Intelligent cooperative control method and system for hydrogen water equipment

Abstract

The invention relates to the field of intelligent control, in particular to an intelligent cooperative control method and system for hydrogen water equipment. Comprising a management platform, and the management platform is in communication connection with a scene construction module, an equipment analysis module, an equipment control module and a scene equipment adjustment module: obtaining historical equipment data of hydrogen water equipment, establishing a standard water quality map, and generating a water quality scene map; the method comprises the following steps: acquiring real-time equipment operation data, analyzing hydrogen water equipment, constructing a state label, and establishing a response level in combination with the state label and the real-time equipment operation data; analyzing the water quality scene map and the state label containing the response level, generating an adjustment trigger node, responding to the collaborative score, and generating a multi-level collaborative control link; and performing cooperative control on the hydrogen water equipment through a multi-stage cooperative control link, collecting cooperative control result data, and adjusting the water quality scene atlas to obtain a water quality updating atlas. The stability of water quality response and the dynamic adaptability of equipment control can be improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, specifically to an intelligent collaborative control method and system for hydrogen water equipment. Background Technology

[0002] Hydrogen water equipment is a type of device that uses electrolysis technology to separate hydrogen and oxygen in water and generate hydrogen-rich water. It is widely used in household drinking water, health drinks, medical aids, and high-end water terminals. As people's awareness of water quality health and the health benefits of hydrogen increases, the market demand for hydrogen water equipment is gradually growing, and the requirements for equipment performance and water quality stability are also rising. In the operation of modern hydrogen water equipment, in addition to focusing on electrolysis efficiency and hydrogen concentration, it is also necessary to consider the coordinated control of multiple physicochemical indicators such as total dissolved solids (TDS), conductivity, and water temperature to ensure the uniformity and consistency of the generated water quality.

[0003] Chinese Patent Publication No. CN116356346A discloses a hydrogen production system and a control method for the hydrogen production system. The hydrogen production system includes: a hydrogen production device and a control device. The hydrogen production device is used to ionize water to produce hydrogen and oxygen. The control device is used to acquire a first actual pressure and a first actual liquid level corresponding to the oxygen-producing side of the hydrogen production device, and a second actual liquid level corresponding to the hydrogen-producing side, and to monitor the actual current change of the hydrogen production device in real time. The control device is also used to control the hydrogen production device based on the first actual pressure, the first actual liquid level, the second actual liquid level, and the actual current change to ensure the safety of the hydrogen production device. This ensures that the hydrogen production system maintains pressure and liquid level balance between the oxygen-producing side and the hydrogen-producing side during operation.

[0004] In existing technologies, start-stop control is usually achieved based on a single parameter threshold or static rules, which cannot fully characterize the relationship between different equipment operating indicators; it is difficult to achieve fine-grained status identification and control positioning in multi-path, multi-node water quality response scenarios; relying solely on traditional node-by-node strategies leads to untimely response and unbalanced control amplitude, which are the problems we need to solve. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the background technology by proposing an intelligent collaborative control method and system for hydrogen water equipment.

[0006] The technical solution of this invention: An intelligent collaborative control system for hydrogen water equipment, comprising a management platform, wherein the management platform is communicatively connected to a scene construction module, an equipment analysis module, an equipment control module, and a scene equipment adjustment module. The scenario construction module is used to acquire historical equipment data of hydrogen water equipment, establish a standard water quality map, map the currently collected water quality attribute data to the standard water quality map, and generate a water quality scenario map based on the standard water quality map. The equipment analysis module is used to acquire real-time equipment operation data of the hydrogen water equipment, analyze the hydrogen water equipment based on the water quality scene map, construct status tags for the hydrogen water equipment, and establish response levels corresponding to the status tags by combining the status tags and the real-time equipment operation data of the hydrogen water equipment. The equipment control module is used to analyze the water quality scene map and status labels containing response levels, generate adjustment trigger nodes and response coordination scores, and generate multi-level collaborative control links based on the adjustment trigger nodes and response coordination scores. The scene equipment adjustment module is used to coordinate the control of the hydrogen-water equipment through a multi-level collaborative control link, collect collaborative control result data, and adjust the water quality scene map to obtain an updated water quality map.

[0007] Preferably, the process of acquiring historical equipment data from hydrogen water equipment and establishing a standard water quality profile includes: Historical equipment data includes historical operating cycles and corresponding historical equipment operating data; historical equipment operating data includes water temperature, total dissolved solids, conductivity, electrolysis current, hydrogen production, and effluent concentration. Historical equipment operation data is analyzed, and hydrogen-water nodes are constructed using water temperature, total dissolved solids, conductivity, electrolysis current, hydrogen generation, and effluent concentration values ​​from the historical equipment operation data. The operation data of each historical equipment is stored in the hydrogen-water nodes. Statistical analysis methods are used to identify the relationship between the operation data of each historical equipment and the effluent concentration value, and directed hydrogen-water edges are constructed between hydrogen-water nodes. The corresponding hydrogen-water nodes are linked by the directed hydrogen-water edges to construct hydrogen-water paths, and the hydrogen-water paths are summarized to generate a standard water quality map.

[0008] Preferably, the process of mapping the currently collected water quality attribute data to a standard water quality map and generating a water quality scene map based on the standard water quality map includes: Each hydrogen-water directed edge is assigned an edge weight parameter, which is calculated based on the attribute correlation coefficient from historical equipment operation data. The currently collected water quality attribute data is mapped to the corresponding hydrogen-water nodes in the standard water quality map. Based on the graph structure path in the standard water quality map and the edge weight parameters of each path, the water quality attribute data includes real-time water temperature, real-time total dissolved solids, real-time conductivity, real-time electrolysis current, real-time hydrogen production, and real-time water concentration. The path score is obtained by weighting the deviation between historical equipment operation data and water quality attribute data within each hydrogen-water node and the edge weight parameters of the directed hydrogen-water edge. The hydrogen-water path with the lowest path score is selected as the reference path in the current operation cycle, and a water quality scenario map is generated. The deviation refers to the absolute value of the corresponding numerical difference between historical equipment operation data and water quality attribute data within the hydrogen-water node. The path score is used to evaluate the correlation of the hydrogen-water path with the concentration response under the current water quality conditions.

[0009] Preferably, the process of analyzing the hydrogen water equipment by combining water quality attribute data and water quality scene maps to construct status labels for the hydrogen water equipment; and establishing the response level corresponding to the status labels by combining the status labels and the real-time equipment operation data of the hydrogen water equipment, is as follows: The system calculates the real-time water temperature, total dissolved solids, conductivity, and electrolysis current values ​​of the water quality attributes during the current operating cycle, as well as the water temperature change rate, TDS change rate, conductivity change rate, and electrolysis current change rate. It also collects the number of electrolysis start-ups of the hydrogen water equipment. Based on a preset time window, the difference between the maximum and minimum values ​​of the effluent concentration data sequence is calculated to obtain the concentration change intensity value of the current operating cycle; the number of electrolysis starts of the hydrogen water equipment within the same period is counted to obtain the electrolysis frequency value; and the water temperature change rate, TDS change rate, conductivity change rate and electrolysis current change rate are linearly fitted together with the time series to obtain the trend slope as the trend fluctuation factor. The hydrogen-water equipment is analyzed based on the concentration change intensity, electrolysis frequency, and trend fluctuation factor to generate status tags. These status tags include Status Tag 1, Status Tag 2, Status Tag 3, and Status Tag 4. The management platform has a preset status response mapping table, in which each status tag corresponds to a response level and its parameter group. Based on the status tag, the corresponding preset response level item is retrieved within the management platform to establish the response level parameter group corresponding to the current status tag, thus obtaining the response level corresponding to the status tag.

[0010] Preferably, the process of analyzing water quality scene maps and status labels containing response levels to generate adjustment trigger nodes and response coordination scores includes: Obtain the hydrogen-water path of the water quality scene map within the current operating cycle and the water quality attribute data of each hydrogen-water node in the hydrogen-water path. Obtain the historical average of each water quality attribute data corresponding to each hydrogen-water node in the historical multiple operating cycles. Calculate the absolute value of the difference between the water quality attribute data and the historical average, and record it as the deviation value. Match the deviation value of each hydrogen-water node with the adjustment trigger range stored in the response level parameter group associated with the status label. If the deviation value exceeds its corresponding trigger threshold range, then the hydrogen-water node is designated as the adjustment trigger node, and control adjustment suggestions for the hydrogen-water node are generated in combination with the control strategy field in the response level parameter group. All adjustment trigger nodes with control adjustment suggestions are paired up, and the deviation direction, deviation magnitude and corresponding trend slope value between any two adjustment trigger nodes are set; the correlation judgment conditions include correlation judgment condition one, correlation judgment condition two and correlation judgment condition three. When at least two of the association determination conditions are met simultaneously, it is determined that the corresponding adjustment trigger node pair has a collaborative control relationship; a collaborative edge is established for the corresponding adjustment trigger node pair in the hydrogen-water path of the water quality scene map, and the number of satisfied conditions is recorded as the response collaborative score of the adjustment trigger node pair.

[0011] Preferably, the process of generating a multi-level collaborative control link based on adjusting the trigger node and the response collaborative score includes: Based on the response coordination score, a coordination control relationship matrix between nodes is constructed on the path structure. The coordination control relationship matrix uses each hydrogen-water node in the hydrogen-water path as row and column elements, and the response coordination score is the correlation strength. If the response coordination score is greater than the preset coordination threshold, it is marked as an effective linkage relationship in the coordination control relationship matrix. The node with the strongest coordination relationship in the coordination control relationship matrix is ​​identified as the main path node, and the remaining nodes are marked as slave path nodes in descending order of score. Based on the preset adjustment priority, master-slave path node relationship, and hydrogen-water path in the response level, a control linkage relationship is established between the master path node and the slave path node. Through the control linkage relationship, a multi-level collaborative control link between the master path node and the slave path node is generated.

[0012] Preferably, the process of coordinating the control of the hydrogen-water equipment through a multi-level collaborative control link, collecting collaborative control result data, and adjusting the water quality scene map to obtain an updated water quality map includes: For hydrogen-water devices that have implemented multi-level collaborative control links, control execution data and water quality response data within the current operating cycle are collected. The control execution data and the corresponding water quality response data are correlated, and the values ​​of each hydrogen-water node in the current path are compared to identify the hydrogen-water nodes and hydrogen-water paths that have changed. For hydrogen-water nodes whose data has changed, the changed values ​​are directly stored in the corresponding hydrogen-water nodes, and the state markers of the directed edges of hydrogen-water nodes connected to the hydrogen-water nodes are updated. The updated graph is recorded as the water quality update graph.

[0013] This invention also discloses an intelligent collaborative control method for hydrogen water equipment, comprising the following steps: S1. Obtain historical equipment data of the hydrogen water device and establish a standard water quality map; map the currently collected water quality attribute data into the standard water quality map, and generate a water quality scene map based on the standard water quality map; S2. Obtain real-time equipment operation data of the hydrogen water equipment, analyze the hydrogen water equipment based on the water quality scene map, construct the status label of the hydrogen water equipment, and establish the response level corresponding to the status label by combining the status label and the real-time equipment operation data of the hydrogen water equipment. S3. Analyze the water quality scene map and status labels containing response levels, generate adjustment trigger nodes and response coordination scores, and generate multi-level collaborative control links based on the adjustment trigger nodes and response coordination scores. S4. The hydrogen-water equipment is controlled collaboratively through a multi-level collaborative control link. The collaborative control result data is collected, and the water quality scene map is adjusted to obtain an updated water quality map.

[0014] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects: by constructing a standard water quality map and a water quality scenario map, and introducing a hydrogen-water path and node deviation analysis mechanism, it is possible to accurately identify and classify the operating status of hydrogen-water equipment, and depict the dynamic correlation of equipment operation; through the response level parameter and adjustment trigger node collaborative scoring mechanism, a multi-level collaborative control link between master and slave path nodes is constructed to realize the linkage control and closed-loop regulation between hydrogen-water equipment, improve the stability of water quality response and the dynamic adaptability of equipment control, and improve the response speed and control accuracy of the equipment. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of one embodiment of the present invention. Detailed Implementation

[0016] Example 1, as Figure 1As shown, the present invention proposes an intelligent collaborative control system for hydrogen water equipment, including a management platform. The management platform is communicatively connected to a scene construction module, an equipment analysis module, an equipment control module, and a scene equipment adjustment module. The scenario construction module is used to acquire historical equipment data of hydrogen water equipment, establish a standard water quality map, map the currently collected water quality attribute data to the standard water quality map, and generate a water quality scenario map based on the standard water quality map. The equipment analysis module is used to acquire real-time equipment operation data of the hydrogen water equipment, analyze the hydrogen water equipment based on the water quality scene map, construct status tags for the hydrogen water equipment, and establish response levels corresponding to the status tags by combining the status tags and the real-time equipment operation data of the hydrogen water equipment. The equipment control module is used to analyze the water quality scene map and status labels containing response levels, generate adjustment trigger nodes and response coordination scores, and generate multi-level collaborative control links based on the adjustment trigger nodes and response coordination scores. The scene equipment adjustment module is used to coordinate the control of the hydrogen-water equipment through a multi-level collaborative control link, collect collaborative control result data, and adjust the water quality scene map to obtain an updated water quality map.

[0017] It should be further explained that, in the specific implementation process, the process of acquiring historical equipment data of the hydrogen water device and establishing a standard water quality map, mapping the currently collected water quality attribute data to the standard water quality map, and generating a water quality scene map based on the standard water quality map is as follows: The historical equipment data includes historical operating cycles and their corresponding historical equipment operating data; the historical equipment operating data refers to the operating data of the hydrogen water equipment during historical time, including water temperature, total dissolved solids, conductivity, electrolysis current, hydrogen production, and effluent concentration. Historical equipment operation data is analyzed, and hydrogen-water nodes are constructed using water temperature, total dissolved solids (TDS), conductivity, electrolysis current, hydrogen generation, and effluent concentration values ​​from the historical equipment operation data. These hydrogen-water nodes include water temperature, TDS, conductivity, electrolysis current, hydrogen generation, and concentration response nodes. The historical equipment operation data is stored in the hydrogen-water nodes. Statistical analysis methods are used to identify the relationship between the historical equipment operation data and the effluent concentration values, and directed hydrogen-water edges are constructed between the hydrogen-water nodes. The corresponding hydrogen-water nodes are linked through these directed edges to construct hydrogen-water paths, and the hydrogen-water paths are summarized to generate a standard water quality map.

[0018] Each hydrogen-water directed edge is assigned an edge weight parameter, which is calculated based on the attribute correlation coefficient from historical equipment operation data. Specifically, the Pearson correlation coefficient between each water quality attribute data and the effluent concentration value is calculated. The value of the correlation coefficient is used as the edge weight parameter between the corresponding nodes to characterize the influence of the water quality attribute data on the concentration response. The currently collected water quality attribute data are mapped to the corresponding hydrogen-water nodes in the standard water quality map, forming a subset of nodes with the characteristics of the current water quality state. Based on the graph structure path and edge weight parameters of each path in the standard water quality map, the water quality attribute data refers to the data related to the water quality state collected by the hydrogen-water device during the current operating cycle, including but not limited to real-time water temperature, real-time total dissolved solids, real-time conductivity, real-time electrolysis current, real-time hydrogen generation, and real-time water concentration. The path score is obtained by weighting the deviation between historical equipment operation data and water quality attribute data within each hydrogen-water node and the edge weight parameters of the directed hydrogen-water edge. The deviation refers to the absolute value of the corresponding numerical difference between historical equipment operation data and water quality attribute data within the hydrogen-water node. The path score is used to evaluate the correlation between the hydrogen-water path and the concentration response caused by the current water quality conditions. The hydrogen-water path with the lowest path score is selected as the reference path in the current operating cycle, and a water quality scenario map is generated.

[0019] It should be further explained that, in the specific implementation process, the process of acquiring real-time equipment operation data of the hydrogen water device, analyzing the hydrogen water device based on the water quality scene map, constructing status tags for the hydrogen water device, and establishing the response level corresponding to the status tags by combining the status tags and the real-time equipment operation data of the hydrogen water device is as follows: The system calculates the real-time water temperature, total dissolved solids, conductivity, and electrolysis current values ​​of the water quality attributes during the current operating cycle, as well as the water temperature change rate, TDS change rate, conductivity change rate, and electrolysis current change rate. It also collects the number of electrolysis start-ups of the hydrogen water equipment. Based on a preset time window, the difference between the maximum and minimum values ​​of the effluent concentration data sequence is calculated to obtain the concentration change intensity value of the current operating cycle; the number of electrolysis starts of the hydrogen water equipment within the same period is counted to obtain the electrolysis frequency value; and the water temperature change rate, TDS change rate, conductivity change rate and electrolysis current change rate are linearly fitted together with the time series to obtain the trend slope as the trend fluctuation factor. The hydrogen-water equipment is analyzed based on the concentration change intensity, electrolysis frequency, and trend fluctuation factor to generate status labels. These status labels include Status Label 1, Status Label 2, Status Label 3, and Status Label 4. Status Label 1 is generated when the concentration change intensity is below a preset first threshold and the trend fluctuation factor is below a preset second threshold. Status Label 2 is generated when the concentration change intensity is between the preset first and second thresholds and the trend fluctuation factor is between the preset second and third thresholds. Status Label 3 is generated when the concentration change intensity is above the preset second threshold and the trend fluctuation factor is above the third threshold. Status Label 4 is generated when the concentration change direction is opposite to the control direction or the electrolysis frequency increases significantly while the water concentration does not recover under the control direction. It should be noted that the direction of concentration change refers to the direction of concentration increase and decrease; the control direction refers to the direction of concentration to be controlled, including controlling increase and controlling decrease; the first threshold, the second threshold, and the third threshold are stored in the management platform to support online adjustment and dynamic calibration to adapt to different water source environments or equipment aging conditions.

[0020] The management platform has a preset status response mapping table. Each status label in the status response mapping table corresponds to a response level and its parameter group. Based on the status label, the corresponding preset response level item is retrieved in the management platform, the response level parameter group corresponding to the current status label is established, and the response level corresponding to the status label is obtained.

[0021] It should be further explained that, in the specific implementation process, the process of analyzing the water quality scene map and the status labels containing response levels to generate adjustment trigger nodes and response coordination scores, and then generating a multi-level coordinated control link based on the adjustment trigger nodes and response coordination scores, is as follows: The process involves acquiring the hydrogen-water path of the water quality scene map within the current operating cycle, as well as the water quality attribute data of each hydrogen-water node within that path. It also involves obtaining the historical average of each water quality attribute data corresponding to each hydrogen-water node over multiple historical operating cycles, calculating the absolute value of the difference between the water quality attribute data and the historical average, and recording this as the deviation value. The deviation value of each hydrogen-water node is then matched with the adjustment trigger range stored in the response level parameter group associated with the status label. If the deviation value exceeds its corresponding trigger threshold range, the hydrogen-water node is recorded as an adjustment trigger node. Furthermore, a control adjustment suggestion for the hydrogen-water node is generated based on the control strategy field in the response level parameter group. This control adjustment suggestion includes the adjustment direction and the corresponding adjustment object and target adjustment amplitude. The adjustment direction includes increasing and decreasing; the adjustment object includes current and voltage. All adjustment trigger nodes with control adjustment suggestions are paired up, and the deviation direction, deviation magnitude, and corresponding trend slope value between any two adjustment trigger nodes are analyzed; the deviation direction includes positive deviation and negative deviation; the deviation magnitude refers to the absolute value of the numerical difference. The association determination conditions are set, including association determination condition one, association determination condition two, and association determination condition three. Association determination condition one is that if the deviation directions of the two adjustment trigger nodes are both positive or both are negative, they are considered to be the same direction. Association determination condition two is that if the difference in the deviation amplitude of the two adjustment trigger nodes is less than a set amplitude threshold, it indicates that the deviation amplitude is close. Association determination condition three is that if the difference in the trend slope values ​​of the two adjustment trigger nodes is less than a set trend slope threshold, it indicates that the trend changes are close. When at least two of the association determination conditions are met simultaneously, a collaborative edge is established for the corresponding adjustment trigger node pair in the hydrogen-water path of the water quality scene map, and the number of satisfied conditions is recorded as the response collaborative score of the adjustment trigger node pair. Based on the response coordination score, a coordination control relationship matrix between nodes is constructed on the path structure. The coordination control relationship matrix uses each hydrogen-water node in the hydrogen-water path as row and column elements, and the response coordination score is the association strength. If the response coordination score is greater than a preset coordination threshold, it is marked as an effective linkage relationship in the coordination control relationship matrix. The node with the strongest coordination relationship in the coordination control relationship matrix is ​​identified as the main path node, and the remaining nodes are marked as slave path nodes in descending order of their scores. Based on the preset adjustment priority, master-slave path node relationship and hydrogen-water path in the response level, establish the control linkage relationship between the master path node and the slave path node, and generate a multi-level collaborative control link between the master path node and the slave path node through the control linkage relationship. It should be noted that each node in the multi-level collaborative control link executes the corresponding voltage or current adjustment command according to the control adjustment suggestion, and feeds back the adjustment response to the water quality scenario map after each round of adjustment.

[0022] It should be further explained that, in the specific implementation process, the hydrogen-water equipment is controlled collaboratively through a multi-level collaborative control link. The collaborative control result data is collected, and the water quality scene map is adjusted to obtain an updated water quality map. The process is as follows: For hydrogen-water equipment that has implemented a multi-level collaborative control link, control execution data and water quality response data are collected during the current operating cycle. The control execution data includes the voltage amplitude, current adjustment ratio, and execution sequence set in the control command; the water quality response data includes the trend of changes in effluent concentration, water temperature, conductivity, TDS, and the trajectory of changes in electrolysis current. The control execution data is associated with the corresponding water quality response data. The values ​​of each hydrogen-water node in the current path are compared to identify the hydrogen-water nodes and hydrogen-water paths that have changed. For hydrogen-water nodes whose data has changed, the changed values ​​are directly stored in the corresponding hydrogen-water nodes, and the state markers of the directed edges of hydrogen-water nodes connected to the hydrogen-water nodes are updated. The updated graph is recorded as the water quality update graph.

[0023] Example 2: The intelligent collaborative control method for hydrogen water equipment proposed in this invention is applied to the intelligent collaborative control system for hydrogen water equipment described in Example 1, and includes the following steps: S1. Obtain historical equipment data of the hydrogen water device and establish a standard water quality map; map the currently collected water quality attribute data into the standard water quality map, and generate a water quality scene map based on the standard water quality map; S2. Obtain real-time equipment operation data of the hydrogen water equipment, analyze the hydrogen water equipment based on the water quality scene map, construct the status label of the hydrogen water equipment, and establish the response level corresponding to the status label by combining the status label and the real-time equipment operation data of the hydrogen water equipment. S3. Analyze the water quality scene map and status labels containing response levels, generate adjustment trigger nodes and response coordination scores, and generate multi-level collaborative control links based on the adjustment trigger nodes and response coordination scores. S4. The hydrogen-water equipment is controlled collaboratively through a multi-level collaborative control link. The collaborative control result data is collected, and the water quality scene map is adjusted to obtain an updated water quality map.

[0024] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A hydrogen water equipment intelligent collaborative control system comprising a management platform, characterized in that, The management platform is in communication connection with a scene construction module, a device analysis module, a device control module and a scene device adjustment module; The scene construction module is configured to acquire historical device data of the hydrogen water device, establish a standard water quality graph, map the current collected water quality attribute data into the standard water quality graph, and generate a water quality scene graph based on the standard water quality graph; The device analysis module is configured to acquire real-time device running data of the hydrogen water device, analyze the hydrogen water device based on the water quality scene graph, construct a state label of the hydrogen water device, establish a response level corresponding to the state label in combination with the state label and the real-time device running data of the hydrogen water device; The device control module is configured to analyze the water quality scene graph and the state label containing the response level, generate an adjustment trigger node and a response coordination score, and generate a multi-level coordination control link based on the adjustment trigger node and the response coordination score; The scene device adjustment module is configured to perform coordinated control on the hydrogen water device through the multi-level coordination control link, collect coordinated control result data, and adjust the water quality scene graph to obtain a water quality update graph.

2. The intelligent collaborative control system of a hydrogen water device according to claim 1, characterized in that, The process of acquiring historical device data of the hydrogen water device and establishing a standard water quality graph includes: The historical device data includes a historical running period and corresponding historical device running data; the historical device running data includes water temperature value, total dissolved solid value, conductivity value, electrolysis current value, hydrogen generation amount and effluent concentration value; The historical device running data is analyzed, hydrogen water nodes are constructed respectively through the water temperature value, total dissolved solid value, conductivity value, electrolysis current value, hydrogen generation amount and effluent concentration value in the historical device running data, each historical device running data is stored in the hydrogen water node, a statistical analysis method is used to identify the relationship between each historical device running data and the effluent concentration value, and hydrogen water directed edges between the hydrogen water nodes are constructed; the corresponding hydrogen water nodes are linked through the hydrogen water directed edges, a hydrogen water path is constructed, and the hydrogen water path is summarized to generate a standard water quality graph.

3. The intelligent collaborative control system of a hydrogen water device according to claim 2, characterized in that, The process of mapping the current collected water quality attribute data into the standard water quality graph and generating a water quality scene graph based on the standard water quality graph includes: Each hydrogen water directed edge is assigned an edge weight parameter, which is calculated based on the attribute correlation coefficient of the historical device running data; the current collected water quality attribute data is mapped into the corresponding hydrogen water node in the standard water quality graph, and the water quality attribute data includes real-time water temperature value, real-time total dissolved solid value, real-time conductivity value, real-time electrolysis current value, real-time hydrogen generation amount and real-time water concentration value based on the graph structure path in the standard water quality graph and the edge weight parameter of the edge on the path; The path score is obtained by weighted calculation according to the deviation degree between the historical device running data and the water quality attribute data in each hydrogen water node and the edge weight parameter of the hydrogen water directed edge; the hydrogen water path with the lowest path score is selected as the reference path in the current running period to generate a water quality scene graph; the deviation degree refers to the absolute value of the difference between the corresponding numerical values in the historical device running data and the water quality attribute data in the hydrogen water node; the path score is used to evaluate the correlation of the hydrogen water path in triggering concentration response under the current water quality condition.

4. The intelligent collaborative control system of a hydrogen water device according to claim 3, characterized in that, The water quality attribute data and the water quality scene atlas are combined to analyze the hydrogen water equipment, and a state label of the hydrogen water equipment is constructed; the state label and real-time equipment operation data of the hydrogen water equipment are combined to establish a process of a response level corresponding to the state label: The real-time water temperature value, the real-time total dissolved solid value, the real-time conductivity value and the real-time electrolysis current value of the water quality attribute data in the current operation cycle are calculated, the water temperature change rate, the TDS change rate, the conductivity change rate and the electrolysis current change rate are calculated, and the electrolysis start number of the hydrogen water equipment is collected; Based on the preset timing window, the maximum and minimum value difference of the outlet concentration data sequence is calculated to obtain the concentration change intensity value of the current operation cycle; The electrolysis start number of the hydrogen water equipment in the same period is counted to obtain the electrolysis frequency value; the water temperature change rate, the TDS change rate, the conductivity change rate and the electrolysis current change rate are linearly fitted based on the time sequence, and the trend slope is obtained as a trend fluctuation factor; The hydrogen water equipment is analyzed according to the concentration change intensity value, the electrolysis frequency value and the trend fluctuation factor, and a state label is generated, which includes state label one, state label two, state label three and state label four; a state response mapping table is preset in the management platform, and each state label in the state response mapping table corresponds to a response level and its parameter group; Based on the state label, the response level preset corresponding to the state label is searched in the management platform, the response level parameter group corresponding to the current state label is established, and the response level corresponding to the state label is obtained.

5. The intelligent collaborative control system of a hydrogen water device according to claim 4, characterized in that, The water quality scene atlas and the state label containing the response level are analyzed to generate an adjustment trigger node and a response coordination score, and the process includes: The hydrogen water path of the water quality scene atlas in the current operation cycle and the water quality attribute data of each hydrogen water node in the hydrogen water path are obtained, and the historical average value of each water quality attribute data of each hydrogen water node in the historical multiple operation cycles is obtained, the absolute value of the difference between the water quality attribute data and the historical average value is calculated, which is recorded as a deviation value; the deviation value of each hydrogen water node is matched with the adjustment trigger interval stored in the response level parameter group associated with the state label, if the deviation value exceeds the corresponding trigger threshold range, the hydrogen water node is an adjustment trigger node, and a control adjustment suggestion of the hydrogen water node is generated combined with the control strategy field in the response level parameter group; All adjustment trigger nodes with control adjustment suggestions are combined two by two, the deviation direction, the deviation amplitude and the corresponding trend slope value between any two adjustment trigger nodes are obtained; the associated determination conditions are set, which include associated determination condition one, associated determination condition two and associated determination condition three; When at least two of the associated determination conditions are met at the same time, it is determined that the corresponding adjustment trigger node pair has a coordinated control relationship; a coordination edge is established for the corresponding adjustment trigger node pair in the hydrogen water path of the water quality scene atlas, and the number of satisfied items is recorded as the response coordination score of the adjustment trigger node pair.

6. The intelligent collaborative control system of a hydrogen water device according to claim 5, wherein, Based on the adjustment trigger node and the response coordination score, a process of generating a multi-level coordinated control link includes: According to the response synergy score, a synergy control relationship matrix between nodes is constructed on the path structure, the synergy control relationship matrix takes each hydrogen water node in the hydrogen water path as a row and column element, and the response synergy score is the correlation strength; if the response synergy score is greater than a preset synergy threshold value, the synergy control relationship matrix is marked as an effective linkage relationship; the node with the strongest synergy relationship in the synergy control relationship matrix is identified as a master path node, and the remaining nodes are sequentially marked as slave path nodes according to the score from high to low; According to the preset adjustment priority in the response level, the master-slave path node relationship and the hydrogen water path, the regulation and control linkage relationship between the master path node and the slave path node is established, and the multi-level synergy control link between the master path node and the slave path node is generated through the regulation and control linkage relationship.

7. The intelligent collaborative control system of a hydrogen water device according to claim 6, wherein, Through the multi-level synergy control link, the hydrogen water equipment is synergistically controlled, the synergy control result data is collected, and the water quality scene graph is adjusted to obtain the water quality update graph. The process includes: After executing the multi-level synergy control link, the control execution data and water quality response data in the current operation period are collected, the control execution data and the corresponding water quality response data are associated, the values of each hydrogen water node in the current path are compared, and the hydrogen water node and the hydrogen water path that have changed are identified; for the hydrogen water node that has changed data, the changed value is directly stored in the corresponding hydrogen water node, and the state mark of the hydrogen water directed edge connected with the hydrogen water node is updated; the graph after the update is recorded as the water quality update graph. 8.The intelligent collaborative control method of a hydrogen water device according to any one of claims 1 to 7, characterized in that, The process includes the following steps: S1, obtain the historical equipment data of the hydrogen water equipment, establish a standard water quality graph, map the current collected water quality attribute data to the standard water quality graph, and generate a water quality scene graph based on the standard water quality graph; S2, obtain the real-time equipment operation data of the hydrogen water equipment, analyze the hydrogen water equipment based on the water quality scene graph, construct a state label of the hydrogen water equipment, and establish a response level corresponding to the state label based on the state label and the real-time equipment operation data of the hydrogen water equipment; S3, analyze the water quality scene graph and the state label containing the response level, generate an adjustment trigger node and a response synergy score, and generate a multi-level synergy control link based on the adjustment trigger node and the response synergy score; S4, synergistically control the hydrogen water equipment through the multi-level synergy control link, collect the synergy control result data, and adjust the water quality scene graph to obtain the water quality update graph.

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