value of the I-th sampling point in the k-th data category. The predicted value for the i-th sampling point of the k-th class of data. The actual value weighting coefficient, These are the weighting coefficients for the predicted values. For the number of data categories, The number of sampling points for a single type of data. To verify the correction coefficient.

[0012] Furthermore, the expression for the multi-round filtering model in configuration scheme S5 is as follows: ,in The selected optimal configuration scheme. For the configuration scheme feasible domain, These are the weighting coefficients for purity deviation, yield deviation, and cost deviation, respectively. Let be the purity deviation value of scheme s. Let be the production deviation value of scheme s. Let be the cost deviation value of scheme s. Let be the optimization adaptation coefficient of scheme s.

[0013] Further, step S3 includes the following sub-steps: S31, extracting core parameters such as rated gas production, maximum purity fluctuation value, and maximum processing capacity of the gas-liquid separator under different load conditions through a cross-domain data collaborative processing system, and storing them according to the type of operating condition; S32, inputting the classified parameters into the load adaptation model of the electrolysis device, setting constraints for different ratio combinations, and calculating the load adaptation coefficient and operating status parameters for each combination; S33, sorting the calculation results, eliminating ratio combinations with adaptation coefficients exceeding the preset range, and retaining the set of configuration parameters that meet the basic operating requirements; S34, combining the simulation results of the water electrolysis multi-field coupling simulation model, performing a second screening on the retained set of configuration parameters to further narrow down the feasible domain.

[0014] Further, S4 includes the following sub-steps: S41, the cross-domain data collaborative processing system classifies and analyzes the collected electrolyzer operation data, separator operation data, purity detection data, and alkali flow data, extracting time series features and amplitude features of different types of data; S42, it establishes association rules between different types of data, constructs a data association matrix through the water electrolysis guiding matrix incremental update algorithm, identifies data outliers, and marks them; S43, it verifies the marked outlier data based on the association matrix, judges the validity of the outlier data by combining historical operation data and simulation model results, and removes invalid outlier data; S44, based on the verified valid data, it establishes a nonlinear mapping relationship between parameters and device operating status, providing data support for configuration optimization.

[0015] Further, S5 includes the following sub-steps: S51, based on the mapping relationship established in S4, the configuration schemes within the feasible region are updated iteratively using the water electrolysis guiding matrix incremental update algorithm, adjusting the correspondence between the number of electrolyzer groups and the separators; S52, the updated configuration schemes are simulated using the water electrolysis multi-field coupling simulation model, obtaining the purity fluctuation curve, output curve, and load adaptation curve for each scheme; S53, the comprehensive evaluation index of the schemes is calculated based on the simulation curves, and the schemes are ranked according to the quality of the index, retaining the top 20% of high-quality schemes; S54, the high-quality schemes are iteratively optimized in multiple rounds, dynamically adjusting the linkage logic of auxiliary equipment and the branch connection method to form the final set of candidate configuration schemes.

[0016] A water electrolysis system configuration optimization platform based on multi-source data collaborative computing is applied to a water electrolysis system configuration optimization method based on multi-source data collaborative computing. The platform includes: a multi-source data cross-domain acquisition and transmission unit, a water electrolysis multi-field coupling simulation calculation unit, a guiding matrix incremental update processing unit, a load adaptation parameter calculation unit, a configuration scheme intelligent screening and optimization unit, and a configuration result output and execution unit. The multi-source data cross-domain acquisition and transmission unit collects operating parameters of the electrolyzer, gas-liquid separator, and auxiliary equipment through different types of sensors, and transmits them to the water electrolysis multi-field coupling simulation calculation unit after data encoding processing. The water electrolysis multi-field coupling simulation calculation unit receives... After data processing, multiple dynamic simulations are performed, and the simulation results are synchronized to the incremental update processing unit of the guidance matrix and the load adaptation parameter calculation unit. The incremental update processing unit of the guidance matrix iteratively adjusts the initial configuration matrix, and the load adaptation parameter calculation unit calculates the adaptation coefficients under different operating conditions. The results of both are input into the intelligent screening and optimization unit of the configuration scheme. The intelligent screening and optimization unit of the configuration scheme optimizes the configuration scheme by combining a multi-round screening model and transmits the optimal configuration result to the configuration result output and execution unit. The configuration result output and execution unit converts the configuration parameters into control commands and sends them to the control module of the water electrolysis system to implement the configuration scheme.

[0017] Beneficial Effects: This invention proposes a method and platform for optimizing the configuration of a water electrolysis system based on multi-source data collaborative computation. Through a cross-domain data collaborative processing system, it achieves comprehensive acquisition and cross-verification of operating parameters from multiple devices such as electrolyzers and gas-liquid separators. Combined with a multi-field coupling simulation model for water electrolysis, it accurately replicates the interaction laws of electric, temperature, and fluid fields, solving the problem of traditional methods lacking multi-field collaboration and multi-source data linkage. This allows the configuration scheme to fully adapt to dynamic changes under complex operating conditions. The incremental update algorithm of the water electrolysis guiding matrix achieves iterative optimization of the configuration parameter matrix, and, combined with the electrolysis unit load adaptation model, accurately matches different load fluctuation scenarios. This compensates for the shortcomings of traditional configuration dynamic update and screening capabilities, ensuring a high degree of consistency between core configurations such as electrolyzer grouping and separator connection methods and real-time operating status. Simultaneously, the platform, through the orderly linkage of six functional units, forms a closed loop of data acquisition, simulation, parameter updating, scheme screening, and execution, significantly improving the scientific nature and adaptability of the configuration scheme, effectively improving hydrogen production efficiency, gas purity, and system operational stability, providing reliable support for the efficient operation of water electrolysis systems in complex energy scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the overall steps of the method of the present invention; Figure 2 This is a flowchart of method step S3 of the present invention; Figure 3 This is a flowchart of method step S4 of the present invention; Figure 4 This is a flowchart of step S5 of the method of the present invention; Figure 5 This is a diagram showing the platform unit composition of the present invention; Figure 6 This is a visualization of the platform interface of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 As shown, the water electrolysis system configuration optimization method based on multi-source data collaborative computing includes the following steps: S1 collects data on the gas production of the electrolyzer, the processing capacity of the gas-liquid separator, the gas purity detection value, the alkali flow rate data and load fluctuation parameters during the operation of the water electrolysis system through the cross-domain data collaborative processing system, constructs a multi-dimensional data acquisition matrix and transmits it in real time; Specifically, step S1 uses a cross-domain data collaborative processing system that integrates a distributed sensor network, edge computing nodes, and a 5G industrial transmission module to conduct comprehensive and high-frequency data collection on the core operating parameters of the water electrolysis system. In practice, the electrolyzer's gas production parameters are collected instantaneously every 10 milliseconds using a high-precision flow sensor installed at the electrolyzer's outlet, simultaneously recording 1000 sets of data over 60 consecutive seconds to capture dynamic changes. The gas-liquid separator's processing capacity is collected, including the inlet and outlet gas-liquid mixed flow rate, the separated gas flow rate, and the liquid reflux flow rate. An electromagnetic flowmeter is used to update the data every 5 milliseconds to ensure accurate reflection of separation efficiency. Gas purity is detected by an online gas chromatograph, monitoring hydrogen purity, oxygen purity, and trace impurity content every 20 milliseconds, with detection accuracy controlled within 0.01%. Alkali flow data is collected every 8 milliseconds using turbine flowmeters installed at the electrolyzer's inlet, outlet, and circulation pipeline, covering a flow range of 0-50 cubic meters per hour. Load fluctuation parameters focus on grid voltage fluctuations, current fluctuation amplitudes, load change rates, and durations, collected every 3 milliseconds using voltage and current sensors to comprehensively capture the dynamic fluctuation characteristics of the 0-100% load range. After data acquisition, the system constructs a 1024×2048-dimensional multi-source data acquisition matrix based on device type, parameter category, and acquisition timestamp. This matrix includes 1024 acquisition nodes distributed across key locations on the device and 2048 subdivided parameters. Real-time data transmission is achieved through 5G industrial internet and edge computing nodes, with transmission latency controlled within 50 milliseconds. This provides comprehensive, accurate, and real-time data support for subsequent simulation and configuration optimization. This step breaks down the data barriers of single devices and single dimensions, constructing a multi-source, heterogeneous, and dynamic data resource pool to ensure that subsequent optimization analysis is based on real and complete operational data.

[0021] S2, call the water electrolysis multi-field coupling simulation model to dynamically simulate the collected data, generate multi-field distribution characteristic data of the device operation status, and iteratively adjust the initial configuration parameter matrix by combining the water electrolysis guidance matrix incremental update algorithm; Specifically, step S2 transforms the raw collected data into characteristic information reflecting the operating status of the device through simulation and algorithm iteration, and makes preliminary adjustments to the initial configuration parameters. In practice, the water electrolysis multi-field coupling simulation model preset in the system is first invoked. This model has built-in multi-field coupling calculation logic for 1000 monitoring points. Based on the data collected in S1, such as the gas production of the electrolyzer, the flow rate of the alkali solution, and the load fluctuation, it can accurately simulate the distribution and interaction of the electric field, temperature field, and fluid field, and generate multi-field distribution characteristic data including the electric field gradient, gas density gradient, temperature field distribution, and alkali viscosity coefficient of each monitoring point. The simulation step size is set to 20 milliseconds to ensure dynamic tracking of changes in the operating status of the device. Subsequently, an incremental update algorithm for the water electrolysis guidance matrix was initiated. Based on the initial configuration parameter matrix, which includes 128 core configuration parameters such as the number of electrolyzers, separator ratio, and branch connection method, the algorithm iteratively adjusts the initial matrix according to the incremental values ​​of multi-source data and the preset weight matrix, with an update step size coefficient of 0.01. During each iteration, the adjustment direction is corrected in conjunction with the results of multi-field coupled simulation. The number of iterations is set to 50 rounds to ensure that the configuration parameters are initially adapted to the current operating conditions. The entire process is executed in real time through the edge computing module of the cross-domain data collaborative processing system, with the computation latency controlled within 100 milliseconds. This achieves both accurate replication of multi-field effects and dynamic iteration of configuration parameters, providing an optimized initial parameter foundation for subsequent ratio calculations and scheme selection, and avoiding the problem of disconnect between traditional static configuration and actual operating conditions.

[0022] S3. Based on the load adaptation model of the electrolysis unit, a preliminary calculation is made on the ratio of the number of electrolyzers and separators under different operating conditions to determine the feasible range of configuration parameters. Specifically, step S3 determines the feasible domain of configuration parameters through load adaptation calculation, defining a reasonable range for subsequent precise optimization and avoiding invalid calculations and scheme selection. In practice, firstly, the cross-domain data collaborative processing system extracts different load condition data collected in S1, dividing it into 10 operating condition intervals (10%-100% load, each interval being 10%). For each interval, 20 core parameters are extracted, including the rated gas production of the electrolyzer, the maximum purity fluctuation value, the maximum processing capacity of the gas-liquid separator, and the optimal flow rate of the alkali circulation, and stored in a distributed database according to the operating condition type. Subsequently, the categorized parameters are input into the load adaptation model of the electrolysis unit. The model has built-in constraints for different ratio combinations, including the ratio range of the number of electrolyzers and separators (1:1 to 5:1), the maximum load carrying capacity of a single electrolyzer, the separator processing capacity threshold, etc. For each operating condition interval, 100 sets of load adaptation coefficients and corresponding operating status parameters (including gas purity, gas production stability, energy consumption, etc.) under different ratio combinations are calculated. Next, the 1000 sets of calculation results were sorted in descending order of their fit coefficients, and combinations with fit coefficients below 0.8 were eliminated, retaining 300 sets of configuration parameters that meet the basic operational requirements. Finally, based on the simulation results of the multi-field coupling simulation model of water electrolysis in S2, the retained parameter sets were further screened, eliminating combinations where the multi-field distribution in the simulation results exceeded the safety threshold, further narrowing the feasible domain to less than 100 sets. This step, through multi-dimensional constraints and simulation verification, effectively compressed the optimization space of the configuration parameters, improved the efficiency and accuracy of subsequent optimization and screening, and ensured that the configuration scheme not only meets the load fit requirements but also complies with the safety specifications for multi-field collaborative operation.

[0023] S4, cross-validates multi-source data through a cross-domain data collaborative processing system, extracts calibration feature parameters that affect configuration optimization, and establishes a mapping relationship between parameters and device operating status; Specifically, step S4 establishes a precise mapping relationship between parameters and device operating status through multi-source data cross-validation and feature extraction, providing core data support for subsequent configuration optimization. In practice, the data analysis module of the cross-domain data collaborative processing system first classifies and analyzes the electrolyzer operating data (including 30 parameters such as gas production, energy consumption, and temperature), separator operating data (including 15 parameters such as processing flow rate and separation efficiency), purity detection data (including 10 parameters such as hydrogen purity and impurity content), and alkali flow data (including 8 parameters such as influent flow rate and circulation flow rate) collected in S1. Using time series analysis and amplitude feature extraction algorithms, 20 feature indicators, including peak value, valley value, mean, variance, and rate of change, are extracted for each type of data. Subsequently, based on the correlation analysis logic of the water electrolysis guided matrix incremental update algorithm, a 100×100-dimensional data correlation matrix is ​​constructed. Matrix elements represent the correlation degree between different parameters. By setting a correlation coefficient threshold of 0.7, outliers (including extreme values ​​exceeding 3 standard deviations and abrupt changes) are identified and marked. Next, the marked abnormal data were validated based on the correlation matrix. This involved accessing 100,000 sets of historical operating data from the distributed database over the past three months, along with simulation model results from S2. Similarity matching and trend consistency analysis were used to determine the validity of the abnormal data, eliminating invalid abnormal data (within 5%). Finally, a nonlinear fitting algorithm was employed to establish a mapping relationship between 20 key characteristic parameters and the device's operating status (including five core indicators such as hydrogen production efficiency, gas purity, and system stability) based on the validated valid data (approximately 80,000 sets). The goodness of fit of the mapping model was controlled above 0.95, ensuring accurate prediction of the device's operating status through parameter changes and providing a scientific basis for subsequent optimization and adjustment of the configuration scheme.

[0024] S5 combines the incremental update algorithm of the water electrolysis guidance matrix to perform multiple rounds of screening of configuration schemes in the feasible domain, and dynamically adjusts the grouping method of electrolyzers, the connection branches of separators and the linkage logic of auxiliary equipment. Specifically, step S5 uses multiple rounds of iterative screening and dynamic adjustment to select the optimal configuration scheme from the feasible region. In practice, firstly, based on the parameter-operating state mapping relationship established in S4, the incremental update algorithm of the water electrolysis guiding matrix is ​​called to perform the initial iterative update of the 100 configuration schemes within the feasible region determined in S3. The algorithm adjusts the number of electrolyzer groups (ranging from 5 to 20 cells per group) and the separator correspondence (connecting 1 to 3 separators per group of separators) with a step size coefficient of 0.02. During the iteration process, the adjustment direction is dynamically corrected based on real-time collected load fluctuation data. The initial iteration count is set to 30 rounds, retaining the top 50 schemes with the highest adaptation coefficient. Subsequently, the water electrolysis multi-field coupling simulation model is called to simulate the operation of the updated 50 schemes. The simulation duration is set to 120 seconds, and the purity fluctuation curve, output curve, and load adaptation curve of each scheme are recorded every 20 milliseconds, generating a curve dataset containing 3000 data points. Next, a comprehensive evaluation index for the schemes was calculated based on the curve dataset. This index includes three sub-indicators: purity stability (weight 0.4), output target achievement rate (weight 0.3), and load adaptability (weight 0.3). A comprehensive score was calculated using a weighted summation method, and the 50 schemes were ranked according to their scores, retaining the top 20% (10 high-quality schemes). Finally, the 10 high-quality schemes underwent multiple rounds of iterative optimization, with 20 iterations. In each iteration, the linkage logic of auxiliary equipment (including pumps, valves, heat exchangers, etc.) (such as valve opening and closing response time, pump start and stop thresholds, etc.) and the branch connection method (such as the number of parallel branches, the position of series nodes, etc.) were dynamically adjusted. After each iteration, the optimization effect was verified through a simulation model, ultimately forming a set of 5 candidate configuration schemes with the best comprehensive performance. This step, through multiple iterations and dynamic adjustments, ensures that the configuration schemes can maintain optimal operating status under complex conditions such as multi-field coordination and load fluctuations.

[0025] S6. Based on the screening results, determine the final configuration quantity and connection method of electrolyzers, gas-liquid separators and auxiliary equipment to form an optimized configuration scheme adapted to different operating scenarios.

[0026] Specifically, step S6 transforms the selected candidate configuration schemes into specific executable configuration parameters, forming a final scheme adapted to different operating scenarios. In practice, the five candidate configuration schemes formed in S5 are first analyzed for scenario matching. Combining the current operating scenario parameters (including 10 scenario indicators such as load level, grid stability, and gas production demand) collected by the cross-domain data collaborative processing system, a scenario similarity matching algorithm is used to accurately match the candidate schemes with 10 typical operating scenarios (such as full-load stable operation, low-load fluctuating operation, and peak gas production operation). Then, based on the matching results, the final configuration quantity of electrolyzers, gas-liquid separators, and auxiliary equipment corresponding to each scenario is determined. The configuration quantity of electrolyzers ranges from 20 to 100 units (adjusted in units of 5), the configuration quantity of gas-liquid separators is determined according to a ratio of 1:1 to 5:1 between electrolyzers and separators, and the configuration quantity of auxiliary equipment (pumps, valves, heat exchangers, etc.) is determined based on the main equipment configuration scale and operating requirements (e.g., 2 circulating pumps and 3 heat exchangers for every 10 electrolyzers). Simultaneously, the connection methods of the equipment are clearly defined. Electrolyzers are connected in parallel in groups (5-20 units per group), and separators are connected to the electrolyzer groups via branch correspondence (1-3 separators are connected to each group of flowmeters). Auxiliary equipment is connected to the main system in series or parallel according to functional modules, forming a complete equipment connection topology. Finally, for the final configuration scheme of each scenario, a detailed configuration parameter list is compiled, including 50 sub-items such as equipment model, quantity, installation location, connection relationship, and operating parameter thresholds, forming an optimized configuration scheme set adapted to different operating scenarios. This step is transmitted in real time to the control center of the water electrolysis system through a cross-domain data collaborative processing system, and can also be stored in a distributed database for subsequent use when switching operating scenarios. This ensures that the unit can operate based on the optimal configuration parameters under different operating conditions, achieving a comprehensive improvement in hydrogen production efficiency, gas purity, and system stability. The implementation of this step marks the completion of the closed loop of the entire optimization configuration process, transforming data-driven optimization analysis into actual unit operation configuration.

[0027] Preferably, the expression for the multi-field coupled simulation model of water electrolysis in S2 is: ,in: This is the matrix of multi-field coupled simulation results. Let be the weight coefficient for the i-th monitoring point. Let be the electric field gradient vector at the i-th monitoring point. Let be the gas density gradient vector at the i-th monitoring point. For multi-field coupling coefficients, Let i be the temperature field distribution value at the i-th monitoring point. For time variables, Let be the viscosity coefficient of the alkali solution at the i-th monitoring point. This represents the total number of monitoring points.

[0028] Specifically, the multi-field coupling simulation model for water electrolysis in step S2 accurately replicates the interaction mechanism of the electric field, temperature field, and fluid field during the operation of the water electrolysis system, providing scientific multi-field distribution data support for parameter adjustment. During the implementation of this model, the number of key monitoring points is first determined. Considering the scale of the device and the required operational accuracy, the total number of monitoring points is set to 1000, evenly distributed in core locations such as the electrolyzer reaction area, the gas-liquid separator, and the alkali circulation pipeline. For each monitoring point, key parameters such as the electric field gradient, gas density gradient, temperature field distribution, and alkali flow viscosity coefficient are collected through a cross-domain data collaborative processing system. The electric field gradient is calculated through the voltage distribution between electrodes, the gas density gradient is derived based on the gas production and volume changes in different regions, the temperature field distribution is collected in real time by embedded temperature sensors, and the alkali flow viscosity coefficient is dynamically calculated by combining alkali concentration and temperature data. To emphasize the influence weight of data from key areas, the weight coefficients for monitoring points in the core reaction area of ​​the electrolyzer were set to 0.8-1.0, and those in the peripheral areas to 0.3-0.5. The multi-field coupling coefficient was dynamically adjusted between 0.6 and 0.9 based on the operating conditions of the unit. The model generates a multi-field coupling simulation result matrix by superimposing the cross product of the electric field and gas density gradient at each monitoring point, combined with the product of the second-order time derivative of the temperature field and the viscosity coefficient of the alkali solution. This matrix includes multi-field synergistic data from 1000 monitoring points, with a time resolution of 20 milliseconds to ensure dynamic tracking of multi-field distribution changes and provide accurate state feedback for the iterative adjustment of the initial configuration parameter matrix.

[0029] Preferably, the expression for the incremental update algorithm of the water electrolysis guidance matrix in S2 is: ,in: This is the incrementally updated guiding matrix. This is the initial guiding matrix. It is the identity matrix. To update the step size coefficient, For multi-source data incremental matrix, For the data weight matrix, The 2-norm of the data increment matrix. The projection matrix is ​​configured with parameters.

[0030] Specifically, in step S2, the incremental update algorithm for the water electrolysis guidance matrix achieves efficient iterative optimization of the configuration parameter matrix based on the dynamic changes of multi-source data, avoiding the problem of disconnect between traditional static configuration and actual operating status. When implementing this algorithm, the initial configuration parameter matrix includes 128 core configuration parameters such as the number of electrolyzers, gas-liquid separator ratio, branch connection method, and operating parameter thresholds. The matrix dimension is set to 64×64, and the unit matrix adopts a standard unit matrix of the same dimension. The update step size coefficient is dynamically adjusted according to the data increment. When the multi-source data increment is small (change rate less than 10%), the step size coefficient is set to 0.01-0.03 to ensure smooth adjustment of configuration parameters; when the data increment is large (change rate greater than 30%), the step size coefficient is adjusted to 0.05-0.08 to accelerate the adaptation speed of configuration parameters. The multi-source data increment matrix is ​​constructed based on the difference between real-time data collected by the cross-domain data collaborative processing system and historical benchmark data, including 2048 detailed data increment indicators such as gas production increment, purity change, flow fluctuation value, and load fluctuation amplitude. The data weight matrix is ​​set according to the degree of influence of parameters on configuration optimization. The weights for parameters related to electrolyzer gas production and gas purity are set to 0.7-0.9, those related to alkali flow rate and load fluctuation are set to 0.4-0.6, and other auxiliary parameters are set to 0.1-0.3. The algorithm normalizes the data by multiplying the data increment matrix and the weight matrix and dividing by the 2-norm of the data increment matrix. This normalization is then superimposed with the product of the identity matrix and the step size coefficient. Finally, the calculation result is mapped to the feasible region of the configuration parameters through the configuration parameter projection matrix, generating an incrementally updated guiding matrix. The entire calculation process is executed in real time through the edge computing module, with a single iteration time controlled within 50 milliseconds. The number of iterations is set to 50 rounds to ensure that the configuration parameters quickly adapt to dynamic changes in multi-source data.

[0031] Preferably, the expression for the load adaptation model of the electrolysis device in S3 is: ,in: For load adaptability factor, The total gas production of the system, This is the load adjustment coefficient. Let be the influence coefficient of the j-th type of load fluctuation. Let j be the amplitude of the load fluctuation. Number of load fluctuation types This represents the total number of electrolytic cells. This refers to the rated gas production of a single electrolytic cell. The separator processing efficiency coefficient. This is the matching coefficient between the electrolytic cell and the separator.

[0032] Specifically, in step S3, the electrolysis unit load adaptation model quantifies the configuration adaptation degree of the electrolysis unit under different load conditions, providing a scientific basis for determining the feasible domain of configuration parameters. When implementing this model, the total gas production of the system is calculated based on the real-time summation of the gas production of each electrolyzer, with a time resolution set to 10 milliseconds. The rated gas production of a single electrolyzer is fixed at 5-20 cubic meters per hour, depending on the unit model. The load adjustment coefficient is dynamically adjusted according to the current load level: 1.0-1.1 for full load operation (load rate 90%-100%), 0.8-1.0 for medium load operation (load rate 50%-89%), and 0.6-0.8 for low load operation (load rate 10%-49%). Load fluctuations are categorized into four types: voltage fluctuations, current fluctuations, gas production and demand fluctuations, and grid frequency fluctuations. The impact coefficients for each type are set based on historical data statistical analysis: voltage fluctuation impact coefficients are set at 0.3-0.5, current fluctuation impact coefficients at 0.4-0.6, gas production and demand fluctuation impact coefficients at 0.5-0.7, and grid frequency fluctuation impact coefficients at 0.2-0.4. Fluctuation amplitudes are calculated by comparing real-time collected data with rated values. The separator processing efficiency coefficient is calculated based on the purity and flow rate data of the gas at the separator's inlet and outlet, ranging from 0.85 to 0.98. The matching coefficient between the electrolyzer and the separator is dynamically adjusted between 0.7 and 0.95 based on the degree of matching between their operating parameters. The model calculates the product of the total gas production of the system, the load adjustment coefficient, and various load fluctuation influencing factors in the numerator term, and calculates the product of the total number of electrolyzers, the rated gas production of a single unit, the separator processing efficiency coefficient, and the ratio adaptation coefficient in the denominator term. Finally, the load adaptation coefficient is obtained. The value of this coefficient is between 0 and 1.2. When the coefficient is between 0.8 and 1.2, it indicates that the configuration ratio is basically adapted to the current load conditions, providing a quantitative standard for delineating the feasible domain.

[0033] Preferably, the cross-validation model expression for the cross-domain data collaborative processing system in S4 is: ,in: The cross-validation result value. This represents the actual value of the I-th sampling point in the k-th data category. The predicted value for the i-th sampling point of the k-th data type. The actual value weighting coefficient, These are the weighting coefficients for the predicted values. For the number of data categories, The number of sampling points for a single type of data. To verify the correction coefficient.

[0034] Specifically, in step S4, the cross-domain data collaborative processing system's data cross-validation model ensures the validity and accuracy of multi-source data through multi-dimensional verification, laying the foundation for establishing precise parameter mapping relationships. During model implementation, data categories are divided into five main types: electrolyzer operation data, gas-liquid separator operation data, gas purity detection data, alkali flow data, and load fluctuation data. The number of sampling points for each category is set to 10,000, covering nearly 10 minutes of operation data. For each sampling point, the actual value is acquired in real-time by sensors, and the predicted value is calculated using a multi-field coupled simulation model for water electrolysis. The weighting coefficient for the actual value is set according to the sensor accuracy: 0.9-1.0 for high-precision sensors (error less than 0.5%) and 0.7-0.8 for ordinary precision sensors (error 1%-2%). The weighting coefficient for the predicted value is dynamically adjusted according to the prediction accuracy of the simulation model. When the model prediction error is less than 3%, the weighting coefficient is set to 0.8-0.9; when the prediction error is greater than 5%, the weighting coefficient is adjusted to 0.5-0.7. The validation correction coefficient is set based on the accuracy statistics of historical validation data, ranging from 0.9 to 1.0. When the historical validation accuracy is higher than 95%, the correction coefficient is set to 0.98-1.0; when the accuracy is between 90% and 95%, the correction coefficient is set to 0.9-0.97. The model calculates the square of the difference between the actual value and the predicted value at each sampling point of each type of data, multiplies it by the corresponding actual value weight coefficient and predicted value weight coefficient, and then calculates the cross-validation result by summing the numerator and denominator and the ratio. This result, combined with the validation correction coefficient, ranges from 0 to 0.2. A result value below 0.05 indicates high data validity, while a result value above 0.15 requires data re-collection and validation to ensure high reliability of the data used to construct the mapping relationship.

[0035] Preferably, the multi-round filtering model expression for the configuration scheme in S5 is: ,in The selected optimal configuration scheme. For the configuration scheme feasible domain, These are the weighting coefficients for purity deviation, yield deviation, and cost deviation, respectively. Let be the purity deviation value of scheme s. Let be the production deviation value of scheme s. Let be the cost deviation value of scheme s. Let be the optimization adaptation coefficient of scheme s.

[0036] Specifically, in step S5, the multi-round screening model for configuration schemes comprehensively considers multiple evaluation indicators to select the optimal configuration scheme from the feasible domain, ensuring that the scheme achieves a balanced optimization in terms of purity, output, and cost. When implementing this model, the feasible domain of the configuration scheme includes the 100 combinations of configuration parameters determined in S3, including different numbers of electrolyzers, separator ratios, grouping methods, and linkage logic. Weighting coefficients are set according to the optimization objectives: the purity deviation weighting coefficient is set at 0.4-0.5 to highlight the core importance of gas purity; the output deviation weighting coefficient is set at 0.3-0.4 to ensure that the gas production meets demand; and the cost deviation weighting coefficient is set at 0.1-0.2 to consider operational economics. The purity deviation value is calculated by the absolute difference between the predicted gas purity and the target purity (e.g., 99.9% hydrogen purity); the output deviation value is the percentage difference between the predicted gas production and the target gas production; and the cost deviation value is calculated based on the difference between the equipment investment and energy consumption costs of the configuration scheme and the benchmark scheme. The optimization adaptation coefficient comprehensively considers the scheme's adaptability to load fluctuations and the stability of multi-field coordinated operation. Its value ranges from 0.8 to 1.0. When the scheme can adapt to more than 80% of load conditions and the multi-field distribution is stable, the adaptation coefficient is set to 0.9-1.0; otherwise, it is reduced proportionally. The model calculates the weighted sum of purity deviation, output deviation, and cost deviation, and then multiplies it by the optimization adaptation coefficient to obtain the scheme's comprehensive evaluation score. The schemes are sorted from smallest to largest score (smaller scores indicate better schemes), and the configuration scheme with the best overall performance is selected. This process is achieved through multiple iterations. In each iteration, the weighting coefficients and adaptation coefficients are adjusted to ensure that the selection results meet the optimization requirements of different operating scenarios, ultimately forming a configuration scheme with strong adaptability and excellent overall performance.

[0037] Preferred, such as Figure 2 As shown, step S3 includes the following sub-steps: S31, extracting core parameters such as rated gas production, maximum purity fluctuation value, and maximum processing capacity of the gas-liquid separator under different load conditions through a cross-domain data collaborative processing system, and storing them according to the type of operating condition; S32, inputting the classified parameters into the load adaptation model of the electrolysis device, setting constraints for different ratio combinations, and calculating the load adaptation coefficient and operating status parameters under each combination; S33, sorting the calculation results, eliminating ratio combinations with adaptation coefficients exceeding the preset range, and retaining the set of configuration parameters that meet the basic operating requirements; S34, combining the simulation results of the water electrolysis multi-field coupling simulation model, performing a second screening on the retained set of configuration parameters to further narrow down the feasible domain.

[0038] Specifically, step S3, through step-by-step parameter extraction, ratio calculation, and multiple rounds of screening, accurately delineates the feasible domain of configuration parameters, providing an efficient and reliable parameter range for subsequent optimization. In practice, S31 uses a cross-domain data collaborative processing system to extract core parameters under different load conditions from the collected multi-source data. These parameters include key indicators such as the rated gas production of the electrolyzer, the maximum purity fluctuation value, and the maximum processing capacity of the gas-liquid separator. These are divided into 10 categories based on a 10%-100% load range (each 10% representing a level), and the corresponding parameters are categorized and stored in a distributed database to ensure accurate correspondence between parameters and operating conditions. S32 inputs the categorized parameters one by one into the electrolysis unit load adaptation model, setting constraints such as the ratio of electrolyzers to separators from 1:1 to 5:1, the maximum load capacity of a single electrolyzer, and the separator processing capacity threshold. This calculates the load adaptation system for 100 different ratio combinations under each operating condition. The calculation results are processed as follows: S33: The 1000 sets of calculation results are sorted from high to low according to the load adaptation coefficient. Invalid combinations with an adaptation coefficient lower than 0.8 are eliminated, and 300 sets of configuration parameters that meet the basic operating requirements are retained. S34: The simulation results of the water electrolysis multi-field coupling simulation model in step S2 are called. The multi-field distribution status corresponding to the retained parameter set is verified. Combinations with electric field, temperature field and fluid field distributions that exceed the safety threshold are eliminated. The feasible domain is further narrowed down to less than 100 sets. Through layer-by-layer screening, it is ensured that the configuration parameters in the feasible domain meet both the load adaptation requirements and the safety specifications for multi-field collaborative operation, which greatly improves the efficiency and accuracy of subsequent optimization screening.

[0039] Preferred, such as Figure 3 As shown, step S4 includes the following sub-steps: S41, the cross-domain data collaborative processing system classifies and analyzes the collected electrolyzer operation data, separator operation data, purity detection data, and alkali flow data, extracting time series features and amplitude features of different types of data; S42, it establishes association rules between different types of data, constructs a data association matrix through the water electrolysis guiding matrix incremental update algorithm, identifies data outliers, and marks them; S43, it verifies the marked outlier data based on the association matrix, judges the validity of the outlier data by combining historical operation data and simulation model results, and removes invalid outlier data; S44, based on the verified valid data, it establishes a nonlinear mapping relationship between parameters and device operating status, providing data support for configuration optimization.

[0040] Specifically, step S4 extracts key feature parameters and establishes precise mapping relationships through multi-stage data processing and verification, providing high-quality data support for configuration optimization. In practice, S41 uses the data analysis module of the cross-domain data collaborative processing system to classify and analyze the collected electrolyzer operating data (including 30 parameters such as gas production, energy consumption, and temperature), separator operating data (including 15 parameters such as processing flow rate and separation efficiency), purity detection data (including 10 parameters such as hydrogen purity and impurity content), and alkali flow data (including 8 parameters such as inlet flow rate and circulation flow rate). Using time series analysis and amplitude feature extraction algorithms, it extracts 20 feature indicators such as peak value, valley value, mean, variance, and rate of change for each type of data. S42, based on the correlation analysis logic of the incremental update algorithm for the water electrolysis guiding matrix, constructs a 100×100-dimensional data correlation matrix. Matrix elements quantify the correlation between different parameters, setting a correlation coefficient threshold of 0.7 to identify... The system marks outliers such as extreme values ​​and abrupt changes exceeding three standard deviations. S43 verifies the marked outliers based on the correlation matrix, calling upon 100,000 sets of historical operating data from the distributed database over the past three months, along with the simulation model results from step S2. It judges the validity of the outliers through similarity matching and trend consistency analysis, removing invalid outliers accounting for no more than 5%. S44 employs a nonlinear fitting algorithm, based on approximately 80,000 sets of valid data after verification, to establish a mapping relationship between 20 key characteristic parameters and five core operating status indicators of the device, including hydrogen production efficiency, gas purity, and system stability. This ensures the goodness of fit of the mapping model is controlled above 0.95, enabling accurate prediction of the device's operating status through parameter changes, providing a scientific and reliable data basis for subsequent optimization and adjustment of the configuration scheme.

[0041] Preferred, such as Figure 4 As shown, S5 includes the following sub-steps: S51, based on the mapping relationship established in S4, the configuration schemes within the feasible region are updated iteratively using the water electrolysis guiding matrix incremental update algorithm, adjusting the correspondence between the number of electrolyzer groups and the separators; S52, the updated configuration schemes are simulated using the water electrolysis multi-field coupling simulation model, obtaining the purity fluctuation curve, output curve, and load adaptation curve for each scheme; S53, the comprehensive evaluation index of the schemes is calculated based on the simulation curves, and the schemes are ranked according to the quality of the index, retaining the top 20% of high-quality schemes; S54, the high-quality schemes are iteratively optimized in multiple rounds, dynamically adjusting the linkage logic of auxiliary equipment and the branch connection method to form the final set of candidate configuration schemes.

[0042] Specifically, step S5, through multiple rounds of iterative updates, simulation verification, and dynamic adjustments, selects the configuration scheme with the best overall performance from the feasible domain. In practice, S51, based on the parameter-operating state mapping relationship established in step S4, calls the water electrolysis guiding matrix incremental update algorithm to perform the initial iterative update of the 100 configuration schemes within the feasible domain determined in step S3. The number of electrolyzer groups (5-20 units per group) and the corresponding relationship of separators (1-3 separators connected to each group of separators) are adjusted by a step size coefficient of 0.02. During the iteration process, the adjustment direction is dynamically corrected based on real-time collected load fluctuation data. The number of iterations is set to 30, retaining the top 50 schemes with the best fit coefficient. S52, the water electrolysis multi-field coupling simulation model is used to simulate the operation of the updated 50 schemes. The simulation duration is set to 120 seconds, and the purity fluctuation curve, output curve, and load fit curve of each scheme are recorded every 20 milliseconds, generating a curve dataset containing 3000 data points. S53... The comprehensive evaluation index of the scheme is calculated based on the curve dataset. This index includes three sub-indicators: purity stability (weight 0.4), output achievement rate (weight 0.3), and load adaptability (weight 0.3). The comprehensive score is calculated by weighted summation. The 50 schemes are ranked according to their scores, and the top 20% of the 10 high-quality schemes are retained. S54 performs 20 rounds of iterative optimization on the 10 high-quality schemes. In each round of iteration, the linkage logic of auxiliary equipment (including pumps, valves, heat exchangers, etc.) (such as valve opening and closing response time, pump start and stop thresholds, etc.) and the branch connection method (such as the number of parallel branches, the position of series nodes, etc.) are dynamically adjusted. After each round of iteration, the optimization effect is verified by simulation model. Finally, a set of 5 candidate configuration schemes with the best comprehensive performance is formed to ensure that the configuration scheme can maintain the optimal operating state under complex conditions such as multi-field coordination and load fluctuation.

[0043] The multi-field coupling simulation model for water electrolysis in this invention replicates the interaction of electric field, temperature field, and fluid field during the operation of a water electrolysis system. It achieves accurate simulation of multi-field distribution through multi-dimensional parameter fusion. In its implementation, 1000 monitoring points are first deployed in key locations such as the electrolyzer reaction zone, the gas-liquid separator, and the alkali circulation pipeline. A cross-domain data collaborative processing system collects the electric field gradient, gas density gradient, temperature field distribution, and alkali flow viscosity coefficient at each monitoring point. The weighting coefficient for monitoring points in the core area of ​​the electrolyzer is set to 0.8-1.0, and for the edge area, it is set to 0.3-0.5. The multi-field coupling coefficient is dynamically adjusted between 0.6 and 0.9 according to the operating conditions. The model generates a multi-field coupling simulation result matrix by superimposing the cross product of the electric field and gas density gradient at each monitoring point, combined with the product of the second-order time derivative of the temperature field and the alkali viscosity coefficient. The time resolution is set to 20 milliseconds. This model provides accurate multi-field distribution characteristic data for configuration optimization, dynamically tracks changes in device operating status, breaks through the limitations of traditional single-condition simulation, and ensures that configuration parameter adjustments are fully adapted to the complex physical and chemical environment inside the device through quantitative analysis of multi-field synergistic effects. This lays a scientific state perception foundation for subsequent configuration scheme optimization and makes the configuration scheme more in line with actual operating scenarios.

[0044] The incremental update algorithm for the water electrolysis guidance matrix is ​​an algorithm for dynamically iteratively optimizing configuration parameters. It efficiently adjusts the initial configuration matrix based on incremental changes in multi-source data. In its implementation, it uses a 64×64-dimensional initial configuration matrix containing 128 core configuration parameters (including the number of electrolyzers and separator ratios) as a foundation. A cross-domain data collaborative processing system is used to obtain 2048 detailed incremental data indicators, such as gas production increment and purity change, to construct a multi-source data incremental matrix. The update step size coefficient is dynamically adjusted according to the data change rate: 0.01-0.03 when the change rate is below 10%, and 0.05-0.08 when it is above 30%. The data weight matrix allocates weights according to the parameter's influence: parameters related to gas production and purity are set to 0.7-0.9, while parameters related to alkali flow rate and load fluctuation are set to 0.4-0.6. The algorithm iteratively updates the configuration matrix by multiplying the data increment matrix and the weight matrix, performing normalization, and combining the identity matrix and projection matrix. Each iteration takes less than 50 milliseconds, and the algorithm performs 50 iterations. This algorithm responds in real-time to dynamic changes in multi-source data, enabling precise iteration of configuration parameters. It solves the problem of disconnect between traditional static configuration and actual operating conditions, improving the speed and accuracy of configuration parameter adaptation through incremental updates, ensuring that the device configuration always maintains a high degree of consistency with real-time operating conditions.

[0045] The load adaptation model for electrolysis units is the core model for quantifying the degree of equipment configuration adaptation under different load conditions. It achieves the scientific delineation of the configuration feasibility domain through multi-parameter fusion calculation. In its implementation, based on data such as the total gas production of the system (time resolution 10 milliseconds) and the rated gas production of a single electrolyzer (5-20 cubic meters per hour) collected by the cross-domain data collaborative processing system, the system is divided into 10 operating condition intervals (10%-100% load) according to the load level. The load adjustment coefficient is set to 1.0-1.1 at full load, 0.8-1.0 at medium load, and 0.6-0.8 at low load. The model incorporates four types of load fluctuations: voltage, current, gas production demand, and grid frequency, with corresponding influence coefficients of 0.3-0.5, 0.4-0.6, 0.5-0.7, and 0.2-0.4, respectively. Combined with the separator processing efficiency coefficient (0.85-0.98) and the matching coefficient (0.7-0.95), the load matching coefficient is calculated by the ratio of the numerator (total gas production, load adjustment coefficient, and fluctuation influence factor product) to the denominator (equipment quantity, rated parameters, and efficiency coefficient product). This model screens configuration combinations that meet load requirements, determines the feasible region of configuration parameters, quantifies the suitability of configuration schemes to load conditions, eliminates invalid configuration combinations, and compresses the feasible region from 1000 groups to less than 100 groups, significantly improving the efficiency and targeting of subsequent optimization screening and ensuring that configuration schemes meet the operational needs of different load scenarios.

[0046] The cross-domain data collaborative processing system is the core data support of this invention, responsible for the acquisition, transmission, processing, and verification of multi-source data, realizing deep linkage of data from multiple devices and operating conditions. In its implementation, it integrates a distributed sensor network, a 5G industrial transmission module, and edge computing nodes, collecting 2048 detailed parameters such as electrolyzer gas production and separator processing capacity at different frequencies of 3-20 milliseconds, constructing a 1024×2048-dimensional data acquisition matrix, with transmission latency controlled within 50 milliseconds. The system processes data through a four-step process: classification analysis, correlation analysis, anomaly verification, and mapping. First, 20 feature indicators of each type of data are extracted; then, a 100×100-dimensional data correlation matrix is ​​constructed, outliers exceeding three standard deviations are marked, and anomalies are verified by combining 100,000 sets of historical data and simulation results, eliminating less than 5% of invalid data. Finally, a parameter mapping relationship with a goodness of fit of 0.95 or higher is established based on 80,000 sets of valid data. This system provides comprehensive, accurate, and real-time data resources to support model calculations and algorithm iterations. It breaks down data barriers of single devices and single dimensions, solves the problems of scattered and insufficient correlation in traditional data processing, and provides high-quality data support for multi-field coupled simulations and incremental update algorithms. It ensures that the entire configuration optimization process is based on real and complete operational data, thereby improving the scientificity and reliability of the configuration scheme.

[0047] like Figure 5The aforementioned water electrolysis system configuration optimization platform based on multi-source data collaborative computing is applied to a water electrolysis system configuration optimization method based on multi-source data collaborative computing. It includes: a multi-source data cross-domain acquisition and transmission unit, a water electrolysis multi-field coupling simulation calculation unit, a guiding matrix incremental update processing unit, a load adaptation parameter calculation unit, a configuration scheme intelligent screening and optimization unit, and a configuration result output and execution unit. The multi-source data cross-domain acquisition and transmission unit collects operating parameters of the electrolyzer, gas-liquid separator, and auxiliary equipment through different types of sensors, and transmits them to the water electrolysis multi-field coupling simulation calculation unit after data encoding processing. The water electrolysis multi-field coupling simulation calculation unit then... After receiving the data, multiple dynamic simulations are performed, and the simulation results are synchronized to the incremental update processing unit of the guiding matrix and the load adaptation parameter calculation unit. The incremental update processing unit of the guiding matrix iteratively adjusts the initial configuration matrix, and the load adaptation parameter calculation unit calculates the adaptation coefficients under different operating conditions. The results of both are input into the intelligent screening and optimization unit of the configuration scheme. The intelligent screening and optimization unit of the configuration scheme optimizes the configuration scheme by combining a multi-round screening model and transmits the optimal configuration result to the configuration result output and execution unit. The configuration result output and execution unit converts the configuration parameters into control commands and sends them to the control module of the water electrolysis system to implement the configuration scheme.

[0048] like Figure 6 As shown, the multi-source data cross-domain acquisition and transmission unit serves as the core of the platform's data input, and its functionality is deeply integrated with the parameter acquisition logic of the hydrogen production control panel interface. This unit, through a distributed sensor network deployed throughout the entire hydrogen production and purification process, accurately captures key parameters set on the panel, such as hydrogen production pressure, hydrogen production temperature, hydrogen purity, and alkali flow rate. Simultaneously, it collects load-related data such as high / low pressure alarms, hydrogen / oxygen level interlocks, and rectifier current / voltage fluctuations, covering 2048 detailed monitoring indicators. The acquisition process strictly adheres to the dynamic monitoring frequency set on the panel: pressure and temperature parameters are acquired every 10 milliseconds, hydrogen purity is updated every 20 milliseconds, and alkali flow rate and load fluctuation data are acquired every 3 milliseconds, ensuring the capture of parameter changes within the 10%-100% load range. After all the collected data is encoded, a data matrix is ​​constructed in 1024×2048 dimensions. The data is then transmitted in real time to the water electrolysis multi-field coupling simulation calculation unit through the industrial transmission module with a delay of no more than 50 milliseconds. This provides the raw data for subsequent simulations that is highly consistent with the thresholds set on the panel and the real-time operating status, perfectly matching the first link of "data acquisition-transmission-simulation" and laying the foundation for the platform's data-driven approach.

[0049] This paper presents a method and platform for optimizing the configuration of a water electrolysis system based on multi-source data collaborative computing. It constructs a complete system integrating deep multi-source data collaboration and multiple technologies. Through a cross-domain data collaborative processing system, it achieves comprehensive collection and cross-verification of operating parameters of the electrolyzer, gas-liquid separator, and auxiliary equipment, breaking down the barriers of scattered data and insufficient correlation in traditional configurations. Relying on a multi-field coupled simulation model for water electrolysis, it accurately recreates the interaction between the electric field, temperature field, and fluid field. Combined with a load adaptation model, it scientifically calculates the equipment ratio under different operating conditions, solving the problem that existing technologies struggle to simultaneously handle dynamic changes in multiple fields and load fluctuations. Simultaneously, it uses a guided matrix incremental update algorithm to iteratively optimize configuration parameters, coupled with a multi-round screening mechanism to dynamically adjust equipment grouping, connection methods, and linkage logic, ensuring a high degree of adaptation between the configuration scheme and real-time operating status, significantly improving the accuracy and flexibility of the configuration.

[0050] This method and platform address the lack of multi-field collaboration and multi-source data linkage by establishing a cross-domain data collaborative processing system to link data from multiple devices and operating conditions. It integrates the multi-field interaction laws using a multi-field coupled simulation model, ensuring that the configuration scheme fully considers dynamic influencing factors under complex environments. To address the deficiency in dynamic updating and filtering capabilities of configuration parameters, it continuously optimizes the configuration matrix through an incremental update algorithm. Utilizing a load adaptation model and a multi-round filtering mechanism, it accurately matches load fluctuation scenarios and dynamically adjusts core configuration parameters, avoiding the disconnect between traditional solutions and actual operating conditions. Finally, through the orderly linkage of six functional units, a closed-loop execution system is formed, effectively improving the operating efficiency, gas purity, and system stability of the water electrolysis system.

[0051] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent 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 method for optimizing the configuration of a water electrolysis system based on multi-source data collaborative computation, characterized in that, Includes the following steps: S1. Collect data on electrolyzer gas production, gas-liquid separator processing capacity, gas purity detection values, alkali flow rate, and load fluctuation parameters during the operation of the water electrolysis system through a cross-domain data collaborative processing system. Construct a multi-dimensional data acquisition matrix and transmit it in real time. S2. Use a multi-field coupling simulation model for water electrolysis to dynamically simulate the collected data, generating multi-field distribution characteristic data including electric field gradient, gas density gradient, temperature field distribution, and alkali viscosity coefficient at each monitoring point. Iterate and adjust the initial configuration parameter matrix using a water electrolysis guidance matrix incremental update algorithm. S3. Based on the electrolysis unit load adaptation model, adjust the ratio of electrolyzers to separators under different operating conditions. Preliminary calculations are performed to determine the feasible range of configuration parameters, including different numbers of electrolyzers, separator ratios, grouping methods, and linkage logic; S4, cross-validation of multi-source data is conducted through a cross-domain data collaborative processing system to extract calibration feature parameters that affect configuration optimization and establish a mapping relationship between parameters and device operating status; S5, configuration schemes within the feasible range are screened multiple times using the water electrolysis guidance matrix incremental update algorithm, dynamically adjusting the grouping method of electrolyzers, separator connection branches, and linkage logic of auxiliary equipment; S6, based on the screening results, the final configuration quantity and connection method of electrolyzers, gas-liquid separators, and auxiliary equipment are determined to form an optimized configuration scheme adapted to different operating scenarios; The expression for the incremental update algorithm of the water electrolysis guidance matrix in S2 is as follows: ； in, This is the incrementally updated guiding matrix. This is the initial guiding matrix. It is the identity matrix. To update the step size coefficient, For multi-source data incremental matrix, For the data weight matrix, The 2-norm of the data increment matrix. To configure the projection matrix; The multi-round filtering model expression for the configuration scheme in S5 is as follows: ； in, The selected optimal configuration scheme. For the configuration scheme feasible domain, These are the weighting coefficients for purity deviation, yield deviation, and cost deviation, respectively. Let be the purity deviation value of scheme s. Let be the production deviation value of scheme s. Let be the cost deviation value of scheme s. Let be the optimization fit coefficient of scheme s; S3 includes the following steps: S31, extracting core parameters such as rated gas production, maximum purity fluctuation value, and maximum processing capacity of the gas-liquid separator under different load conditions through a cross-domain data collaborative processing system, and storing them according to the type of operating condition; S32, inputting the classified parameters into the load adaptation model of the electrolysis device, setting constraints for different ratio combinations, and calculating the load adaptation coefficient and operating status parameters for each combination; S33, sorting the calculation results, eliminating ratio combinations with adaptation coefficients exceeding the preset range, and retaining the set of configuration parameters that meet the basic operating requirements; S34, combining the simulation results of the water electrolysis multi-field coupling simulation model, performing a second screening on the retained set of configuration parameters to further narrow down the feasible domain. S4 includes the following sub-steps: S41, the cross-domain data collaborative processing system classifies and analyzes the collected electrolyzer operation data, separator operation data, purity detection data, and alkali flow data, extracting time series features and amplitude features of different types of data; S42, it establishes association rules between different types of data, constructs a data association matrix through the water electrolysis guiding matrix incremental update algorithm, identifies data outliers, and marks them; S43, it verifies the marked outlier data based on the association matrix, judges the validity of the outlier data by combining historical operation data and simulation model results, and removes invalid outlier data; S44, based on the verified valid data, it establishes a nonlinear mapping relationship between parameters and device operating status, providing data support for configuration optimization.

2. The water electrolysis system configuration optimization method based on multi-source data collaborative computation according to claim 1, characterized in that, The expression for the multi-field coupling simulation model of water electrolysis in S2 is as follows: ； in, This is the matrix of multi-field coupled simulation results. Let be the weight coefficient for the i-th monitoring point. Let be the electric field gradient vector at the i-th monitoring point. Let be the gas density gradient vector at the i-th monitoring point. For multi-field coupling coefficients, Let i be the temperature field distribution value at the i-th monitoring point. For time variables, Let be the viscosity coefficient of the alkali solution at the i-th monitoring point. This represents the total number of monitoring points.

3. The water electrolysis system configuration optimization method based on multi-source data collaborative computation according to claim 1, characterized in that, The expression for the load adaptation model of the electrolysis unit in S3 is as follows: ； in, For load adaptability factor, This represents the total gas production of the system. This is the load adjustment coefficient. Let be the influence coefficient of the j-th type of load fluctuation. Let j be the amplitude of the load fluctuation. Number of load fluctuation types This represents the total number of electrolytic cells. This refers to the rated gas production of a single electrolytic cell. The separator processing efficiency coefficient. This is the matching coefficient between the electrolytic cell and the separator.

4. The water electrolysis system configuration optimization method based on multi-source data collaborative computation according to claim 1, characterized in that, The data cross-validation model expression for the cross-domain data collaborative processing system in S4 is as follows: ； in, The cross-validation result value. This represents the actual value of the I-th sampling point in the k-th data category. The predicted value for the i-th sampling point of the k-th class of data. The actual value weighting coefficient, The weighting coefficients for the predicted values. For the number of data categories, The number of sampling points for a single type of data. To verify the correction coefficient.

5. The water electrolysis system configuration optimization method based on multi-source data collaborative computation according to claim 1, characterized in that, S5 includes the following steps: S51, based on the mapping relationship established in S4, the configuration scheme in the feasible region is updated for the first time using the water electrolysis guiding matrix incremental update algorithm, and the correspondence between the number of electrolyzer groups and the separator is adjusted. S52, call the multi-field coupling simulation model of water electrolysis to simulate the updated configuration scheme, and obtain the purity fluctuation curve, output curve and load adaptation curve of each scheme; S53, calculate the comprehensive evaluation index of the scheme according to the simulation curve, sort the schemes according to the index, and retain the top 20% of the high-quality schemes; S54, perform multiple rounds of iterative optimization on the high-quality schemes, dynamically adjust the linkage logic of auxiliary equipment and the branch connection mode, and form the final set of candidate configuration schemes.

6. A water electrolysis system configuration optimization platform based on multi-source data collaborative computing, characterized in that, This platform is applied to the water electrolysis system configuration optimization method based on multi-source data collaborative computing as described in claim 1, comprising: a multi-source data cross-domain acquisition and transmission unit, a water electrolysis multi-field coupling simulation calculation unit, a guiding matrix incremental update processing unit, a load adaptation parameter calculation unit, a configuration scheme intelligent screening and optimization unit, and a configuration result output and execution unit; the multi-source data cross-domain acquisition and transmission unit collects operating parameters of the electrolyzer, gas-liquid separator, and auxiliary equipment through different types of sensors, and transmits them to the water electrolysis multi-field coupling simulation calculation unit after data encoding processing; the water electrolysis multi-field coupling simulation calculation unit receives the data and performs multi-field dynamic... The simulation results are synchronized to the incremental update processing unit of the guiding matrix and the load adaptation parameter calculation unit. The incremental update processing unit of the guiding matrix iteratively adjusts the initial configuration matrix, and the load adaptation parameter calculation unit calculates the adaptation coefficients under different operating conditions. The results of both are input into the intelligent screening and optimization unit of the configuration scheme. The intelligent screening and optimization unit of the configuration scheme optimizes the configuration scheme by combining a multi-round screening model and transmits the optimal configuration result to the configuration result output and execution unit. The configuration result output and execution unit converts the configuration parameters into control commands and sends them to the control module of the water electrolysis system to implement the configuration scheme.

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