Green electricity coupling hydrogen energy micro-grid operation optimization method

Abstract

The invention discloses a green electricity coupling hydrogen energy micro-grid operation optimization method, and relates to the technical field of micro-grid energy management and hydrogen energy utilization, and the method comprises the steps: S1, collecting the generated power of renewable energy in real time through a micro-grid monitoring system, predicting the output and load information, and judging whether to trigger the operation of an electrolytic hydrogen production device or not based on the green electricity fluctuation, the hydrogen production process is dynamically coupled with green electricity production capacity, and data such as hydrogen production batch identification and purity are collected in real time from electrolysis equipment through a sensor network; a multi-source information integration technology is adopted, production batch identification and time stamp recording are combined, and a preliminary hydrogen category distribution data set containing purity grade division and the proportion of the number of groups is generated; according to the green electricity coupling hydrogen energy micro-grid operation optimization method, the resource utilization rate, the matching precision and the operation stability of the green electricity-hydrogen energy micro-grid are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid energy management and hydrogen energy utilization, and particularly relates to a green electricity coupled hydrogen energy micro-grid operation optimization method. BACKGROUND

[0002] Under the background of energy transformation and low-carbon development, the micro-grid system combining green electricity and hydrogen energy has become an important technical path to improve energy utilization efficiency, enhance system flexibility and realize deep decarbonization. Renewable energy has the characteristics of strong fluctuation and unstable output. Relying on the analysis ability of industrial big data and the collaborative architecture of distributed control, it can be collaboratively configured with electrolytic hydrogen production systems, hydrogen storage systems and hydrogen-using equipment. A multi-energy complementary mechanism of electricity-hydrogen coupling can be formed in the micro-grid. Through real-time analysis and optimization of massive data such as renewable energy output and electrolytic hydrogen production conditions by industrial data processing technology, the green electricity consumption capacity can be improved while realizing the cross-time and space transfer of energy.

[0003] In existing green electricity hydrogen production and hydrogen energy utilization systems, hydrogen production usually relies on electrolysis equipment, and electrolysis devices produce hydrogen with significantly different purities under different operating conditions. Different batches of hydrogen often have noticeable differences in purity, impurity content, and quality stability. However, existing systems generally lack full-process traceability based on industrial data processing, and fail to systematically record and manage batch information, purity levels, and quality characteristics of hydrogen production. Different purities of hydrogen are often treated as directly usable resources, leading to uncontrollable hydrogen quality and low resource utilization efficiency. On the other hand, hydrogen-using equipment in microgrids has significant differences in purity requirements. For example, fuel cells require high-purity hydrogen to ensure the efficiency of electrochemical reactions, while hydrogen boilers and industrial heating equipment have lower purity requirements. To achieve precise hierarchical supply of hydrogen, it is necessary to integrate equipment demand parameters, hydrogen quality data, and operating condition information on an industrial big data platform, and to build a multi-dimensional matching model based on the node coordination advantages of distributed control. Conversely, if hydrogen cannot be supplied hierarchically according to equipment requirements, high-purity hydrogen may be consumed by low-demand equipment, and low-purity hydrogen may not be matched with suitable equipment, resulting in higher overall operating costs and even waste of hydrogen energy resources. In addition, existing technologies lack intelligent matching mechanisms based on factors such as equipment location, operating demand window, hydrogen consumption rate, and purity quality stability during hydrogen scheduling. Distributed control architecture can break the limitations of traditional centralized scheduling and enable real-time interaction and autonomous decision-making of node data, but existing systems fail to fully utilize its advantages. They only perform simple scheduling based on inventory levels without considering the priority of different equipment and the purity tolerance range, making it impossible to achieve optimal allocation of hydrogen resources. Furthermore, existing technologies lack dynamic verification and feedback mechanisms for hydrogen allocation ratio schemes based on industrial data processing. When low-purity hydrogen inventory deviates, equipment hydrogen consumption rate exceeds expectations, or purity stability decreases, real-time data analysis and mining cannot be used to adjust the scheduling strategy in a timely manner, affecting the overall operating efficiency of the system. At the system level, existing hydrogen microgrid scheduling methods often lack the ability to simulate and evaluate the execution effect of the final scheduling strategy. They fail to build simulation models using massive historical data and real-time monitoring data from industrial big data, and cannot perform parameter backtracking and strategy optimization when the allocation scheme does not meet the expected energy efficiency indicators. The lack of this closed-loop mechanism makes it difficult for the system to continuously improve its operating performance, which is not conducive to maintaining the efficient and stable operation of the microgrid under complex operating conditions. In summary, existing technologies still have obvious shortcomings in hydrogen purity hierarchical management, equipment demand identification, resource optimization allocation, and scheduling strategy closed-loop optimization, making it difficult to meet the demand for efficient and intelligent operation of hydrogen energy resources in microgrids under the background of green electricity hydrogen production. SUMMARY

[0004] The present application aims to provide a green electricity coupled hydrogen energy micro-grid operation optimization method to solve the problems existing in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a green electricity coupled hydrogen energy micro-grid operation optimization method, comprising S1, collecting real-time renewable energy power generation, predicted output and load information through a micro-grid monitoring system, determining whether to trigger electrolytic hydrogen production device operation based on green electricity volatility, dynamically coupling hydrogen production process and green electricity energy production, and collecting hydrogen production batch identification and purity data from electrolytic equipment through a sensor network; using multi-source information integration technology, combining production batch identification and time stamp records to generate preliminary hydrogen category distribution dataset containing purity grade division and group quantity proportion; S2, obtaining storage location information and available inventory based on the preliminary hydrogen category distribution dataset, grouping processing for purity grade division and quality stability, and determining detailed hydrogen category distribution list of low purity group, medium purity group and high purity group; S3, collecting equipment type identification, purity demand threshold and operation time window information through a device management interface, combining historical demand records and priority sorting; if the purity demand threshold is higher than the average quality stability of the high purity group, it is marked as a strict demand category, and an equipment demand classification list containing fault tolerance range value is obtained; S4, using linear programming technology to match and calculate the equipment type identification, hydrogen consumption rate and geographical location distribution in the equipment demand classification list with the storage location, group quantity proportion and stability parameters in the hydrogen category distribution list, and determining the allocation proportion scheme of hydrogen in each purity group.

[0006] Preferably, S1 comprises collecting real-time renewable energy power generation data and load information through a micro-grid monitoring device, combining predicted output data, comparing with a preset volatility threshold, triggering electrolytic hydrogen production device operation instruction if the power generation fluctuation exceeds the preset threshold, and obtaining dynamic coupling operation state data; according to the dynamic coupling operation state data, obtaining hydrogen production batch identification and purity information from the electrolytic hydrogen production device through a sensor network, classifying and storing batch data, and determining a preliminary dataset containing batch identification and purity grade; using a multi-source information integration tool, associating and matching batch identification in the preliminary dataset with time stamp records to generate a hydrogen category distribution dataset containing purity grade division and quantity proportion, and obtaining structured distribution information; using a data processing tool to organize the structured distribution information, counting each purity grade, and marking as an optimization category if the purity grade proportion is lower than a preset threshold to obtain final category distribution optimization data.

[0007] Preferably, S2 comprises extracting storage location information and available inventory quantity from the preliminary hydrogen category distribution dataset, classifying the purity grade data to obtain classified grouping data; marking the location according to the grouping data, using a data matching tool to associate the inventory quantity and purity grade, and determining the detailed distribution information of each group; using a data integration tool to structure the detailed distribution information, and if the inventory of a certain group is lower than a preset threshold, marking it as a priority adjustment group to obtain the marked list data; using a storage tool to archive the low purity group, medium purity group and high purity group according to the list data, judging the quality stability distribution of each group, and obtaining the final distribution list.

[0008] Preferably, S3 comprises obtaining device type identification, purity demand threshold and running time window data from the device management interface, combining the pre-established historical demand record database, determining the priority ranking information of each device through a data comparison tool to obtain a preliminary demand priority list; obtaining the average quality stability data of the high purity group according to the preliminary demand priority list, and if the purity demand threshold of a certain device is higher than the average quality stability, marking it as a strict demand category to obtain a device classification set containing the marked device; using a data integration tool to extract the fault tolerance range value of each device according to the device classification set, combining the running time window information, judging the demand matching degree of the device in a specific time period through a time matching algorithm, and determining the final classification matching result; using a storage tool to archive the strict demand category and other categories according to the classification matching result, and obtaining a device demand classification list containing the fault tolerance range value.

[0009] Preferably, S4 comprises obtaining device type identification, hydrogen consumption rate and geographic location distribution data from the device demand classification list, and extracting storage location information from the hydrogen category distribution list; using a data integration tool to compare the data to obtain a correspondence list of devices and hydrogen categories; using a data filtering tool to extract the association information of device type identification and purity group classification according to the correspondence list, and if the hydrogen consumption rate is higher than a preset threshold, marking it as a high consumption category to determine the high consumption category device list; obtaining the matching information of geographic location distribution and storage location according to the high consumption category device list, processing the data through a location matching algorithm, judging whether it meets the preset matching range, and obtaining a location matching result set; using a data calculation tool to combine the quantity proportion and stability parameter according to the location matching result set, and if the stability parameter is lower than a preset threshold, adjusting the allocation scheme to determine the final hydrogen allocation proportion list of each purity group.

[0010] Preferably, the method further includes S5: determining whether the available inventory of the low-purity group in the allocation ratio scheme exceeds the total hydrogen consumption rate in the relevant equipment classification list; if it exceeds, the purification module is triggered to perform purity improvement processing on the low-purity group according to the purity compensation coefficient, and obtain updated purity level classification and quality stability information. Specifically, this includes obtaining hydrogen consumption rate data from the equipment classification list, summarizing and calculating the data using a data integration tool to obtain the total consumption rate value; comparing the total consumption rate value with the available inventory of the low-purity group, if the available inventory exceeds the total consumption rate value, marking the relevant batches using a data recording tool to determine the inventory list to be processed; obtaining purity compensation coefficient data based on the inventory list to be processed, and using a parameter adjustment tool to perform purity improvement processing on the low-purity group inventory to obtain adjusted purity level information; and performing quality stability testing on the processed inventory batches using a data verification tool based on the adjusted purity level information, and determining the final quality stability information if it meets the preset threshold range.

[0011] Preferably, the process also includes S6: adjusting the updated purity grade classification, quantity proportion of each group, and allocation ratio scheme through iterative calculation technology; generating a final hydrogen resource allocation list by combining the operating time window and geographical location constraints; and transmitting the list to the microgrid control unit for scheduling. Specifically, this includes obtaining constraint data from the operating time window and geographical location constraint database; comparing the constraint data with the purity grade classification information; if the data meets a preset threshold range, generating a preliminary allocation ratio scheme through a data integration tool to obtain an initial allocation list; obtaining quantity proportion data and purity adjustment parameters based on the initial allocation list; dynamically adjusting the allocation ratio scheme using a parameter matching tool; and recalculating the adjusted allocation list using a data calibration tool if the adjusted ratio exceeds a preset threshold.

[0012] Preferably, step S6 further includes associating and matching the adjusted allocation list with geographical location constraint information using a data transmission tool, performing consistency checks on the matching results using a logic verification tool, generating a final hydrogen resource allocation list if it meets preset standards, and determining the completeness of the list; obtaining the final hydrogen resource allocation list, transmitting it to the microgrid control unit using a communication interface tool, monitoring the transmission process in real time, and generating scheduling execution instructions using an instruction generation tool if it meets integrity requirements, thus determining the final execution data.

[0013] Preferably, the process also includes S7: transmitting the final hydrogen resource allocation list to the microgrid control unit via the system simulation interface; performing execution verification based on priority ranking and fault tolerance range values; if the energy efficiency does not reach the preset threshold, backtracking to the group processing stage, readjusting the matching logic of purity level classification and storage location information to obtain an optimized allocation ratio scheme; specifically, retrieving the final hydrogen resource allocation list data from a pre-established database, comparing the list data with the priority ranking rules, and if it meets the preset threshold range, performing integrity detection through a data verification tool to obtain verified allocation list data; and sending the verified allocation list data to the target unit using a communication transmission tool, performing data segmentation processing based on the fault tolerance range values ​​during transmission to determine the completed list records.

[0014] Preferably, step S7 further includes matching the completed list record with a preset energy efficiency threshold using a logical comparison tool. If the standard is not met, a backtracking processing logic is triggered to obtain the reclassified purity level information. Based on the reclassified purity level information, a parameter adjustment tool is used to dynamically match the storage location information with the allocation ratio scheme to determine whether the optimized list meets the execution standard.

[0015] As can be seen from the above technical solution, the present invention has the following beneficial effects: This method for optimizing the operation of a green electricity-hydrogen coupled microgrid dynamically couples green electricity output with the electrolysis hydrogen production process, ensuring priority utilization of renewable energy even under fluctuating conditions and improving green electricity consumption efficiency. Simultaneously, it introduces a multi-source data integration mechanism for hydrogen batch identification, purity levels, and quality stability. Through processing industrial big data, it achieves refined group management of hydrogen, avoiding the uncontrollable hydrogen quality issues of existing technologies. By collecting purity requirements, operating windows, and hydrogen consumption characteristics of hydrogen-using equipment, a demand classification list is established, and linear programming is used to achieve optimal matching between hydrogen categories and equipment requirements, improving the utilization efficiency of hydrogen of different purities. When the system detects abnormal inventory levels of low-purity groups, it automatically triggers purification processing and generates a final scheduling scheme through iterative optimization, ensuring that the scheduling results conform to operational constraints. Finally, the scheduling effect is verified through a system simulation interface, and backtracking adjustments are made when energy efficiency is insufficient, achieving closed-loop optimization of hydrogen energy scheduling. Therefore, this invention significantly improves the resource utilization, matching accuracy, and operational stability of the green electricity-hydrogen microgrid. Attached Figure Description

[0016] Figure 1 This is a flowchart of the green electricity coupled hydrogen energy microgrid operation optimization method of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, this invention provides a technical solution: a method for optimizing the operation of a green electricity coupled hydrogen energy microgrid, including S1, real-time acquisition of renewable energy power generation, predicted output and load information through a microgrid monitoring system, determining whether to trigger the operation of the electrolysis hydrogen production unit based on the volatility of green electricity, so that the hydrogen production process is dynamically coupled with the green electricity production capacity, and real-time acquisition of hydrogen production batch identification and purity data from the electrolysis equipment through a sensor network; and the use of multi-source information integration technology to combine production batch identification with timestamp records to generate a preliminary hydrogen category classification including purity level division and the quantity proportion of each group. S2. Based on the preliminary hydrogen category distribution dataset, obtain storage location information and available inventory, and group the hydrogen according to purity level and quality stability to determine a detailed hydrogen category distribution list for low purity, medium purity, and high purity groups; S3. Collect equipment type identifiers, purity requirement thresholds, and operating time window information through the equipment management interface, and combine them with historical demand records and priority sorting; if the purity requirement threshold is higher than the average quality stability of the high purity group, it is marked as a strict requirement category, resulting in an equipment requirement classification list including tolerance range values; S4. Use linear... The planning technology matches the equipment type identifiers, hydrogen consumption rates, and geographical distribution in the equipment demand classification list with the storage locations, quantity proportions of each group, and stability parameters in the hydrogen category distribution list to determine the hydrogen allocation ratio scheme for each purity group; S5, it determines whether the available inventory of the low purity group in the allocation ratio scheme exceeds the sum of the hydrogen consumption rates in the relevant equipment classification lists; if it does, it triggers the purification module to perform purity improvement processing on the low purity group according to the purity compensation coefficient, obtaining updated purity level classification and quality stability information; S6, it adjusts the updated purity level classification, quantity proportions of each group, and allocation ratio scheme through iterative calculation technology, and generates the final hydrogen resource allocation list by combining the operating time window and geographical constraints, and transmits it to the microgrid control unit for scheduling; S7, it transmits the final hydrogen resource allocation list to the microgrid control unit through the system simulation interface, and performs execution verification by combining priority sorting and fault tolerance range values. If the energy efficiency does not reach the preset threshold, it backtracks to the group processing stage, readjusts the matching logic of purity level classification and storage location information, and obtains the optimized allocation ratio scheme.

[0019] This implementation method leverages the randomness and volatility inherent in renewable energy. It dynamically determines the timing of hydrogen production via electrolysis by monitoring real-time output changes from green electricity sources such as wind and solar power, thus coupling hydrogen production with the real-time capacity of renewable energy. A sensor network collects production batch identifiers, hydrogen purity, and output data from the electrolysis system. Through multi-source data integration, batch records are matched with timestamps to form a preliminary purity classification dataset. Subsequently, hydrogen resources are grouped into low, medium, and high purity categories based on purity levels and quality stability parameters. The system simultaneously collects information on the type of hydrogen-using equipment, purity thresholds, operating windows, and historical usage preferences, and determines whether purity demand exceeds the stability range of the existing high-purity group to identify equipment with stringent requirements. Then, a linear programming algorithm is used to match purity groups based on equipment location, consumption rate, and hydrogen inventory distribution to determine the optimal allocation ratio. If the low-purity group has a high inventory but allocation cannot meet demand, the purification module increases its purity according to a preset compensation coefficient to improve the overall effective utilization rate of hydrogen levels. The system continuously adjusts the category division and allocation ratio through iterative optimization, and forms the final allocation list by combining equipment operating windows and spatial distance constraints. Before scheduling, simulations are used to verify whether energy efficiency meets the threshold. If it is too low, the classification steps are backtracked to revise the division logic to ensure that the final scheduling scheme is optimal.

[0020] This method enables close coupling of the entire process of green electricity generation and hydrogen production, improving the utilization rate of renewable energy and reducing wind and solar curtailment rates. Multi-source data integration and grouping processing allow for refined management of hydrogen based on purity levels and stability, enhancing the controllability of hydrogen resources. The introduction of linear programming and iterative optimization techniques makes hydrogen supply and demand matching more precise, avoiding waste of high-purity hydrogen and improving the flexibility of supply strategies. Dynamic compensation through the purification module further enhances the adaptability to low-purity hydrogen, improving inventory utilization efficiency. Simultaneously, optimizing allocation paths based on equipment operating windows and geographical location helps reduce transmission losses and scheduling delays. Simulation verification mechanisms further enhance system stability, ensuring that the scheduling strategy has high energy efficiency and fault tolerance in actual operation. Overall, this method can significantly improve the operational optimization level of hydrogen microgrids, enhancing economic efficiency, safety, and energy utilization efficiency.

[0021] S1 includes real-time acquisition of renewable energy power generation data and load information through microgrid monitoring devices, combined with predicted output data, and comparison using a preset volatility threshold. If the power generation fluctuation exceeds the preset threshold, an operation command for the electrolytic hydrogen production unit is triggered, resulting in dynamically coupled operating status data. Based on the dynamically coupled operating status data, hydrogen production batch identifiers and purity information are obtained from the electrolytic hydrogen production unit through a sensor network. The batch data is classified and stored to determine a preliminary dataset containing batch identifiers and purity levels. Using a multi-source information integration tool, the batch identifiers in the preliminary dataset are associated and matched with timestamp records to generate a hydrogen category distribution dataset containing purity level classifications and quantity proportions, obtaining structured distribution information. The structured distribution information is organized using data processing tools, and statistics are performed for each purity level. If the purity level proportion is lower than a preset threshold, it is marked as a category that needs optimization, resulting in the final category distribution optimization data.

[0022] In this embodiment, renewable energy power generation data is recorded by a microgrid monitoring device at a fixed sampling interval, which can be set to 1 second, ensuring that a complete data acquisition, including power generation and load values, is completed every second. The monitoring device's sampling program generates a timestamp every second and writes the timestamp along with the real-time collected power generation and load values ​​into the monitoring data table, ensuring that each data point can be accurately located chronologically. Predicted output data is calculated in advance by the microgrid prediction system and provided to the monitoring device; the predicted data corresponds one-to-one with the real-time collected data in chronological order. Within each 1-second sampling cycle, the monitoring device performs a comparison process according to fixed steps. First, it reads the power generation value recorded in the previous second from the data table and obtains the power generation value for the current second from the current sampling cycle. Then, it calculates the power generation difference between two seconds. A preset volatility threshold is determined based on historical data, including monitoring data from at least the past 30 days. The monitoring device statistically analyzes all power generation differences from the past 30 days, records the power change every second, and extracts the range of changes with the most frequent occurrences as the typical range of changes. For example, if 30 days of historical data shows that the most frequent changes in power generation per second occur between 20 and 40, then 40 is set as the volatility threshold. During real-time operation, if the current power change exceeds 40, the monitoring device immediately outputs an operation command. This command is written into the device's operation control port in a fixed format, and the electrolytic hydrogen production unit enters the operating state upon receiving the command. The trigger time, trigger reason, and the power generation value at that time are recorded as dynamic coupling state data and written into the operation state table.

[0023] During operation, the electrolytic hydrogen production unit uses a sensor network to collect data on the production batch identifier, hydrogen purity value, and collection time of hydrogen at fixed sampling intervals, which can be set to 10 seconds. The batch identifier is a number generated by the hydrogen production unit each time production starts, based on the start-up time. The purity value is the percentage of hydrogen purity measured by the sensors; for example, 99 indicates a purity of 99%. The collected data is stored according to the batch number. Specifically, the system checks if the batch number already exists in the data table. If it doesn't exist, a new batch entry is created, and the purity value and collection time are written to that entry; if it already exists, the record is appended to the corresponding entry. After obtaining the initial dataset, the multi-source information integration tool associates the batch numbers with timestamps, binds each purity record to its corresponding collection time, and sorts the data from earliest to latest collection time to ensure a complete time-series structure. Based on this, the system classifies purity values ​​according to fixed criteria. For example, purity between 99 and 100 is considered high purity, between 95 and 99 is medium purity, and between 90 and 95 is low purity. The integration tool counts the number of records in each purity level and divides this number by the total number of records to obtain the proportion of high, medium, and low purity. Then, the purity level, corresponding proportion, batch number range, and time range are combined in a fixed format to generate a hydrogen category distribution dataset. This dataset is read by the data processing tool, which determines the proportion of each purity level according to a predefined process. Each purity level has a preset proportion threshold, determined by a historical stable distribution. This historical stable distribution is determined by statistically analyzing the lowest stable proportion of each purity level over the past 60 days. For example, if historical data shows that the proportion of a certain purity level remains between 0.25 and 0.35 for most of the time, the system sets 0.25 as the minimum proportion threshold for that level. The data processing tool reads the proportion of the current purity level and compares it with the corresponding 0.25 threshold. If the proportion is lower than 0.25, the purity level is marked as a category that needs optimization. The marking method involves the system writing a fixed marker field into the entry, recording the words "needs optimization," and simultaneously recording the current actual proportion of that category. After marking is complete, the system writes all the data into category distribution optimization data according to a fixed format.

[0024] S2 includes extracting storage location information and available inventory quantity from the preliminary hydrogen category distribution dataset, classifying the purity level data to obtain classified grouped data; marking the location based on the grouped data, using a data matching tool to associate inventory quantity with purity level, and determining the detailed distribution information of each group; using an integration tool to structure the detailed distribution information, if the inventory of a certain group is lower than a preset threshold, it is marked as a priority adjustment group, and the marked list data is obtained; for the list data, a storage tool is used to archive the low purity group, medium purity group, and high purity group, determine the quality stability distribution of each group, and obtain the final distribution list.

[0025] In this embodiment, the storage location information and available inventory quantity for each record are first extracted from the preliminary hydrogen category distribution dataset. The storage location information is the fixed number of the hydrogen storage device in the microgrid, including the tank number and physical location number. The available inventory quantity is the available volume of hydrogen recorded in that tank, recorded in standard units of measurement. The system reads all records one by one from the preliminary dataset and classifies them according to the purity level data field. The purity level is based on the existing classification range in the preliminary dataset without changing the range; for example, low purity level is 90 to 95, medium purity level is 95 to 99, and high purity level is 99 to 100. The system reads the purity value in each data record and compares the purity value with the above range. If the purity value is between 90 and 95, the data record is written to the low purity group; if the purity value is between 95 and 99, it is written to the medium purity group; and if the purity value is between 99 and 100, it is written to the high purity group, thus forming the classified grouped data. After classifying the data, the system generates a location tag for each data entry based on the grouped data. This location tag includes a storage location number, a geographic location number, and the actual coordinates within the microgrid. The location tag is written into each data entry according to a fixed format, giving each data entry a clear spatial attribute. After generating the location tags, the system activates a data matching tool. This tool reads the purity level and inventory quantity of each data entry within each group in a fixed order and matches the inventory quantity with the purity level. The matching process is as follows: the tool reads the first record from the classified grouped data, writes the purity level and inventory quantity as matching entries using fixed fields, and writes the matching entries into the statistical list corresponding to that purity level. Then, it reads the next record and repeats the operation until all records have been processed. After completing the matching, the data matching tool calculates the total inventory quantity of all entries within each group. The calculation method is to read the inventory quantity of each record in turn, sum them up and record them as the total inventory quantity of that purity level. At the same time, it records the storage location and inventory quantity of each record, and integrates these data into detailed distribution information for the group. The detailed distribution information includes the number of entries, a list of all inventory quantities, a list of all storage locations, and a purity level label.

[0026] The integration tool then reads the detailed distribution information for each group and performs structured processing. The structured processing involves the integration tool writing the purity level, total inventory quantity, percentage of each inventory quantity, number of entries, and all storage locations into structured entries in a fixed order. The inventory quantity percentage is processed by the integration tool on an entry-by-entry basis, by dividing the inventory quantity of each record by the total inventory of that purity group to obtain the percentage value, and then writing the percentage value into the corresponding field of that entry. After completing the structured entries, the integration tool reads the total inventory of each group and compares this total inventory with a preset threshold. The preset threshold is determined as follows: the system statistically analyzes the inventory quantity of each purity level over the past 60 days, records the lowest daily inventory quantity, sorts the 60 lowest values ​​from largest to smallest, and takes the value in the middle of the sort as the safe minimum inventory value for that purity level. For example, if the 30th lowest inventory quantity in the low purity group over the past 60 days is 200, then 200 is set as the minimum inventory threshold for the low purity group. If the integration tool finds that the current total inventory is below the threshold during comparison, it will mark the group as a priority adjustment group. The marking method is to write the priority adjustment group identifier into the structured entry of the group, and record the current total inventory and the threshold value to form traceable information.

[0027] After marking is complete, all groups are compiled into a marked list of data and processed by the storage tool. The storage tool sequentially creates archive directories for low-purity, medium-purity, and high-purity groups according to the purity level classification structure, and writes the corresponding structured entries for each category into the corresponding directories. The writing method is as follows: the system reads the list data in purity level order. Data belonging to the low-purity group is written to the low-purity directory, data belonging to the medium-purity group is written to the medium-purity directory, and data belonging to the high-purity group is written to the high-purity directory. The data is then sorted by inventory quantity from largest to smallest to ensure accurate retrieval within the archive. After archiving is complete, the system analyzes the purity value range within each group. The purity value range is determined by the minimum and maximum purity values ​​within the group. For example, if all purity values ​​in a group are between 99 and 99.5, the system records the minimum value as 99 and the maximum value as 99.5, and writes this range into the final distribution list as quality stability information. The system processes all entries for the low-purity group, medium-purity group, and high-purity group in sequence until a final distribution list is generated, which includes the total inventory quantity, storage location distribution, quantity percentage distribution, and quality stability distribution of the three purity groups.

[0028] S3 involves obtaining equipment type identifiers, purity requirement thresholds, and operating time window data from the equipment management interface. Combined with a pre-established historical requirement record database, a data comparison tool is used to determine the priority ranking information for each piece of equipment, resulting in a preliminary requirement priority list. For this preliminary priority list, the average quality stability data of the high-purity group is obtained. If the purity requirement threshold of a piece of equipment is higher than the average quality stability, it is marked as a strict requirement category, resulting in a set of equipment categories including these markings. Based on the equipment category set, a data integration tool is used to extract the fault tolerance range value for each piece of equipment. Combined with the operating time window information, a time matching algorithm is used to determine the degree of requirement matching for each equipment within a specific time period, determining the final classification matching result. For the classification matching result, a storage tool is used to archive the strict requirement category and other categories, resulting in a equipment requirement category list containing the fault tolerance range value.

[0029] In this implementation, the system first reads the equipment type identifier, purity requirement threshold, and operating time window data for all equipment from the equipment management interface. The equipment type identifier is a fixed number assigned to each piece of equipment by the system, used to uniquely identify the equipment throughout the scheduling process. The purity requirement threshold is the minimum percentage of hydrogen purity required for the equipment to operate normally, determined by explicit parameters in the equipment specification document. The operating time window is the specific time period during which the equipment is allowed to operate, consisting of a start time and an end time; for example, the operating time window for a certain piece of equipment is from 8:00 AM to 6:00 PM daily. After reading the above data, the system retrieves all hydrogen usage records for each piece of equipment from the historical demand record database for the past 60 days, including historical usage time, historical purity requirement value, historical hydrogen consumption, and historical number of operating failures. The system initiates the comparison operation using a data comparison tool. The specific steps are as follows: Each device's historical records are read one by one. The historical usage count is used as evaluation parameter 1, the historical average purity requirement as evaluation parameter 2, and the number of failures due to insufficient purity during historical operation as evaluation parameter 3. Evaluation parameter 1 is directly determined by the number of historical records, evaluation parameter 2 is determined by averaging all purity requirement values ​​in the historical records, and evaluation parameter 3 is determined by the number of times the historical records show "failure to achieve operation due to insufficient purity." The system uses the magnitudes of evaluation parameters 1, 2, and 3 as the priority criteria. A higher evaluation parameter 3 indicates a higher sensitivity to purity matching, thus increasing the priority. If evaluation parameters 3 are the same, evaluation parameter 2 is compared; a higher evaluation parameter 2 indicates a stronger dependence on high-purity hydrogen during operation, further increasing the priority. If evaluation parameters 2 are also the same, evaluation parameter 1 is compared; devices with a higher evaluation parameter 1 have a higher priority. The system sorts all devices according to the above rules and generates a preliminary priority list.

[0030] After obtaining the priority list of requirements, the average quality stability is calculated based on all purity records in the high-purity group generated in step S2. The average quality stability is the arithmetic mean of all purity values ​​within the high-purity group, obtained by the system performing the following operations: The system reads the purity values ​​of all records in the high-purity group, sums all the purity values ​​one by one, and then divides the sum by the number of records to obtain the average purity value. The system then compares the purity requirement threshold for each device with this average quality stability. If the purity requirement threshold of a device is higher than the average quality stability, the system marks the device as belonging to the strict requirement category. This marking is achieved by writing a strict requirement field for the device in the device classification set, and including the device's purity requirement threshold and the average quality stability value of the high-purity group in the field, ensuring that subsequent steps can accurately determine the reason for its strict requirement.

[0031] After obtaining the equipment classification set, the data integration tool is used to read the tolerance range value for each device. The tolerance range value is the allowable purity deviation of the device without affecting normal operation, determined by historical operating records. Specifically, the system checks all operating records of the device over the past 60 days, finds the lowest purity value corresponding to all stable operating conditions, and then calculates the difference between this lowest purity value and the device's purity requirement threshold. This difference is the tolerance range value. For example, if a device's purity requirement threshold is 98, and the historical record shows the device can still operate stably with a minimum purity of 97, then the tolerance range value is 1. The system writes this tolerance range value into the corresponding device entry in the classification set and uses it for subsequent time-matching algorithms.

[0032] The time-matching algorithm is then executed, using the operating time window as a basis to determine whether the equipment can obtain a sufficient hydrogen supply within a specific time period. The specific execution flow of the time-matching algorithm is as follows: The system reads the equipment's operating time window, for example, a device is allowed to operate from 8:00 AM to 6:00 PM daily; the system then reads the hydrogen supply time series data (this data is generated by the previous steps, recording the available purity levels for each time period each day); at fixed time intervals of 1 hour or 30 minutes, the system compares the available purity value for that time period with the equipment's demand threshold plus a tolerance range value. For example, if the equipment's demand threshold is 98 and the tolerance range value is 1, then the equipment can meet the operating conditions if the purity reaches 97 or higher; the system compares the available purity for each time period from 8:00 AM, 9:00 AM, 10:00 AM to 6:00 PM in chronological order. If the supply purity for a certain time period is greater than or equal to 97, the system records that time period as a matchable time period; if the supply purity is lower than 97, it is recorded as an unmatchable time period. The system writes the matching results for all time periods into the classification matching results for subsequent scheduling.

[0033] After obtaining the classification matching results, the storage tool is invoked to perform the archiving operation. The archiving operation is performed as follows: the storage tool first creates two archiving directories: a strict requirement category directory and a general requirement category directory. The system reads the device information one by one according to the classification matching results. If the device belongs to the strict requirement category, it is written to the strict requirement directory; otherwise, it is written to the general requirement directory.

[0034] S4 includes obtaining equipment type identifiers, hydrogen consumption rates, and geographical distribution data from the equipment demand classification list, and simultaneously extracting storage location information from the hydrogen category distribution list. A data integration tool is used to compare the data to obtain a list of correspondences between equipment and hydrogen categories. For this correspondence list, a data filtering tool is used to extract the association information between equipment type identifiers and purity group classifications. If the hydrogen consumption rate is higher than a preset threshold, it is marked as a high-consumption category, and a list of high-consumption category equipment is determined. Based on the high-consumption category equipment list, matching information between geographical distribution and storage location is obtained. A location matching algorithm is used to process the data to determine whether it meets a preset matching range, resulting in a set of location matching results. For the location matching result set, a data calculation tool is used to combine quantity proportions and stability parameters. If the stability parameter is lower than a preset threshold, the allocation scheme is adjusted to determine the final list of hydrogen allocation ratios for each purity group.

[0035] In this implementation, the system first reads the equipment type identifier, hydrogen consumption rate, and geographical location distribution data for each piece of equipment from the equipment requirement classification list. The equipment type identifier is a fixed number used to uniquely identify the equipment; the hydrogen consumption rate is the amount of hydrogen the equipment needs to consume per unit time, recorded in fixed volume units; and the geographical location distribution data is the actual location coordinates or area number of the equipment within the microgrid. The system then reads the hydrogen storage location information for each purity group from the hydrogen category distribution list. This information includes the tank number, storage location number, and corresponding physical location coordinates. The data integration tool compares the equipment location data with each hydrogen storage location one by one. The comparison method is as follows: the system reads the geographical location number of each piece of equipment, then reads the number and coordinates of each hydrogen storage location one by one. The system calculates the distance between the two locations. If the distance meets the basic storage association conditions, the equipment and the corresponding storage location are added to the correspondence list, forming an optional correspondence between equipment and hydrogen category.

[0036] After generating the correspondence list, a data filtering tool is used to filter the entries in the list. The data filtering tool reads the equipment type identifier and corresponding purity group classification information for each device in the correspondence list, while simultaneously checking whether the hydrogen consumption rate of the device exceeds a preset threshold. The preset threshold is determined as follows: the system statistically analyzes the hydrogen consumption rate of all devices over the past 60 days, sorts the consumption rates from highest to lowest, and takes the lowest consumption value of the top 20% of devices as the threshold. For example, if the lowest consumption rate of the top 20% of devices is 300, then the system sets 300 as the consumption rate threshold. The system compares the actual consumption rate of each device with 300. If a device's hydrogen consumption rate is higher than 300, it is marked as a high-consumption category. The marking method involves writing a "high-consumption category" field into the device entry, making it recognizable and prioritized in subsequent steps. Simultaneously, the system aggregates all marked devices to generate a high-consumption category device list.

[0037] After obtaining the list of high-consumption category equipment, the geographical location matching process begins. The process is as follows: the system reads the geographical coordinates or region number of each high-consumption category equipment, and simultaneously reads the corresponding hydrogen storage location coordinates or region number. The location matching algorithm judges the location according to fixed rules. The system calculates the actual distance between the equipment location and the storage location, which is determined by the difference between the two numbers or coordinates. If the system predefines the matching range to 5 kilometers, the system checks whether the distance between the equipment location and the storage location is less than or equal to 5 kilometers. If the condition is met, it is recorded as a successful location match; if it is greater than 5 kilometers, it is recorded as a mismatch. The system summarizes all matching results by equipment to form a location matching result set.

[0038] After obtaining the location matching result set, the system activates the data calculation tool to calculate the quantity proportion and determine the stability parameter. The quantity proportion is the percentage of each purity group among the available options for the corresponding equipment. The system calculates this by reading the inventory quantity of each purity group in the matching results and dividing that quantity by the sum of the inventory quantities of all purity groups. The stability parameter is the size of the stable range of purity values ​​within each purity group, calculated by the system by reading the maximum and minimum values ​​of the purity values. For example, if a purity value range is 99 to 99.5, the stability parameter is the value of that range. The system compares the stability parameter with a preset stability threshold. The stability threshold is determined by statistically analyzing the stable range of all purity groups over the past 60 days and using the minimum value of the largest 10% range as the threshold. For example, if the statistical results show that the minimum value of the largest 10% range is 0.3, the system sets 0.3 as the stability threshold. The system compares whether the stability parameter of a purity group is lower than 0.3. If it is, it indicates that the purity group is not stable enough in the current state and the allocation scheme needs to be adjusted. The adjustment method involves the system reducing the allocation ratio of the purity group and increasing the allocation ratio of the purity group with higher stability, so that the final allocation scheme meets the equipment's operational requirements and hydrogen supply stability requirements. The system records the adjusted results as a final hydrogen allocation ratio list for each purity group.

[0039] S5 includes obtaining hydrogen consumption rate data from the equipment classification list, summarizing and calculating the data using a data integration tool to obtain the total consumption rate value; comparing the total consumption rate value with the available inventory of the low-purity group, if the available inventory exceeds the total consumption rate value, marking the relevant batches using a data recording tool to determine the inventory list to be processed; obtaining purity compensation coefficient data based on the inventory list to be processed, using a parameter adjustment tool to perform purity improvement processing on the low-purity group inventory to obtain the adjusted purity level information; and performing quality stability testing on the processed inventory batches using a data verification tool based on the adjusted purity level information, if it meets the preset threshold range, determining the final quality stability information.

[0040] In this implementation, the system first reads the hydrogen consumption rate data for all equipment from the equipment demand classification list. The hydrogen consumption rate is the amount of hydrogen required by the equipment per unit time, recorded in fixed volume. The system reads the consumption rate for each piece of equipment one by one and calls a data integration tool to summarize and calculate all consumption rates. The summarization method is as follows: the system reads the consumption rate in each record sequentially, adds them up one by one, and uses the sum as the total consumption rate value. For example, if the consumption rates of three pieces of equipment are 100, 150, and 200 respectively, the system adds them up sequentially to obtain a total consumption rate value of 450. The system then reads the available inventory quantity for all batches in the low-purity group. The available inventory quantity is the volume of hydrogen that can be supplied externally for that batch. The system compares the total available inventory of the low-purity group with the total consumption rate value. The comparison method is to add up the inventory quantities of all batches in the low-purity group and then compare this sum with the total consumption rate value. If the total available inventory of the low-purity group is greater than the total consumption rate value, it indicates that there is a remaining amount of inventory in the low-purity group that can be used for purity improvement.

[0041] After determining that the inventory level exceeds the total consumption rate, the relevant batches are marked using data logging tools, creating a pending inventory list. The marking method is as follows: the system reads the inventory level of all batches in the low-purity group one by one, sorts them from largest to smallest based on inventory level, and then marks the batches with the largest inventory levels as pending batches according to the sorting order. The system writes the batch number, inventory level, and purity value of these marked batches into the pending inventory list, providing clear processing targets for the next step.

[0042] After obtaining the inventory list to be processed, the system reads the preset purity compensation coefficient data. The purity compensation coefficient is the compensation amount required to improve the purity of low-purity hydrogen. It is determined by the system statistically analyzing the purity improvement of each batch after purification over the past 60 days of the hydrogen production unit, and using the average improvement as the purity compensation coefficient. For example, if the average improvement of a certain purity level in the historical records is 3, the system will write 3 as the compensation coefficient into the compensation parameter table. The system then calls the parameter adjustment tool to perform purity improvement processing on each batch in the inventory list to be processed. The improvement processing method is to add the purity compensation coefficient to the purity value of the batch. For example, if the batch purity is 93 and the compensation coefficient is 3, the improved purity level information will be 96. The system writes all processed batch records into the adjusted purity level information table.

[0043] After generating the adjusted purity level information, a quality stability test is performed on each processed batch using a data verification tool. The test method is as follows: the system reads the continuous purity records of each batch after purification, identifies the maximum and minimum purity values, and uses the difference between them as the stability range for that batch. For example, if a batch's purity fluctuates between 96 and 96.4 after processing, the stability range is the difference between the maximum and minimum purity values. The system compares this stability range with a preset stability threshold. The stability threshold is determined by: the system statistically analyzing the purity fluctuation ranges of all batches under stable operating conditions over the past 60 days, sorting these fluctuation ranges from smallest to largest, and then taking the median value as the threshold. For example, if the median value is 0.5, the system uses 0.5 as the stability threshold, meaning that the purity fluctuation range of a processed batch must be less than or equal to 0.5 to be considered stable.

[0044] The system compares the stability range of each batch to see if it is less than or equal to the threshold. If the stability range of a batch is within the threshold, the system marks the batch as a stable batch and writes its processed purity value and stability range into the final quality stability information list. The system performs the above test on all batches, ultimately generating quality stability information for all processed batches that meet the stability requirements.

[0045] S6 includes: acquiring constraint data from the operating time window and geographical location constraint database; comparing the constraint data with purity level classification information; if it meets the preset threshold range, generating a preliminary allocation ratio scheme through a data integration tool to obtain an initial allocation list; acquiring quantity proportion data and purity adjustment parameters based on the initial allocation list; dynamically adjusting the allocation ratio scheme using a parameter matching tool; if the adjusted ratio exceeds the preset threshold, recalculating through a data calibration tool to determine the adjusted allocation list; associating and matching the adjusted allocation list with geographical location constraint information through a data transmission tool; performing consistency checks on the matching results using a logic verification tool; if it meets the preset standards, generating a final hydrogen resource allocation list and determining its integrity; acquiring the final hydrogen resource allocation list and transmitting it to the microgrid control unit through a communication interface tool; monitoring the transmission process in real time; if it meets the integrity requirements, generating scheduling execution instructions through an instruction generation tool to determine the final execution data.

[0046] In this implementation, the system first retrieves constraint data from the operating time window and geographic location constraint database. The constraint data includes the allowed start and end times for each device, as well as the distance restrictions between the device and the hydrogen storage location. The system reads all constraint records one by one, writing the operating time window and its geographic location constraint for each device into the constraint table. The system then reads the purity grade classification information, generated in the preceding steps, which includes purity ranges and corresponding inventory distributions for three groups: low purity, medium purity, and high purity. The system performs a comparison operation to match the constraint data with the purity grade classification information item by item. The comparison method is as follows: first, it determines whether the device's operating time window is within a period when hydrogen can be supplied; then, it determines whether the distance between the device location and the hydrogen storage location for each purity group is within the restriction range. The system generates a matching entry for each successfully matched record and calls a data integration tool to generate a preliminary allocation ratio scheme based on the matching entries and certain proportional rules. The allocation rule is as follows: If a device can match multiple purity groups simultaneously within its operational timeframe, the system calculates the allocation ratio of that device among the three purity groups based on the available inventory quantity of each purity level, the intensity of demand for the device, and the distance difference to the device's location. The system writes each allocation result into the initial allocation list, which includes the device number, the allocated purity group category, and the corresponding ratio value.

[0047] After obtaining the initial allocation list, the system calls the proportion adjustment module to retrieve the quantity percentage data and purity adjustment parameters for each purity group. The quantity percentage data represents the percentage of each purity group's inventory quantity relative to the total inventory quantity. The purity adjustment parameters are the parameters formed through the compensation operation in step S5 above, used to compensate for insufficient purity in low-purity groups during the allocation process. The system compares the initial allocation ratio with the quantity percentage data and purity adjustment parameters item by item, and uses a parameter matching tool to dynamically adjust the ratio values. The adjustment method is as follows: if the quantity percentage of a purity group is high, the system appropriately increases its allocation ratio; if the quantity percentage is low, the system appropriately decreases the allocation ratio of that group; if the purity adjustment parameter of that group is large, the system increases the adaptation ratio of that group according to the adjustment parameter to compensate for the purity. If the adjusted ratio value exceeds a preset threshold, the system calls the data calibration tool to recalculate the ratio value. The preset threshold is determined by: the system statistically analyzing the highest allocation ratio of each purity group in all scheduling over the past 60 days, sorting the highest values ​​from smallest to largest, and taking the median value of the sorted values ​​as the threshold to prevent a certain purity group from being over-allocated. The system compares the adjusted proportion with the median value item by item. If the proportion exceeds the median value, the system adjusts it proportionally to ensure that it does not exceed the threshold, and finally calculates and forms the adjusted allocation list.

[0048] Subsequently, a data transmission tool is used to correlate and match the adjusted allocation list with the geographical location constraints. The matching method is as follows: the system compares the distance between the purity group location in each allocation record and the equipment location to ensure that the allocation plan is spatially feasible. The system writes all successfully matched results into the matching result set, and then calls a logic verification tool to perform consistency checks on the matching results. The checks are as follows: whether the running time of each allocation record is within the allowed operating time window of the equipment; whether the hydrogen allocation amount of each group is within the allowable inventory range of that purity group; and whether the allocation ratio is consistent with the adjusted ratio. If all checks meet the preset standards, the system constructs the final hydrogen resource allocation list from all qualified entries and checks the completeness of the list. The completeness check method is as follows: the system checks item by item for unallocated equipment, purity groups with undetermined ratios, or missing fields in records. If none of these are found, the list is marked as complete.

[0049] The final hydrogen resource allocation list is then read and transmitted to the microgrid control unit via a communication interface tool. During transmission, the system performs real-time monitoring, checking for data packet integrity, arrival order, and interruptions. If the monitoring results show complete and uninterrupted data, the system uses an instruction generation tool to convert the list content into instructions, generating standardized scheduling instructions from the recorded equipment number, operating time, hydrogen type, and allocation ratio. Finally, the final execution data is determined.

[0050] S7 includes retrieving the final hydrogen resource allocation list data from a pre-established database, comparing the list data with priority sorting rules, and if it meets a preset threshold range, performing integrity checks using a data verification tool to obtain verified allocation list data; based on the verified allocation list data, sending it to the target unit using a communication transmission tool, performing data segmentation processing during transmission in conjunction with fault tolerance range values ​​to determine the list records that have been transmitted; for the transmitted list records, matching them with preset energy efficiency thresholds using a logic comparison tool, triggering backtracking processing logic to obtain reclassified purity level information; based on the reclassified purity level information, dynamically matching the storage location information with the allocation ratio scheme using a parameter adjustment tool to determine whether the optimized list meets the execution standards.

[0051] In this embodiment, the final hydrogen resource allocation list data is first read from a pre-established database. This list data, generated by previous steps, includes the hydrogen demand type, allocated purity group category, allocation ratio, operating time, and corresponding storage location information for each device. The system reads the list data item by item and sorts each record according to the device number to ensure the consistency of the data comparison process. The system then compares the list data item by item with the priority ranking rules. The priority ranking rules are the priority list generated in step S3, determined based on the intensity of device demand and historical operating performance. The comparison method is as follows: the system reads the device number in each allocation entry, searches for the priority value of the device in the priority rules, and then determines whether the corresponding allocation scheme meets the corresponding priority requirements. For example, if a device is in the top 10% of the priority ranking, the system checks whether the device has obtained a higher purity group allocation ratio or a more stable hydrogen source; if so, it means that it meets the preset threshold range. The preset threshold range is determined by the system based on the scheduling results of the past 60 days. For example, if statistics show that the top 10% of priority equipment receives a supply of no less than 50% of the high-purity group, the system will set 50% as the minimum requirement for that equipment type.

[0052] After determining that the list data meets the priority requirements, the data verification tool is invoked to perform integrity checks on each record. The checks include verifying whether the record contains the device number, allocation purity, allocation ratio, operating time period, and storage location information; verifying whether the allocation ratio is a valid value (e.g., between 0 and 100) and consistent with the sum of all allocation ratios; verifying whether the operating time period matches the device's allowed operating window; and verifying whether the storage location information is within a valid range. The system combines all successfully verified records into the verified allocation list data.

[0053] The communication transmission tool is then activated, sending the verified allocation list to the target unit. During transmission, to improve data transmission stability and success rate, the system segments the data based on a fault tolerance range. This fault tolerance range is determined by the system based on past data loss patterns and the maximum tolerable segment length. For example, if system statistics show that the data loss rate is lowest when the segment length is below 200 bytes, then 200 bytes is used as the maximum segment length. The system splits the list data into segments no longer than 200 bytes, appends a sequence number to each segment, and transmits them sequentially, performing real-time monitoring during transmission. Real-time monitoring includes determining whether each data segment is delivered completely, whether the sequence numbers are received in order, and whether interruptions or duplicate receptions occur. After all data segments are successfully received, the system reassembles them into a completed transmission list record.

[0054] Next, a logical comparison tool is used to check whether the completed inventory records match the energy efficiency threshold. The energy efficiency threshold is determined by the system based on the average energy utilization rate of the system over the past 60 days. For example, if the system statistics show an average energy utilization rate of 0.8, then 0.8 is used as the threshold, requiring that the energy utilization efficiency after each scheduling execution must not be lower than this value. The system simulates the equipment operation process based on the inventory records to determine whether the assigned purity group and the equipment load can reach the energy efficiency threshold. If not, the system triggers a backtracking process. The execution method of the backtracking process is as follows: the system recalculates the purity stability based on the actual performance of each purity group in the allocation records and obtains new purity level classification information to replace the previous classification results.

[0055] After obtaining the reclassified purity level information, the system uses a parameter adjustment tool to dynamically match the storage location information with the allocation ratio scheme. The dynamic matching method involves reading the new purity level information and matching it against each piece of equipment requirement information to determine if each piece of equipment can obtain an allocation ratio that meets its operational needs under the new purity classification. If the purity group obtained by a piece of equipment is insufficient to meet its operational needs, the system will proportionally increase the allocation amount for its corresponding purity group. If necessary, its storage location information will be adjusted to match a more reasonable supply source. The system writes all adjustment operations into the optimized list and determines whether the list meets the execution criteria. Execution criteria include whether the allocation ratio is reasonable, whether the inventory is sufficient, whether the operating time is matched, whether the energy efficiency meets the standards, and whether there are any conflicting entries within the list. If all criteria are met, the system uses this optimized list as the final executable list.

[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for operation optimization of green electricity coupled hydrogen energy microgrid, characterized in that, The application relates to a hydrogen gas classification and distribution method based on dynamic coupling of electrolytic hydrogen production and green electricity generation. S1, real-time acquisition of renewable energy power generation, predicted output and load information through a micro-grid monitoring system, judgment of whether to trigger electrolytic hydrogen production device operation based on green electricity fluctuation, dynamic coupling of hydrogen production process and green electricity generation, real-time acquisition of hydrogen production batch identification and purity data from electrolytic equipment through a sensor network; A multi-source information integration technology is used to combine the production batch identification and the time stamp record to generate a preliminary hydrogen gas category distribution data set containing purity grade division and quantity proportion of each group; S2, according to the preliminary hydrogen gas category distribution data set, obtaining storage location information and available inventory, grouping processing according to purity grade division and quality stability, determining detailed hydrogen gas category distribution lists of low-purity group, medium-purity group and high-purity group; S3, collecting equipment type identification, purity demand threshold and operation time window information through an equipment management interface, combining historical demand records and priority sorting; If the purity demand threshold is higher than the average quality stability of the high-purity group, it is marked as a strict demand category, and an equipment demand classification list containing a fault tolerance range value is obtained; S4, using linear programming technology to match and calculate the equipment type identification, hydrogen consumption rate and geographical location distribution in the equipment demand classification list with the storage location, quantity proportion and stability parameters in the hydrogen gas category distribution list, and determining the allocation proportion scheme of hydrogen gas in each purity group. 2.The green electricity coupled hydrogen energy micro-grid operation optimization method of claim 1, wherein: The S1 comprises: Real-time acquisition of renewable energy power generation data and load information through a micro-grid monitoring device, combination of predicted output data, comparison with a preset fluctuation threshold, if the power generation fluctuation exceeds the preset threshold, an electrolytic hydrogen production device operation instruction is triggered, and dynamic coupling operation state data is obtained; According to the dynamic coupling operation state data, hydrogen production batch identification and purity information are obtained from the electrolytic hydrogen production device through a sensor network, batch data is classified and stored, and a preliminary data set containing batch identification and purity grade is determined; A multi-source information integration tool is used to associate and match the batch identification in the preliminary data set with the time stamp record, generate a hydrogen gas category distribution data set containing purity grade division and quantity proportion, and obtain structured distribution information; The structured distribution information is arranged through a data processing tool, and each purity grade is counted, if the purity grade proportion is lower than a preset threshold, it is marked as an optimization category, and final category distribution optimization data is obtained.

3. The green electric-coupled hydrogen energy microgrid operation optimization method of claim 1, wherein: The S2 comprises: Extracting storage location information and available inventory quantity from the preliminary hydrogen gas category distribution data set, classifying purity grade data, and obtaining classified grouping data; According to the grouping data, the position is marked, the inventory quantity is associated with the purity grade by using a data matching tool, and the detailed distribution information of each group is determined; The detailed distribution information is structured by an integration tool, if the inventory of a certain group is lower than a preset threshold, it is marked as a priority adjustment group, and the marked list data is obtained; For the list data, a storage tool is used to archive the low-purity group, the medium-purity group and the high-purity group, judge the quality stability distribution of each group, and obtain the final distribution list. 4.The green electricity coupled hydrogen energy micro-grid operation optimization method of claim 1, wherein: The S3 comprises: Obtain the device type identifier, purity requirement threshold value and running time window data from the device management interface, combine the pre-established historical demand record database, determine the priority ranking information of each device through the data comparison tool, and obtain the preliminary demand priority list; For the preliminary demand priority list, obtain the average quality stability data of the high-purity group, if the purity requirement threshold value of a device is higher than the average quality stability, mark it as a strict demand category, and obtain a device classification set containing the marked device; According to the device classification set, use the data integration tool to extract the fault tolerance range value of each device, combine the running time window information, judge the demand matching degree of the device in a specific time period through the time matching algorithm, and determine the final classification matching result; For the classification matching result, use the storage tool to archive the strict demand category and other categories, and obtain the device demand classification list containing the fault tolerance range value.

5. The green electricity coupled hydrogen energy microgrid operation optimization method of claim 1, wherein: The S4 comprises: Obtain the device type identifier, hydrogen consumption rate and geographical location distribution data from the device demand classification list, and extract the storage location information in the hydrogen category distribution list, compare the data through the data integration tool, and obtain the correspondence list of the device and the hydrogen category; For the correspondence list, use the data filtering tool to extract the association information of the device type identifier and the purity group classification, if the hydrogen consumption rate is higher than the preset threshold value, mark it as a high consumption category, and determine the high consumption category device list; According to the high consumption category device list, obtain the matching information of the geographical location distribution and the storage location, process the data through the location matching algorithm, judge whether it meets the preset matching range, and obtain the location matching result set; For the location matching result set, use the data calculation tool to combine the quantity proportion and stability parameters, if the stability parameter is lower than the preset threshold value, adjust the allocation scheme, and determine the final hydrogen allocation proportion list of each purity group.

6. The green electricity coupled hydrogen energy microgrid operation optimization method of claim 1, wherein, It also includes S5, judging whether the available inventory of the low-purity group in the allocation proportion scheme exceeds the total hydrogen consumption rate in the related device classification list; If it exceeds, trigger the purification module to perform purity improvement processing on the low-purity group according to the purity compensation coefficient, obtain the updated purity grade division and quality stability information, which specifically comprises: Obtain the hydrogen consumption rate data from the device classification list, and calculate the total consumption rate value through the data integration tool; Compare the total consumption rate value with the available inventory of the low-purity group, if the available inventory exceeds the total consumption rate value, mark the related batch through the data recording tool, and determine the to-be-processed inventory list; According to the to-be-processed inventory list, obtain the purity compensation coefficient data, use the parameter adjustment tool to perform purity improvement processing on the low-purity group inventory, and obtain the adjusted purity grade information; For the adjusted purity grade information, use the data verification tool to detect the quality stability of the processed inventory batch, if it meets the preset threshold range, determine the final quality stability information.

7. The green electric-coupled hydrogen energy microgrid operation optimization method of claim 1, wherein, S6, adjusting the updated purity grade division, the quantity proportion of each group, and the allocation proportion scheme through iterative calculation technology, generating a final hydrogen resource allocation list in combination with the operation time window and geographical location constraints, and transmitting the list to the microgrid control unit for scheduling, specifically including: Obtaining limit condition data from the operation time window and geographical location constraint database, comparing the limit condition data with the purity grade division information, and if the data meets the preset threshold range, generating a preliminary allocation proportion scheme through a data integration tool to obtain an initial allocation list; According to the initial allocation list, obtaining quantity proportion data and purity adjustment parameters, and using a parameter matching tool to dynamically adjust the allocation proportion scheme, if the adjusted proportion exceeds the preset threshold, recalculating through a data calibration tool to determine the adjusted allocation list. 8.The green electricity coupled hydrogen energy micro-grid operation optimization method of claim 7, wherein: The S6 further includes: Associating and matching the adjusted allocation list with the geographical location constraint information through a data transmission tool, and using a logical verification tool to detect the consistency of the matching result, if it meets the preset standard, generating a final hydrogen resource allocation list, and judging the completeness of the list; Obtaining the final hydrogen resource allocation list, and using a communication interface tool to transmit it to the microgrid control unit, and monitoring the transmission process in real time, if it meets the integrity requirement, forming a scheduling execution instruction through an instruction generation tool to determine the final execution data.

9. The green electricity coupled hydrogen energy microgrid operation optimization method of claim 7, wherein, S7, transmitting the final hydrogen resource allocation list to the microgrid control unit through a system simulation interface, and performing execution verification in combination with priority sorting and fault tolerance range values, if the energy efficiency does not reach the preset threshold, backtracking to the grouping processing link to re-adjust the purity grade division and the matching logic of the storage location information to obtain an optimized allocation proportion scheme, specifically including: Obtaining final hydrogen resource allocation list data from a pre-established database, comparing the list data with the priority sorting rules, if it meets the preset threshold range, performing integrity detection through a data verification tool to obtain verified allocation list data; According to the verified allocation list data, using a communication transmission tool to send it to the target unit, and performing data segmentation processing in combination with the fault tolerance range value during transmission to determine the transmission completed list record.

10. The green electricity coupled hydrogen energy microgrid operation optimization method of claim 9, wherein: The S7 further includes: For the transmission completed list record, matching the preset energy efficiency threshold through a logical comparison tool, if it does not meet the standard, triggering the backtracking processing logic to obtain the re-divided purity grade information; According to the re-divided purity grade information, using a parameter adjustment tool to dynamically match the storage location information and the allocation proportion scheme, and judging whether the optimized list meets the execution standard.

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