Electrocatalytic hydrogen evolution synthesis parameter optimization system for organic framework material

Abstract

The invention relates to the technical field of electro-catalytic hydrogen evolution, and discloses an organic framework material electro-catalytic hydrogen evolution synthesis parameter optimization system. The system comprises a parameter acquisition module, an optimization processing module, an execution control module, a state monitoring module and a collaborative optimization module. The parameter acquisition module acquires hydrogen evolution synthesis original parameters through the multi-category sensing device array and transmits the hydrogen evolution synthesis original parameters to the optimization processing module; the optimization processing module performs data preprocessing and optimization operation on the original parameters to generate regulation and control parameters; the execution control module drives the synthesis equipment to operate according to the regulation and control parameters and feeds back control data; the state monitoring module combines the regulation and control parameters and the execution data, and adopts a historical information comparison method to generate state monitoring data; and the collaborative optimization module dynamically adjusts parameters of each module according to the monitoring data to form a closed-loop optimization system. According to the system, intelligent parameter optimization and cooperative control of the whole electrocatalytic hydrogen evolution synthesis process of the organic framework material are realized, and the synthesis efficiency and the quality stability are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrocatalytic hydrogen evolution, and in particular to a synthesis parameter optimization system for electrocatalytic hydrogen evolution of an organic framework material. Background Art

[0002] Electrocatalytic hydrogen evolution (EHE) is an important method for hydrogen production and holds broad application prospects in the field of new energy. Organic frameworks (OFRs) exhibit significant potential for EHE due to their unique structures and properties. However, optimizing and controlling the parameters during the synthesis of EHE using OFRs presents numerous challenges.

[0003] Traditional methods for optimizing synthesis parameters have obvious shortcomings. The means of parameter collection are relatively simple, making it difficult to fully obtain the original parameters of various aspects such as the real-time electrode potential, reaction environment temperature, solution component concentration, and catalytic site location during the electrocatalytic hydrogen evolution synthesis process. This results in incomplete and inaccurate data, and an inability to provide a sufficient basis for parameter optimization. There is a lack of efficient methods for processing the original parameters, and it is impossible to eliminate outliers, correct errors, and extract key features in a timely manner, resulting in low accuracy and efficiency in parameter optimization. In the execution control link, traditional methods have difficulty in achieving independent regulation of each catalytic site and precise adjustment of medium distribution, and are unable to meet the synthesis requirements of different catalytic sites. This leads to easy mutual interference between the sites during the synthesis process, affecting the synthesis effect.

[0004] In terms of state monitoring, the lack of effective monitoring methods based on historical information comparison prevents timely detection of abnormal conditions during the synthesis process, making it difficult to take timely adjustment measures, thereby affecting the stability of the synthesis and the performance of the material. Furthermore, the lack of an effective collaborative optimization mechanism between modules prevents the formation of a closed-loop parameter optimization system, making it difficult to achieve continuous optimization of the synthesis process. This makes it difficult to effectively improve the efficiency and quality of electrocatalytic hydrogen evolution synthesis using organic framework materials, limiting its application in actual production. Summary of the Invention

[0005] The object of the present invention is to provide a system for optimizing synthesis parameters of electrocatalytic hydrogen evolution of organic framework materials to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides a parameter optimization system for electrocatalytic hydrogen evolution synthesis of organic framework materials, the system comprising: a parameter acquisition module, an optimization processing module, an execution control module, a state monitoring module and a collaborative optimization module;

[0007] The parameter acquisition module is used to obtain a basic information set related to parameters in the electrocatalytic hydrogen evolution synthesis process, specifically by arranging a multi-category sensor array to collect information, obtain the original parameters of the organic framework material hydrogen evolution synthesis, and transmit the original parameters of the organic framework material hydrogen evolution synthesis to the optimization processing module;

[0008] The optimization processing module is used to calculate the original parameters and generate optimized control parameters, specifically to perform data preprocessing and parameter optimization operations on the original parameters of the hydrogen evolution synthesis of the organic framework material in sequence to obtain the synthesis optimized control parameters, and send the synthesis optimized control parameters to the execution control module and the state monitoring module;

[0009] The execution control module is used to perform the synthesis operation according to the control parameters, specifically to control the operation of the electrocatalytic hydrogen evolution synthesis execution equipment based on the synthesis optimization control parameters, complete the parameter control action during the material synthesis process, and feed back the execution control data to the status monitoring module;

[0010] The state monitoring module is used to analyze the operating state of the synthesis process, specifically by combining the synthesis optimization control parameters and the execution control data, using a state monitoring method based on historical information comparison to obtain hydrogen evolution synthesis state monitoring data, and transmitting the hydrogen evolution synthesis state monitoring data to the collaborative optimization module;

[0011] The collaborative optimization module is used to coordinate the operation of each module and optimize the synthesis process. Specifically, it adjusts the operating parameters of each module according to the hydrogen evolution synthesis status monitoring data to achieve collaborative optimization of the electrocatalytic hydrogen evolution synthesis process and form a closed-loop parameter optimization system.

[0012] Preferably, in the parameter acquisition module, the original parameters of the hydrogen evolution synthesis of the organic framework material specifically include real-time electrode potential parameters, reaction environment temperature parameters, solution component concentration parameters, catalytic site position parameters and historical synthesis record parameters; the historical synthesis record parameters specifically include historical reaction time record parameters, historical concentration adjustment record parameters and historical synthesis effect feedback parameters.

[0013] Preferably, in the optimization processing module, the step of sequentially performing data preprocessing and parameter optimization operations on the original parameters of the hydrogen evolution synthesis of the organic framework material to obtain the synthesis optimization control parameters includes data screening processing, feature extraction processing, parameter calculation operation and control parameter generation;

[0014] The data screening process is used to eliminate abnormal values ​​in the original parameters and correct errors, specifically to screen and correct the original parameters of the hydrogen evolution synthesis of the organic framework material by setting the parameter threshold range;

[0015] The feature extraction process specifically extracts key features related to synthesis efficiency from the screened data, including potential fluctuation features, temperature change features, and concentration stability features, and performs correlation calculation on the key features;

[0016] The parameter calculation operation is specifically to determine the optimal combination value of solution concentration, reaction time and ambient temperature by using a parameter combination optimization method based on the associated characteristic data;

[0017] The control parameter generation is specifically to convert the optimal combination value into an executable synthetic control parameter to form the synthetic optimized control parameter.

[0018] Preferably, in the execution control module, the operation of the electrocatalytic hydrogen evolution synthesis execution equipment is regulated based on the synthesis optimization control parameters to complete the parameter control actions in the material synthesis process, specifically including site-independent control, medium distribution adjustment and control action synchronization;

[0019] The site-independent regulation is specifically to set the solution concentration and reaction time of the corresponding site according to the synthesis requirements of each catalytic site and the synthesis optimization control parameters;

[0020] The medium distribution regulation is specifically to adjust the distribution ratio of the solution medium of each catalytic site through the concentration regulating valve group to ensure that the synthesis requirements of different sites are met;

[0021] The control action synchronization is specifically to coordinate the start and stop time of each catalytic site execution device through a time synchronization controller to avoid mutual interference during the synthesis process.

[0022] Preferably, in the state monitoring module, the step of obtaining hydrogen evolution synthesis state monitoring data by combining the synthesis optimization control parameters and the execution control data and adopting a state monitoring method based on historical information comparison includes historical information matching, real-time data comparison, abnormal state identification and monitoring result generation;

[0023] The historical information matching is specifically to extract historical synthesis data that is the same or similar to the current synthesis process from the historical synthesis record parameters as a reference benchmark;

[0024] The real-time data comparison is specifically to compare the execution control data with the reference benchmark item by item, and calculate the deviation values ​​of potential, temperature and concentration;

[0025] The abnormal state determination is specifically to determine whether there is an abnormality in the current synthesis process by setting a deviation threshold range, and if the deviation value exceeds the threshold, it is marked as an abnormal state;

[0026] The monitoring result generation is specifically to integrate the control result and the abnormal state information to form the hydrogen evolution synthesis state monitoring data.

[0027] Preferably, in the collaborative optimization module, the operating parameters of each module are adjusted according to the hydrogen evolution synthesis state monitoring data to achieve collaborative optimization of the electrocatalytic hydrogen evolution synthesis process, which specifically includes data reception and processing, optimization strategy generation, instruction issuance and execution, and feedback adjustment and optimization;

[0028] The data receiving and processing is specifically receiving the hydrogen evolution synthesis state monitoring data and analyzing the abnormal information and comparison results therein;

[0029] The optimization strategy generation is specifically to generate an adjustment strategy for abnormal sites or parameters based on abnormal information and control results, including a concentration correction strategy, a time extension strategy, or a temperature adjustment strategy;

[0030] The instructions are sent for execution, specifically converting the adjustment strategy into a control instruction and sending it to the corresponding module, wherein the optimization processing module receives the parameter correction instruction, and the execution control module receives the action adjustment instruction;

[0031] The feedback adjustment optimization specifically involves collecting adjusted execution control data and status monitoring data, verifying the adjustment effect and further optimizing the optimization strategy to form a continuously optimized collaborative optimization process.

[0032] Preferably, the multi-category sensor array arranged in the parameter acquisition module specifically includes an electrochemical potential sensor for collecting potential parameters, a semiconductor temperature sensor for collecting temperature parameters, an optical concentration sensor for collecting concentration parameters, and a laser positioning sensor for collecting position parameters; each sensor collects information at a preset interval, and the preset interval includes collecting data once every 3 seconds, collecting data once every 6 seconds, and collecting data once every 12 seconds, and the original parameters of the hydrogen evolution synthesis of the organic framework material are obtained by collecting data at different intervals.

[0033] Preferably, the data screening processing implemented in the optimization processing module specifically includes noise suppression processing and missing value supplementation processing; the noise suppression processing specifically adopts a moving average processing method to smooth the high-frequency noise data; the missing value supplementation processing specifically uses a polynomial interpolation method to supplement the missing sensor data within the collection interval to ensure the integrity and continuity of the original parameters of the hydrogen evolution synthesis of the organic framework material.

[0034] Preferably, the hydrogen evolution synthesis state monitoring data obtained in the state monitoring module specifically includes an abnormality probability parameter, a state category parameter and a monitoring index parameter; the abnormality probability parameter is used to represent the possibility of an abnormality occurring in the current synthesis process, and the value range is 0 to 100%; the state category parameter is used to represent the identifiable synthesis state category, specifically including efficient synthesis state, normal synthesis state, inefficient synthesis state and abnormal synthesis state; the monitoring index parameter is used to provide a basis for state identification, specifically including a potential deviation recording parameter, a temperature deviation recording parameter and a concentration deviation recording parameter.

[0035] Preferably, the optimization strategy generated in the collaborative optimization module specifically includes a concentration enhancement strategy for an inefficient synthesis state, an emergency interruption strategy for an abnormal synthesis state, and a parameter fine-tuning strategy for a normal synthesis state.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system provided by this patent arranges a multi-category sensor array through a parameter acquisition module, which can comprehensively obtain the original parameters such as real-time electrode potential parameters, reaction environment temperature parameters, solution component concentration parameters, catalytic site position parameters and historical synthesis record parameters during the electrocatalytic hydrogen evolution synthesis process, providing a rich and accurate data basis for subsequent parameter optimization.

[0038] The optimization processing module sequentially performs data preprocessing and parameter optimization on the original parameters. It uses data screening to eliminate outliers and correct errors. It uses feature extraction to extract key features related to synthesis efficiency and perform correlation calculations. It uses parameter combination optimization methods to determine the optimal combination of solution concentration, reaction time, and ambient temperature, generating executable synthesis optimization control parameters. This improves the accuracy and efficiency of parameter optimization, thereby enhancing synthesis efficiency and material performance. The execution control module, based on the synthesis optimization control parameters, achieves site-independent control, media distribution adjustment, and synchronized control actions. It sets the solution concentration and reaction time for each catalytic site according to its synthesis requirements, adjusts the distribution ratio of the solution medium to each catalytic site through a concentration control valve group, and coordinates the start and stop times of the execution equipment for each catalytic site using a time synchronization controller. This avoids mutual interference during the synthesis process, achieves precise parameter control, and ensures the stability and consistency of the synthesis process.

[0039] The state monitoring module combines synthesis optimization control parameters and execution control data, and adopts a state monitoring method based on historical information comparison. Through historical information matching, real-time data comparison, abnormal state identification, and monitoring result generation, it can promptly detect abnormal conditions during the synthesis process, providing an accurate basis for collaborative optimization. The collaborative optimization module adjusts the operating parameters of each module based on the hydrogen evolution synthesis state monitoring data, generates optimization strategies for different states and issues them for execution, and collects feedback data to verify the adjustment effects, forming a continuously optimized collaborative optimization process. This realizes the collaborative optimization of the electrocatalytic hydrogen evolution synthesis process and forms a closed-loop parameter optimization system. This further improves the synthesis efficiency and material performance, reduces the synthesis cost, enhances the adaptability and reliability of the system, and makes the electrocatalytic hydrogen evolution synthesis process of organic framework materials more efficient, stable, and controllable. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a working principle diagram of the organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system of the present invention;

[0041] Figure 2 Flowchart for optimizing module parameter processing;

[0042] Figure 3 Flowchart of collaborative optimization for collaborative optimization module;

[0043] Figure 4 Flowchart for working with a multi-class sensor array. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] See also Figure 1-Figure 4 The present invention provides a parameter optimization system for electrocatalytic hydrogen evolution synthesis using organic framework materials. The system includes: a parameter acquisition module, an optimization processing module, an execution control module, a state monitoring module, and a collaborative optimization module. The specific implementation steps are as follows:

[0046] The parameter acquisition module collects information by arranging an array of multiple categories of sensor devices, obtains the original parameters of hydrogen evolution synthesis of organic framework materials, and transmits the original parameters to the optimization processing module.

[0047] The optimization processing module performs data preprocessing and parameter optimization operations on the original parameters in turn to obtain synthetic optimized control parameters, and then sends the control parameters to the execution control module and the status monitoring module.

[0048] The execution control module controls the operation of the electrocatalytic hydrogen evolution synthesis execution equipment based on the synthesis optimization control parameters, completes the parameter control actions in the material synthesis process, and feeds back the execution control data to the status monitoring module.

[0049] The state monitoring module combines the synthesis optimization control parameters and execution control data, adopts a state monitoring method based on historical information comparison, obtains hydrogen evolution synthesis state monitoring data, and then transmits the monitoring data to the collaborative optimization module.

[0050] The collaborative optimization module adjusts the working parameters of each module according to the hydrogen evolution synthesis status monitoring data, realizes the collaborative optimization of the electrocatalytic hydrogen evolution synthesis process, and forms a closed-loop parameter optimization system.

[0051] Example 1:

[0052] In the parameter acquisition module, the original parameters of the hydrogen evolution synthesis of organic framework materials specifically cover multiple aspects. These include the real-time electrode potential parameter, which reflects the real-time potential of the electrode during the electrocatalytic hydrogen evolution synthesis process; the reaction environment temperature parameter, which is used to record the temperature conditions of the synthesis reaction environment; the solution component concentration parameter, which reflects the concentration information of each component in the solution; the catalytic site position parameter, which specifies the specific location of the catalytic site in the synthesis system; and the historical synthesis record parameter, which includes the historical reaction duration record parameter, that is, the time record of previous synthesis reactions; the historical concentration adjustment record parameter, which is the relevant record of past adjustments to the solution concentration; and the historical synthesis effect feedback parameter, which provides feedback information on previous synthesis effects.

[0053] The parameter acquisition module is equipped with an array of multiple sensor devices, each of which performs different acquisition tasks. Among them, the electrochemical potential sensor is used to collect potential parameters, accurately capturing changes in electrode potential; the semiconductor temperature sensor is responsible for collecting temperature parameters, monitoring the temperature of the reaction environment in real time; the optical concentration sensor is used to collect concentration parameters, accurately obtaining concentration information of solution components; and the laser positioning sensor is used to collect position parameters to determine the location of catalytic sites. Each sensor collects information at preset intervals, including data collection every 3 seconds, every 6 seconds, and every 12 seconds. By setting different collection intervals, the original parameters of the hydrogen evolution synthesis of organic framework materials can be obtained from different time dimensions, making the collected data more comprehensive and rich, and able to more carefully reflect the various changes during the synthesis process.

[0054] In the optimization processing module, the collected original parameters need to be screened and processed, which specifically includes noise suppression processing and missing value supplementation processing. The noise suppression processing uses a moving average processing method to smooth the high-frequency noise data. The moving average processing method can effectively reduce the interference of high-frequency noise on the data by averaging the data within a certain period of time, making the data smoother and more stable, and more truly reflecting the changing trend of the parameters. The missing value supplementation processing uses the polynomial interpolation method to supplement the missing sensor data within the collection interval. During the data acquisition process, data may be missing due to various reasons. The polynomial interpolation method can fit the values ​​of the missing data points through mathematical operations based on the existing data points, thereby ensuring the integrity and continuity of the original parameters of the hydrogen evolution synthesis of organic framework materials. Complete and continuous original parameters are crucial for subsequent parameter optimization operations. Only based on such data can accurate analysis and calculation be carried out to obtain reliable synthesis optimization control parameters.

[0055] The parameter acquisition module, deploying multiple types of sensors, collects data at preset intervals, acquiring raw parameters including real-time electrode potential, reaction environment temperature, solution component concentrations, catalytic site locations, and historical synthesis records. The optimization processing module performs data filtering and processing on these raw parameters, including noise suppression and missing value interpolation, to ensure their quality. These processing steps work together to provide accurate, complete, and continuous raw data for the entire electrocatalytic hydrogen evolution synthesis parameter optimization system. This enables subsequent modules, such as optimization processing, execution control, status monitoring, and collaborative optimization, to operate based on reliable data, ensuring proper system operation and optimized synthesis parameters. The parameter acquisition and optimization modules play a crucial role in the entire system; their effective operation is fundamental to achieving optimal electrocatalytic hydrogen evolution synthesis parameters. The acquisition and processing of raw parameters provides essential data support for the subsequent modules, enabling the system to comprehensively monitor and optimize the electrocatalytic hydrogen evolution synthesis process, thereby improving synthesis efficiency and effectiveness. The use of different sensor types and varying acquisition intervals during parameter acquisition allows the collected data to reflect the synthesis process from multiple perspectives and timescales, providing rich information for subsequent analysis and optimization. The data screening process in the optimization processing module further improves the quality of the data, reduces the impact of noise and missing values ​​in the data on system analysis and decision-making, and ensures that the system can make correct judgments and adjustments based on accurate data.

[0056] Example 2:

[0057] When the optimization processing module processes the original parameters of the hydrogen evolution synthesis of organic framework materials to generate the optimized control parameters for synthesis, it needs to complete data preprocessing and parameter optimization operations in sequence, specifically covering the steps of data screening processing, feature extraction processing, parameter calculation operations and control parameter generation. The core of the data screening processing step is to screen and correct the original parameters by setting the parameter threshold range, so as to eliminate outliers and correct errors. In the process of electrocatalytic hydrogen evolution synthesis, since the sensor may be interfered with by the external environment when collecting data, or there are certain errors in the equipment itself, some outliers that deviate from the normal range will inevitably appear in the original parameters. If these outliers are not processed, they will have an adverse effect on subsequent analysis and calculations. By setting a reasonable parameter threshold range, data that is obviously beyond the normal range can be identified and eliminated, and some data with errors can be corrected to make the processed data more accurate and reliable.

[0058] After data screening is complete, the feature extraction process begins. This step requires extracting key features related to synthesis efficiency from the screened data, specifically potential fluctuation features, temperature change features, and concentration stability features, and performing correlation calculations on these key features. The potential fluctuation feature reflects the changes in electrode potential during the synthesis process, and the stability of the potential directly affects the reaction rate of electrocatalytic hydrogen evolution. The temperature change feature reflects the dynamic changes in the reaction environment temperature. As an important factor affecting chemical reactions, the law of temperature change is closely related to synthesis efficiency. The concentration stability feature demonstrates the stability of the concentration of solution components. The appropriate concentration is key to ensuring the smooth progress of the reaction. By performing correlation calculations on these features, the inherent connections between them can be discovered, providing a more comprehensive basis for subsequent parameter optimization.

[0059] Next comes the parameter calculation phase, which uses parameter combination optimization methods based on the correlated characteristic data to determine the optimal combination of solution concentration, reaction time, and ambient temperature. Parameter combination optimization methods are based on a wealth of historical data and experimental experience. Using mathematical models and algorithms, they comprehensively analyze and optimize multiple parameters to find the parameter combination that optimizes synthesis efficiency. When determining the optimal combination, it's important to consider the mutual influence and constraints between the various parameters. Instead of optimizing a single parameter, the overall approach must be considered to find the optimal combination of all parameters.

[0060] Finally, the control parameter generation step involves converting the optimal combination values ​​obtained through parameter calculation into executable synthetic control parameters, thereby forming synthetic optimized control parameters. Since the optimal combination values ​​are usually theoretical values, they need to be converted into specific control parameters that can be recognized and executed by the execution control module. For example, the optimal solution concentration can be converted into a specific control parameter for the concentration control valve, and the optimal reaction time can be converted into a time control parameter for the equipment operation. Only in this way can the optimized parameters be truly applied to the electrocatalytic hydrogen evolution synthesis process.

[0061] In the state monitoring module, the hydrogen evolution synthesis state monitoring data obtained specifically includes an abnormality probability parameter, a state category parameter, and monitoring indicator parameters. The abnormality probability parameter represents the probability of an abnormality occurring during the current synthesis process, ranging from 0 to 100%. This parameter is derived through comparative analysis of real-time and historical data and a comprehensive evaluation of various parameters. It provides operators with reference information on whether the current synthesis process is likely to experience an abnormality. The state category parameter represents identifiable synthesis state categories, specifically including efficient synthesis state, normal synthesis state, inefficient synthesis state, and abnormal synthesis state. By analyzing and determining various parameters, the synthesis process is divided into different state categories, allowing operators to quickly understand the current operation status of the synthesis process. Monitoring indicator parameters provide a basis for state identification and include potential deviation recording parameters, temperature deviation recording parameters, and concentration deviation recording parameters. These deviation recording parameters are calculated by comparing real-time potential, temperature, and concentration data with reference baselines. They accurately reflect the degree of deviation of each parameter from the ideal state, providing specific quantitative indicators for state classification.

[0062] The optimization module generates optimized synthesis control parameters through a series of processing and analysis of the raw parameters. This provides the execution control module with an accurate control basis, enabling the execution equipment to operate according to the optimized parameters, thereby improving the efficiency and quality of electrocatalytic hydrogen evolution synthesis. The status monitoring data from the status monitoring module provides comprehensive status information to the collaborative optimization module, enabling it to timely adjust the operating parameters of each module based on the actual state of the synthesis process, achieving coordinated optimization of the entire synthesis process. The optimization module and the status monitoring module collaborate and cooperate in the entire electrocatalytic hydrogen evolution synthesis parameter optimization system, jointly ensuring stable system operation and continuous optimization of synthesis parameters. The quality of the optimization module's processing of the raw parameters directly affects the accuracy of the optimized synthesis control parameters, while the status monitoring data provided by the status monitoring module provides a reference for further parameter optimization in the optimization module. The two form a virtuous cycle that drives the continuous improvement and development of the system. In practical applications, the optimization module needs to flexibly adjust the parameter combination optimization method and threshold range settings according to different synthesis processes and raw material characteristics to ensure that the generated optimized synthesis control parameters are targeted and effective. The condition monitoring module also needs to continuously update and improve the reference benchmark and condition identification standards to adapt to different synthesis conditions and requirements and improve the accuracy and reliability of condition monitoring.

[0063] Example 3:

[0064] When the execution control module controls the operation of the electrocatalytic hydrogen evolution synthesis execution equipment based on the synthesis optimization control parameters, it needs to complete the parameter control actions in the material synthesis process, specifically including three aspects: site-independent control, medium distribution adjustment, and control action synchronization. Site-independent control requires setting the solution concentration and reaction time of the corresponding site according to the synthesis requirements of each catalytic site and the synthesis optimization control parameters. In the electrocatalytic hydrogen evolution synthesis system, the catalytic activity of different catalytic sites may vary, and their requirements for solution concentration and reaction time are also different. For example, some catalytic sites may require a higher concentration of solution to promote the reaction, while other sites may achieve better reaction effects at lower concentrations; similarly, the reaction time required for different sites may also be different. Some sites have a faster reaction rate and require a shorter time, while others require a longer time to complete the reaction. By independently controlling each catalytic site, the personalized synthesis needs of different sites can be met, making the entire synthesis process more accurate and efficient.

[0065] Media distribution regulation requires adjusting the distribution ratio of the solution medium to each catalytic site through a concentration control valve group to ensure that the synthesis requirements of different sites are met. The distribution ratio of the solution medium to each catalytic site directly affects the reaction conditions at each site. The concentration control valve group consists of multiple control valves, each corresponding to a catalytic site. By controlling the opening of each control valve, the flow rate of the solution medium to each catalytic site can be precisely adjusted, thereby adjusting the distribution ratio. For example, when a catalytic site requires a higher concentration of solution, the opening of the corresponding control valve can be increased to increase the flow rate of the solution medium to that site, thereby increasing its solution concentration. Conversely, when the solution concentration of a site needs to be reduced, the opening of the corresponding control valve can be reduced. This precise media distribution regulation enables each catalytic site to react under the most suitable solution medium conditions, which is beneficial for improving synthesis efficiency and the quality of the synthesized product.

[0066] Synchronizing control actions requires coordinating the start and stop times of the actuators at each catalytic site through a time synchronization controller to avoid mutual interference during the synthesis process. During the electrocatalytic hydrogen evolution synthesis process, inconsistent start and stop times of the actuators at each catalytic site can lead to asynchronous reaction processes, resulting in mutual interference and affecting the synthesis effect. The time synchronization controller can provide a unified time reference for each actuator, ensuring that they start and stop according to a predetermined time sequence. For example, at the beginning of the synthesis, the time synchronization controller sends a start signal, and the actuators at each catalytic site start simultaneously to begin the reaction. After the reaction is complete, the time synchronization controller sends a stop signal, and each device stops operating simultaneously. This precise time synchronization control can ensure that the reaction processes at each catalytic site remain consistent, avoiding interference caused by time differences and ensuring the stability and reliability of the synthesis process.

[0067] When the state monitoring module combines synthesis optimization control parameters and execution control data and adopts a state monitoring method based on historical information comparison to obtain hydrogen evolution synthesis state monitoring data, it must go through the steps of historical information matching, real-time data comparison, abnormal state identification, and monitoring result generation. Historical information matching requires extracting historical synthesis data that is identical or similar to the current synthesis process from the historical synthesis record parameters as a reference benchmark. The historical synthesis record parameters contain a large amount of relevant data from previous synthesis processes. By analyzing the parameters of the current synthesis process, the same or similar historical synthesis data can be found. This data can reflect the normal operating status and expected results of the synthesis process under similar process conditions, thereby providing a reference for monitoring the current synthesis process.

[0068] Real-time data comparison requires comparing the execution control data with the reference benchmark item by item and calculating the deviation values ​​of potential, temperature and concentration. The execution control data is the parameter data such as potential, temperature, concentration, etc. collected in real time during the current synthesis process. By comparing it with the corresponding parameters in the reference benchmark item by item, the difference between the current parameters and the historical normal parameters can be intuitively seen. By calculating the deviation value, that is, the difference between the current parameter value and the reference benchmark value, the degree of this difference can be quantified, providing a specific numerical basis for subsequent abnormal state judgment. The calculation formula of the deviation value is: ,in Indicates the deviation value, Indicates the currently collected parameter value. Indicates the parameter value in the reference standard. For example, for the potential parameter, the calculated deviation value ,in is the current potential value, is the reference potential value; temperature deviation value , concentration deviation value , respectively, represent the differences between the current temperature and concentration and the reference benchmark.

[0069] Abnormal state judgment requires setting a deviation threshold range to determine whether the current synthesis process is abnormal. If the deviation value exceeds the threshold, it is marked as an abnormal state. According to historical synthesis data and process requirements, a reasonable threshold range is set for the deviation values ​​of parameters such as potential, temperature and concentration. When the calculated deviation value is within the threshold range, it means that the current synthesis process is in a normal state; when the deviation value exceeds the threshold range, it indicates that the synthesis process may have an abnormality and needs to be handled in time. For example, the threshold range of the potential deviation is set to , if the calculated potential deviation value Greater than or less than , it is marked as abnormal potential state.

[0070] Generating monitoring results requires integrating control results and abnormal state information to form hydrogen evolution synthesis state monitoring data. Parameter deviations obtained from real-time data comparisons and abnormality flag information from abnormal state identification are collated and summarized to form comprehensive state monitoring data. This data clearly reflects the operating status of each parameter in the current synthesis process and whether any abnormalities exist, providing accurate information support for subsequent collaborative optimization modules, enabling them to adjust the operating parameters of each module based on the state monitoring data and optimize the synthesis process.

[0071] The execution control module precisely executes optimized synthesis parameters through three key operations: site-independent control, media distribution adjustment, and control action synchronization, ensuring that the electrocatalytic hydrogen evolution synthesis process proceeds according to predetermined parameters. The state monitoring module comprehensively monitors the synthesis process using a state monitoring method based on historical information comparison, promptly identifying anomalies and generating state monitoring data. The execution control module and the state monitoring module work together throughout the entire system. Precise execution of the execution control module is essential for ensuring the smooth progress of the synthesis process, while real-time monitoring by the state monitoring module provides a basis for adjustment and optimization of the execution control module. Together, they ensure stable operation of the electrocatalytic hydrogen evolution synthesis parameter optimization system and enhance synthesis performance. In practical applications, the execution control module needs to flexibly adjust the site-independent control parameter settings, the media distribution adjustment ratio, and the timing of control action synchronization based on different synthesis processes and catalytic site characteristics to meet diverse synthesis requirements. The state monitoring module also needs to continuously accumulate historical synthesis data to optimize the deviation threshold range and improve the accuracy and timeliness of abnormal state detection, providing more reliable support for system optimization.

[0072] Example 4:

[0073] When the collaborative optimization module adjusts the working parameters of each module according to the hydrogen evolution synthesis state monitoring data to achieve collaborative optimization of the electrocatalytic hydrogen evolution synthesis process, it needs to complete a series of operations in sequence, including data reception and processing, optimization strategy generation, instruction issuance and execution, and feedback adjustment and optimization. The core of the data reception and processing link is to receive the hydrogen evolution synthesis state monitoring data and parse the abnormal information and control results therein. The hydrogen evolution synthesis state monitoring data contains rich information such as abnormal probability parameters, state category parameters, and monitoring index parameters. For example, when the state monitoring data shows that the current synthesis state is an inefficient synthesis state, and the concentration deviation record parameter in the monitoring index parameter indicates that the solution concentration is lower than the reference benchmark value, the data reception and processing link needs to accurately identify and extract these key information to provide a clear basis for the subsequent optimization strategy generation.

[0074] The optimization strategy generation process needs to generate corresponding adjustment strategies for abnormal sites or parameters based on the abnormal information obtained through analysis and the control results. Specifically, these strategies include concentration correction strategies, time extension strategies, or temperature regulation strategies. For example, if the state monitoring data shows that a catalytic site is in an inefficient synthesis state, and analysis finds that the reaction rate is slow due to insufficient solution concentration at the site, then the generated optimization strategy may be a concentration increase strategy, that is, appropriately increasing the solution concentration at the site. If it is found that the reaction time at a certain site is insufficient, resulting in incomplete reaction and thus an inefficient synthesis state, then a time extension strategy may be generated to extend the reaction time of the site. When the temperature deviation is large, affecting the activity of the catalyst and resulting in low synthesis efficiency, a temperature regulation strategy will be generated to adjust the reaction environment temperature of the site. In addition, the generated optimization strategies also include specific strategies for different synthesis states, such as a concentration increase strategy for inefficient synthesis states, an emergency interruption strategy for abnormal synthesis states, and a parameter fine-tuning strategy for normal synthesis states. For example, under normal synthesis conditions, although all parameters are basically normal, in order to further optimize the synthesis effect, some parameters may be slightly adjusted, such as slightly increasing the temperature or fine-tuning the solution concentration, to seek better synthesis conditions.

[0075] The instruction issuance and execution link needs to convert the generated adjustment strategy into a control instruction and send it to the corresponding module, where the optimization processing module receives the parameter correction instruction and the execution control module receives the action adjustment instruction. For example, when the concentration increase strategy for a certain site is generated, the strategy needs to be converted into a specific control instruction, such as controlling the concentration regulating valve group to increase the solution medium flow rate of the site. At this time, the execution control module receives the action adjustment instruction and controls the corresponding concentration regulating valve to perform the operation; if the temperature adjustment strategy is generated, the reaction environment temperature of a certain site needs to be adjusted. It is also converted into an instruction that can be executed by the execution control module to control the heating or cooling equipment to adjust the temperature. When the optimization processing module needs to correct the parameters according to the adjustment strategy, such as recalculating the optimal reaction time of a certain site, it will receive the parameter correction instruction from the collaborative optimization module, re-process the data and calculate the parameters, and generate new synthetic optimization control parameters.

[0076] The primary task of the feedback adjustment optimization phase is to collect adjusted execution control data and status monitoring data, verify the adjustment results, and further refine the optimization strategy, forming a continuous collaborative optimization process. For example, after executing a concentration increase strategy at a specific site, it is necessary to collect the adjusted solution concentration data (execution control data) and new status monitoring data for that site to verify whether the synthesis state at that site has transitioned from an inefficient synthesis state to a normal synthesis state and whether the concentration deviation has decreased. If the adjustment effect is unsatisfactory, that is, the synthesis efficiency still does not meet the expected level after the concentration increase, the cause needs to be analyzed. It may be that the concentration increase was insufficient or there are other influencing factors. The optimization strategy can then be adjusted, such as further increasing the concentration or combining it with other optimization strategies. If the adjustment effect is positive and the synthesis state improves, the optimization strategy can be used as a reference for application in similar situations. Through this feedback adjustment process, the strategy generation and execution of the collaborative optimization module are continuously optimized, allowing the entire system to better adapt to different synthesis situations and achieve continuous optimization of the synthesis process.

[0077] In a practical application scenario, assume that during an electrocatalytic hydrogen evolution synthesis process, the state monitoring module generates state monitoring data indicating that the abnormality probability parameter for catalytic site A is 75%, the state category parameter is abnormal synthesis, and the potential deviation recording parameter in the monitoring indicator parameters is +0.15V, exceeding the preset threshold range of ±0.1V. After receiving this state monitoring data, the collaborative optimization module analyzes the data reception and processing phase and identifies the potential anomaly at site A. The optimization strategy generation phase generates an emergency interruption strategy to address this abnormality. Because large potential deviations can cause electrode damage or other serious problems, the reaction at this site must be immediately stopped. The instruction issuance and execution phase converts the emergency interruption strategy into a control instruction and sends it to the execution control module. Upon receiving the instruction, the execution control module immediately stops the execution equipment at site A. Simultaneously, the collaborative optimization module sends a parameter correction instruction to the optimization processing module, requesting it to reanalyze the potential data at site A and identify the cause of the potential anomaly. After the emergency interruption is executed, the feedback adjustment optimization phase collects relevant data from site A after it has ceased operation, as well as the optimization processing module's analysis of the cause of the potential anomaly. Assuming that the analysis finds that the potential anomaly is caused by aging of the electrode material at site A, the collaborative optimization module will consider replacing the electrode material in the subsequent optimization strategy based on the feedback results, adjust the potential parameter settings of the site, and verify the synthesis effect after replacing the electrode material. Further optimize the optimization strategy to ensure that similar abnormal situations do not occur again.

[0078] The collaborative optimization module forms a closed-loop optimization system through the close coordination of four key steps: data reception and processing, optimization strategy generation, command issuance and execution, and feedback adjustment and optimization. Based on real-time status monitoring data generated during the synthesis process, it promptly identifies problems and implements corresponding optimization strategies, adjusting the operating parameters of each module to ensure that the entire electrocatalytic hydrogen evolution synthesis process remains in optimal operation. Data reception and processing are the prerequisites for collaborative optimization. Only by accurately receiving and interpreting status monitoring data can an effective optimization strategy be formulated. Optimization strategy generation is the core, requiring the development of reasonable adjustment plans based on actual conditions. Command issuance and execution are key, ensuring that the optimization strategy is accurately communicated to and implemented by each module. Feedback adjustment and optimization ensure continuous improvement, continuously enhancing system performance through continuous verification and optimization. Throughout the collaborative optimization process, each step is interconnected and mutually influential, jointly driving the efficient operation of the electrocatalytic hydrogen evolution synthesis parameter optimization system and the continuous improvement of synthesis results. In practice, the parameters and strategies of the collaborative optimization module must be flexibly adjusted according to the characteristics of different synthesis processes and equipment to adapt to the complex and changing synthesis environment and ensure that the system can maximize its optimization effect.

[0079] Example 5:

[0080] The parameter acquisition module features a multi-category array of sensors, including electrochemical potential sensors for collecting potential parameters, semiconductor temperature sensors for collecting temperature parameters, optical concentration sensors for collecting concentration parameters, and laser positioning sensors for collecting position parameters. These sensors collect information at preset intervals: every 3 seconds, every 6 seconds, and every 12 seconds. Different collection intervals are tailored to the characteristics of different parameters. For example, if the electrochemical potential changes rapidly, data may need to be collected every 3 seconds to capture its rapid fluctuations; while if the concentration of solution components changes relatively slowly, data collection every 12 seconds can be used. This collection method with varying intervals allows the acquisition of raw parameters for the hydrogen evolution synthesis of organic framework materials over different timescales, enabling the raw parameters to more comprehensively reflect the dynamic changes in the synthesis process.

[0081] The data screening processing implemented in the optimization processing module includes noise suppression processing and missing value supplementation processing. The noise suppression processing uses the moving average processing method to smooth the high-frequency noise data. During the data acquisition process, the sensor may be affected by external electromagnetic interference and other factors, generating high-frequency noise data. These noises will interfere with the judgment of the true trend of the original parameters. The moving average processing can effectively reduce the impact of noise. The missing value supplementation processing uses the polynomial interpolation method to supplement the missing sensor data within the collection interval. Due to sensor failure or other reasons, data may be missing. The polynomial interpolation method can infer the value of the missing data point based on the data of adjacent time points, thereby ensuring the integrity and continuity of the original parameters of the hydrogen evolution synthesis of organic framework materials, and providing a reliable data basis for subsequent parameter optimization operations.

[0082] The hydrogen evolution synthesis state monitoring data obtained in the state monitoring module includes abnormality probability parameters, state category parameters and monitoring index parameters. The abnormality probability parameter represents the possibility of abnormality in the current synthesis process with a value from 0 to 100%. This parameter comprehensively considers the deviation of various parameters and historical synthesis data, and provides operators with a quantitative reference for the stability of the synthesis process. The state category parameter divides the synthesis state into efficient synthesis state, normal synthesis state, inefficient synthesis state and abnormal synthesis state. Different state categories correspond to different synthesis efficiency and operating conditions. Operators can quickly understand the general situation of the current synthesis process according to the state category. The monitoring index parameters specifically include potential deviation recording parameters, temperature deviation recording parameters and concentration deviation recording parameters. These parameters are calculated by comparing the real-time collected data with the historical reference benchmark. They can accurately reflect the degree of deviation of each parameter and provide a specific basis for the judgment of the state category.

[0083] The optimization strategies generated by the collaborative optimization module take corresponding adjustment measures for different synthesis states. For inefficient synthesis states, a concentration increase strategy is generated. For example, when the reaction rate of a catalytic site is slow due to insufficient solution concentration, the solution concentration of the site is increased to promote the reaction. For abnormal synthesis states, an emergency interruption strategy is generated. When a serious parameter abnormality is found at a site that may affect equipment safety or the quality of the synthesized product, the reaction at that site is immediately stopped. For normal synthesis states, a parameter fine-tuning strategy is generated. When all parameters are basically normal, parameters such as temperature and concentration are slightly adjusted to seek more optimal synthesis conditions. These optimization strategies can solve problems in a targeted manner according to the actual state of the synthesis process, achieving collaborative optimization of the electrocatalytic hydrogen evolution synthesis process.

[0084] The execution control module precisely executes the synthesis optimization control parameters through site-independent control, medium distribution adjustment, and control action synchronization. Site-independent control sets the solution concentration and reaction time of each catalytic site based on the synthesis requirements and synthesis optimization control parameters of each catalytic site, meeting the personalized needs of different sites. Medium distribution adjustment uses a concentration control valve group to adjust the distribution ratio of the solution medium to each catalytic site, ensuring that different sites react under appropriate solution concentration conditions. Control action synchronization coordinates the start and stop times of the execution equipment of each catalytic site through a time synchronization controller to avoid mutual interference during the synthesis process and ensure the orderly progress of the synthesis process.

[0085] During the electrocatalytic hydrogen evolution synthesis process, each module works in tandem. The parameter acquisition module acquires comprehensive and accurate raw parameters using a multi-category sensor array and varying acquisition intervals. The optimization processing module filters and processes these raw parameters to generate reliable synthesis optimization control parameters. The execution control module precisely controls the execution equipment based on these control parameters. The state monitoring module monitors the synthesis status in real time and generates state monitoring data. The collaborative optimization module adjusts the operating parameters of each module based on this state monitoring data, forming a closed-loop optimization system. For example, if the state monitoring module detects that a catalytic site is inefficient and its solution concentration is below a reference baseline, the collaborative optimization module generates a concentration-enhancing strategy. The instruction distribution execution link converts this strategy into a control command and sends it to the execution control module. The execution control module adjusts the concentration control valve group through medium distribution to increase the solution flow rate at that site and thus improve the solution concentration. Simultaneously, the optimization processing module may recalculate the optimal reaction time for that site based on the new concentration parameters. After the adjustment, the state monitoring module continues to monitor the synthesis status at that site, and the collaborative optimization module collects feedback data to verify the effectiveness of the adjustment. If the results are unsatisfactory, the strategy is further optimized until the synthesis status at that site is improved.

[0086] Through the synergistic effect of various modules, the system optimizes the parameters of electrocatalytic hydrogen evolution synthesis, ensuring that the synthesis process proceeds in an efficient and stable state. The comprehensiveness and accuracy of parameter acquisition provide a basis for subsequent processing, the scientific nature of the optimization process ensures the rationality of the control parameters, the precision of the execution control enables the effective implementation of the control parameters, the real-time status monitoring provides a basis for optimization, and the closed-loop nature of the collaborative optimization enables continuous improvement of the system. In practical applications, the parameter settings and working modes of each module can be flexibly adjusted according to different synthesis processes and raw material characteristics to adapt to different synthesis requirements and improve the efficiency and quality of the electrocatalytic hydrogen evolution synthesis of organic framework materials.

[0087] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0088] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A system for optimizing the synthesis parameters of electrocatalytic hydrogen evolution using organic framework materials, characterized by: It includes parameter acquisition module, optimization processing module, execution control module, status monitoring module and collaborative optimization module; The parameter acquisition module is used to obtain a basic information set related to parameters in the electrocatalytic hydrogen evolution synthesis process, specifically by arranging a multi-category sensor array to collect information, obtain the original parameters of the organic framework material hydrogen evolution synthesis, and transmit the original parameters of the organic framework material hydrogen evolution synthesis to the optimization processing module; The optimization processing module is used to calculate the original parameters and generate optimized control parameters, specifically to perform data preprocessing and parameter optimization operations on the original parameters of the hydrogen evolution synthesis of the organic framework material in sequence to obtain the synthesis optimized control parameters, and send the synthesis optimized control parameters to the execution control module and the state monitoring module; The execution control module is used to perform the synthesis operation according to the control parameters, specifically to control the operation of the electrocatalytic hydrogen evolution synthesis execution equipment based on the synthesis optimization control parameters, complete the parameter control action during the material synthesis process, and feed back the execution control data to the status monitoring module; The state monitoring module is used to analyze the operating state of the synthesis process, specifically by combining the synthesis optimization control parameters and the execution control data, using a state monitoring method based on historical information comparison to obtain hydrogen evolution synthesis state monitoring data, and transmitting the hydrogen evolution synthesis state monitoring data to the collaborative optimization module; The collaborative optimization module is used to coordinate the operation of each module and optimize the synthesis process. Specifically, it adjusts the operating parameters of each module according to the hydrogen evolution synthesis status monitoring data to achieve collaborative optimization of the electrocatalytic hydrogen evolution synthesis process and form a closed-loop parameter optimization system.

2. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 1, characterized in that: In the parameter acquisition module, the original parameters of the hydrogen evolution synthesis of the organic framework material specifically include real-time electrode potential parameters, reaction environment temperature parameters, solution component concentration parameters, catalytic site position parameters and historical synthesis record parameters; the historical synthesis record parameters specifically include historical reaction time record parameters, historical concentration adjustment record parameters and historical synthesis effect feedback parameters.

3. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 2, characterized in that: In the optimization processing module, the step of sequentially performing data preprocessing and parameter optimization operations on the original parameters of hydrogen evolution synthesis of the organic framework material to obtain the synthesis optimization control parameters includes data screening processing, feature extraction processing, parameter calculation operation and control parameter generation; The data screening process is used to eliminate abnormal values ​​in the original parameters and correct errors, specifically to screen and correct the original parameters of the hydrogen evolution synthesis of the organic framework material by setting the parameter threshold range; The feature extraction process specifically extracts key features related to synthesis efficiency from the screened data, including potential fluctuation features, temperature change features, and concentration stability features, and performs correlation calculation on the key features; The parameter calculation operation is specifically to determine the optimal combination value of solution concentration, reaction time and ambient temperature by using a parameter combination optimization method based on the associated characteristic data; The control parameter generation is specifically to convert the optimal combination value into an executable synthetic control parameter to form the synthetic optimized control parameter.

4. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 3, characterized in that: In the execution control module, the operation of the electrocatalytic hydrogen evolution synthesis execution equipment is controlled based on the synthesis optimization control parameters to complete the parameter control actions during the material synthesis process, specifically including site-independent control, medium distribution adjustment and control action synchronization; The site-independent regulation is specifically to set the solution concentration and reaction time of the corresponding site according to the synthesis requirements of each catalytic site and the synthesis optimization control parameters; The medium distribution regulation is specifically to adjust the distribution ratio of the solution medium of each catalytic site through the concentration regulating valve group to ensure that the synthesis requirements of different sites are met; The control action synchronization is specifically to coordinate the start and stop time of each catalytic site execution device through a time synchronization controller to avoid mutual interference during the synthesis process.

5. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 4, characterized in that: In the state monitoring module, the step of combining the synthesis optimization control parameters and the execution control data and adopting a state monitoring method based on historical information comparison to obtain hydrogen evolution synthesis state monitoring data includes historical information matching, real-time data comparison, abnormal state identification and monitoring result generation; The historical information matching is specifically to extract historical synthesis data that is the same or similar to the current synthesis process from the historical synthesis record parameters as a reference benchmark; The real-time data comparison is specifically to compare the execution control data with the reference benchmark item by item, and calculate the deviation values ​​of potential, temperature and concentration; The abnormal state determination is specifically to determine whether there is an abnormality in the current synthesis process by setting a deviation threshold range, and if the deviation value exceeds the threshold, it is marked as an abnormal state; The monitoring result generation is specifically to integrate the control result and the abnormal state information to form the hydrogen evolution synthesis state monitoring data.

6. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 5, characterized in that: In the collaborative optimization module, the operating parameters of each module are adjusted according to the hydrogen evolution synthesis state monitoring data to achieve collaborative optimization of the electrocatalytic hydrogen evolution synthesis process, which specifically includes data reception and processing, optimization strategy generation, instruction issuance and execution, and feedback adjustment and optimization; The data receiving and processing is specifically receiving the hydrogen evolution synthesis state monitoring data and analyzing the abnormal information and comparison results therein; The optimization strategy generation is specifically to generate an adjustment strategy for abnormal sites or parameters based on abnormal information and control results, including a concentration correction strategy, a time extension strategy, or a temperature adjustment strategy; The instructions are sent for execution, specifically converting the adjustment strategy into a control instruction and sending it to the corresponding module, wherein the optimization processing module receives the parameter correction instruction, and the execution control module receives the action adjustment instruction; The feedback adjustment optimization specifically involves collecting adjusted execution control data and status monitoring data, verifying the adjustment effect and further optimizing the optimization strategy to form a continuously optimized collaborative optimization process.

7. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 6, characterized in that: The multi-category sensor array arranged in the parameter acquisition module specifically includes an electrochemical potential sensor for collecting potential parameters, a semiconductor temperature sensor for collecting temperature parameters, an optical concentration sensor for collecting concentration parameters, and a laser positioning sensor for collecting position parameters; each sensor collects information at a preset interval, and the preset interval includes collecting data once every 3 seconds, once every 6 seconds, and once every 12 seconds. The original parameters of the hydrogen evolution synthesis of the organic framework material are obtained by collecting data at different intervals.

8. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 7, characterized in that: The data screening process implemented in the optimization processing module specifically includes noise suppression processing and missing value supplementation processing; the noise suppression process specifically adopts a moving average processing method to smooth the high-frequency noise data; the missing value supplementation process specifically uses a polynomial interpolation method to supplement the missing sensor data within the collection interval to ensure the integrity and continuity of the original parameters of the hydrogen evolution synthesis of the organic framework material.

9. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 8, characterized in that: The hydrogen evolution synthesis state monitoring data obtained in the state monitoring module specifically includes an abnormality probability parameter, a state category parameter and a monitoring index parameter; the abnormality probability parameter is used to represent the probability value of an abnormality occurring in the current synthesis process, and the value range is 0 to 100%; the state category parameter is used to represent the identifiable synthesis state category, specifically including efficient synthesis state, normal synthesis state, inefficient synthesis state and abnormal synthesis state; the monitoring index parameter is used to provide a basis for state identification, specifically including a potential deviation recording parameter, a temperature deviation recording parameter and a concentration deviation recording parameter.

10. The organic framework material electrocatalytic hydrogen evolution synthesis parameter optimization system according to claim 9, characterized in that: The optimization strategies generated in the collaborative optimization module specifically include a concentration enhancement strategy for inefficient synthesis states, an emergency interruption strategy for abnormal synthesis states, and a parameter fine-tuning strategy for normal synthesis states.

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