Hydrogen purification method based on big data analysis and electronic device

Abstract

The application is suitable for the technical field of device control, and provides a hydrogen purification method based on big data analysis and an electronic device, which comprises the following steps: acquiring real-time collection data of raw material gas in a hydrogen production cavity; the real-time collection data comprises first component information and flow data of the raw material gas; performing data analysis on the real-time collection data through a preset supervision model to obtain a valve control script corresponding to the hydrogen production cavity; controlling each valve to switch according to the valve switching time sequence, so that the raw material gas is adsorbed by an adsorber corresponding to each valve; and collecting the raw material gas obtained after being adsorbed by all the valves to obtain target hydrogen gas with a preset purity. The above method can improve the adsorption effect of the adsorber corresponding to each valve, improve the purity of hydrogen gas in the finished gas, improve the accuracy of hydrogen production management, and improve the production efficiency of hydrogen gas without repeatedly adsorbing impurities.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of device control, and particularly relates to a hydrogen purification method based on big data analysis and an electronic device. BACKGROUND

[0002] As an important raw material in industrial applications, how to manufacture high-purity hydrogen has become the focus of attention. The existing hydrogen production and purification technology generally adopts a pressure swing adsorption (PSA) process. In the PSA process, a large number of valves need to be accurately and quickly switched to control, so that the raw gas is cyclically adsorbed by multiple adsorbers to achieve the purpose of hydrogen purification. This requires the control system to accurately switch the state of a large number of valves and ensure the reliability and stability of the switching process.

[0003] In the existing PSA process, the control technology of the valves generally adjusts the starting state of each valve based on a preset time sequence by workers. However, in actual production, changes in the composition, flow rate and other parameters of the raw gas will affect the control of the internal components of the hydrogen production cavity. If the same script is used to control each valve, the accuracy of valve control may be reduced, thereby affecting the purity of the finished gas in the hydrogen production and purification process, reducing production efficiency and product quality. SUMMARY

[0004] The embodiments of the application provide a hydrogen purification method based on big data analysis and an electronic device, which can solve the problem that in the existing PSA process, the control technology of the valves generally adjusts the starting state of each valve based on a preset time sequence by workers. However, in actual production, changes in the composition, flow rate and other parameters of the raw gas will affect the control of the internal components of the hydrogen production cavity. If the same script is used to control each valve, the accuracy of valve control may be reduced, thereby affecting the purity of the finished gas in the hydrogen production and purification process, reducing production efficiency and product quality.

[0005] In a first aspect, the embodiments of the application provide a hydrogen purification method based on big data analysis, comprising:

[0006] obtaining real-time collection data of raw gas in a hydrogen production cavity; the real-time collection data includes first component information and flow rate data of the raw gas; the hydrogen production cavity contains a plurality of valves;

[0007] perform data analysis on the real-time acquisition data through a preset supervision model to obtain a valve control script corresponding to the hydrogen production cavity; the supervision model is obtained through big data analysis based on historical data of the hydrogen production cavity; and the valve control script is used to determine a valve switching timing of the hydrogen production cavity;

[0008] control each valve to switch according to the valve switching timing, so that the impurities in the raw gas are adsorbed by the adsorber corresponding to each valve;

[0009] acquire the raw gas obtained after being adsorbed by all the valves to obtain target hydrogen gas with a preset purity.

[0010] In a possible implementation manner of the first aspect, the control of each valve to switch according to the valve switching timing, so that the impurities in the raw gas are adsorbed by the adsorber corresponding to each valve, includes:

[0011] for the adsorber of the Nth valve, an adsorption calibration coefficient is determined according to second component information of the raw gas when flowing through the (N-1)th valve and a first adsorption parameter corresponding to the (N-1)th valve; N is a positive integer greater than 1 and not greater than the total number of valves of the hydrogen production cavity; and the adsorption calibration coefficient is:

[0012]

[0013] wherein, Adsob adj (N) is the adsorption calibration coefficient; Adsob(N-1) is the first adsorption parameter; Compent i [N-1] is a concentration value of the i-th gas in the second component information; BaseCom i is an expected concentration value of the i-th gas when the raw gas flows through the (N-1)th valve; M is the total number of gases in the second component information; P is the total number of valves; and Adsob(j) is an adsorption parameter corresponding to the jth valve;

[0014] the expected adsorption parameter of the Nth valve is calibrated according to the adsorption calibration coefficient to obtain a second adsorption parameter of the Nth valve;

[0015] when the raw gas flows through the Nth valve, the adsorber is controlled to perform adsorption operation on the raw gas according to the second adsorption parameter.

[0016] In a possible implementation manner of the first aspect, for the adsorber of the Nth valve, an adsorption calibration coefficient is determined according to second component information of the raw gas when flowing through the (N-1)th valve and a first adsorption parameter corresponding to the (N-1)th valve, including:

[0017] According to the number of component detection modules deployed in the middle of the adsorption area corresponding to the N-1 valve, a plurality of sub-threads corresponding to the number of modules are created; each sub-thread corresponds to one of the component detection modules;

[0018] The original collection data corresponding to each of the component detection modules is obtained through each sub-thread respectively, and the original collection data corresponding to each sub-thread is processed in parallel to obtain the second component information.

[0019] In a possible implementation of the first aspect, before determining the adsorption calibration coefficient of the adsorber corresponding to the Nth valve according to the second component information of the raw material gas flowing through the N-1th valve and the first adsorption parameter corresponding to the N-1th valve, the method further includes:

[0020] According to the expected adsorption parameter corresponding to the Nth valve, the running state of the adsorber of the Nth valve is set when the raw material gas flows through the N-1th valve; the expected adsorption parameter includes a plurality of dimension indicators; the dimension indicators include: gas pressure value, adsorption efficiency and regeneration efficiency;

[0021] Correspondingly, the adsorption operation of the adsorber on the raw material gas according to the second adsorption parameter when the raw material gas flows through the Nth valve includes:

[0022] The deviation value of the expected adsorption parameter and the second adsorption parameter corresponding to each dimension indicator is calculated;

[0023] Based on the order from large to small of the deviation value corresponding to each dimension indicator, the adjustment order corresponding to each dimension indicator is determined;

[0024] Based on the adjustment order, the running state of the adsorber corresponding to each dimension indicator is adjusted in turn.

[0025] In a possible implementation of the first aspect, before the data analysis of the real-time collection data by the preset supervision model to obtain the valve control script corresponding to the hydrogen production cavity, the method further includes:

[0026] The test running data corresponding to each valve of the hydrogen production cavity is obtained; the test running data includes the state switching time length and the sealing coefficient of the valve when the test result is obtained before the hydrogen production starts;

[0027] The first switching time sequence curve and the second sealing time sequence curve corresponding to each valve are constructed by the historical running data corresponding to the hydrogen production cavity;

[0028] determine a first slope deviation between the first switching timing curve and the first coordinate according to a first coordinate corresponding to a first coordinate system corresponding to the state switching duration; the first slope deviation is used to adjust a timing factor in the supervised model;

[0029] determine a second slope deviation between the second sealing timing curve and the second coordinate according to a second coordinate corresponding to a second coordinate system corresponding to the sealing coefficient; the second slope deviation is used to adjust a valve thrust factor in the supervised model;

[0030] calibrate the supervised model according to the first slope deviation and the second slope deviation corresponding to all valves.

[0031] In a possible implementation manner of the first aspect, before the calibration of the supervised model according to the first slope deviation and the second slope deviation, the method further includes:

[0032] calculate an aging coefficient corresponding to each valve according to the second slope deviation and the first slope deviation;

[0033] if any of the aging coefficients is greater than a preset aging threshold, generate first abnormal information corresponding to the valve.

[0034] In a possible implementation manner of the first aspect, after the collection of the raw material gas obtained after the adsorption of all the valves to obtain target hydrogen gas with a preset purity, the method further includes:

[0035] collect an air flow rate corresponding to the raw material gas output after the adsorption of all the valves;

[0036] if the air flow rate is greater than a preset flow rate threshold, generate second abnormal information of valve air leakage.

[0037] Secondly, the present application provides a hydrogen purification device based on big data analysis, which comprises:

[0038] a real-time collection unit configured to obtain real-time collection data of raw material gas in a hydrogen production cavity; the real-time collection data comprises first component information and flow data of the raw material gas; the hydrogen production cavity comprises a plurality of valves;

[0039] a valve control script generation unit configured to perform data analysis on the real-time collection data by using a preset supervised model to obtain a valve control script corresponding to the hydrogen production cavity; the supervised model is obtained by big data analysis based on historical data of the hydrogen production cavity; the valve control script is used to determine a valve switching timing of the hydrogen production cavity;

[0040] a valve control unit configured to control each of the valves to switch according to the valve switching time sequence, so that the raw material gas is adsorbed by the corresponding adsorber of each of the valves.

[0041] a purification unit configured to collect the raw material gas after being adsorbed by all the valves, so as to obtain the target hydrogen gas with a preset purity.

[0042] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method of any one of the above first aspect when executing the computer program.

[0043] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executable on a processor to implement the method of any one of the above first aspect.

[0044] In a fifth aspect, a computer program product is provided, which, when executed on a UAV, causes the UAV to implement the method of any one of the above first aspect.

[0045] Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows: by collecting real-time collection data of the raw material gas in the process of hydrogen production and purification, and importing the real-time collection data into a supervised learning algorithm to generate a valve control script, the valve control script is consistent with the actual situation in the cavity, thereby improving the accuracy of valve control and the matching degree with the hydrogen production scene, and each valve is controlled to switch according to the valve control script, and the impurities in the raw material gas are adsorbed by the corresponding adsorber, so as to improve the purity of hydrogen in the raw material gas, and after the adsorption operation of the corresponding adsorber of all the valves, the corresponding raw material gas is output as a finished gas to obtain the target hydrogen gas with a preset purity, so as to achieve the purpose of hydrogen purification. Compared with the existing hydrogen production technology, in the embodiments of the present application, the control of the valve is not controlled by fixed time sequence logic, but in the process of hydrogen production and purification, the real-time collection data in the current cavity is imported into a supervised model to generate a valve control script matched therewith, so as to improve the matching degree between the valve control script and the actual scene, thereby improving the accuracy of valve control, and thereby improving the adsorption effect of each valve corresponding to the adsorber, so as to improve the purity of hydrogen in the finished gas, and also improving the accuracy of hydrogen production management, without repeatedly adsorbing impurities, thereby improving the production efficiency of hydrogen. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0047] Figure 1 is a structural schematic diagram of a hydrogen production and purification system provided by an embodiment of the present application;

[0048] Figure 2 is an implementation schematic diagram of a hydrogen production and purification method based on big data analysis provided by an embodiment of the present application;

[0049] Figure 3 is a specific implementation flowchart of S203 in a hydrogen production and purification method based on big data analysis provided by a second embodiment of the present application;

[0050] Figure 4 is a specific implementation flowchart of S2033 in a hydrogen production and purification method based on big data analysis provided by a third embodiment of the present application;

[0051] Figure 5 is a specific implementation flowchart of a hydrogen production and purification method based on big data analysis provided by a fourth embodiment of the present application before S201;

[0052] Figure 6 is a specific implementation flowchart of a hydrogen production and purification method based on big data analysis provided by a fifth embodiment of the present application before S505;

[0053] Figure 7 is a specific implementation flowchart of a hydrogen production and purification method based on big data analysis provided by a sixth embodiment of the present application after S201;

[0054] Figure 8 is a structural schematic diagram of a hydrogen production and purification device based on big data analysis provided by an embodiment of the present application;

[0055] Figure 9 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0056] In the following description, specific details are set forth in order to provide a thorough understanding of embodiments of the present application. However, persons skilled in the art will understand that embodiments of the present application can be practiced without these specific details, in other instances, well-known systems, structures, circuits, and methods have not been described in detail in order to avoid obscuring the present application.

[0057] It should be understood that when used in the specification and the appended claims, the term "comprises" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0058] In addition, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0059] The hydrogen purification method based on big data analysis provided by the embodiments of the present application can be applied to the control device of the hydrogen production and purification system. Exemplarily, Figure 1 The structure schematic diagram of the hydrogen production and purification system provided by an embodiment of the present application is shown. Referring to Figure 1 The hydrogen production and purification system includes a control device 11 and a hydrogen production cavity 12 for hydrogen purification. The hydrogen production cavity 12 includes a raw gas storage bin, an adsorption bin and a finished gas storage bin. The raw gas storage bin is used to store raw gas with low hydrogen purity. The number of adsorption bins can be set according to actual conditions. For example, the hydrogen production cavity 12 provided in the embodiment includes three adsorption bins, and can also be other numbers. The finished gas storage bin is used to store high-purity hydrogen after purification, i.e. as finished gas output. The gas flow can be controlled among the various bin bodies in the hydrogen production cavity 12 through a type of valve to achieve the purpose of pressure swing purification. A type of valve can also be provided in the adsorption bin to achieve pressure swing adsorption in the adsorption bin, improve the flexibility of pressure swing adsorption, and also improve the utilization efficiency of the adsorption bin. Since pressure swing adsorption relies on the coordinated operation of the valves between different bin bodies to achieve the purpose of impurity adsorption through the corresponding adsorbers in the adsorption bin, the control device 11 needs to accurately control the on-off state of each valve according to the valve timing.

[0060] The control device 11 can be in communication connection with the related components in the hydrogen production cavity 12, for example, the control device 11 can be in communication connection with each valve on the hydrogen production cavity 12 to achieve the purpose of remotely controlling each valve; the control device 11 can also be in communication connection with the adsorbers in the adsorption bin, so as to control the adsorbers to adsorb impurities from the gas with preset operating parameters. After the raw gas passes through all the adsorbers corresponding to the valves, the raw gas after the adsorption operation can be stored in the finished gas storage bin, i.e. the target hydrogen gas with a preset purity is obtained.

[0061] Please refer to Figure 2 , Figure 2An implementation schematic of a hydrogen purification method based on big data analysis provided by the embodiment of the application is shown. The hydrogen purification method based on big data analysis is applied to the control device 11 described above, that is, the execution subject of the embodiment of the application can be the control device 11 described above. Specifically, the control device 11 is an electronic device, which can be a computer, a notebook computer, a server, a smart phone, or the like. For ease of description, the execution subject is taken as an example of an electronic device in the following description. Specifically, the method includes the following steps:

[0062] In S201, real-time collection data of raw material gas in a hydrogen production cavity is obtained. The real-time collection data includes first component information and flow data of the raw material gas. The hydrogen production cavity contains a plurality of valves.

[0063] In the embodiment, the electronic device can obtain the real-time collection data through a data collection module in the hydrogen production cavity. The data collection module can be arranged in the raw material gas storage bin or in any adsorption bin. The number and arrangement of the data collection module can be set according to actual conditions. Optionally, the data collection module can be arranged on a valve on a pipeline between the bins, such as a valve on the pipeline between the raw material gas storage bin and the first adsorption bin. Figure 1

[0064] In some possible implementation manners, when the data collection module is arranged in the raw material gas storage bin, the triggering time of S201 is at an accurate stage of preparing to perform impurity adsorption on the raw material gas, that is, before performing impurity adsorption. The corresponding valve control script can be determined through the embodiment.

[0065] In some possible implementation manners, when the data collection module is arranged on a pipeline between the adsorption bins, the triggering time of S201 is at an execution stage of performing impurity adsorption on the raw material gas, that is, in the process of performing impurity adsorption. The original valve control script can be calibrated in real time through the embodiment, so as to improve the accuracy of the valve control script in the running process.

[0066] ​In some possible implementations, the electronic device can collect the ingredient data of the raw material gas once per preset time interval (such as every 1 minute) through the gas chromatograph, and measure the flow of the raw material gas in real time using the vortex flowmeter. After preprocessing such as denoising and normalization on the collected data, key features affecting valve switching are extracted, such as hydrogen concentration, methane concentration, and flow, and the real-time collection data set described above is constructed. Using the real-time collection data set, a nonlinear mapping relationship model between the valve switching timing and duration and the raw material gas parameters is established by using a support vector regression algorithm, that is, a subsequent supervised model. The model is deployed in the hydrogen production system, and real-time raw material gas parameter data is received. When it is detected that the hydrogen concentration changes by more than a concentration threshold or the flow changes by more than a preset amplitude threshold, the model is triggered to calculate, so that an adjusted valve control script can be obtained immediately in a preset response time. The valve control script includes the valve timing and the state duration of the valve. The control system executes the valve switching operation according to the model calculation result, adjusts the switching timing to the optimal value output by the model, and adjusts the duration accordingly to adapt to the changes in the ingredient and flow of the raw material gas.

[0067] In S202, the real-time collection data is analyzed by a preset supervised model to obtain a valve control script corresponding to the hydrogen production cavity; the supervised model is obtained by big data analysis based on historical data of the hydrogen production cavity; and the valve control script is used to determine the valve switching timing of the hydrogen production cavity.

[0068] In this embodiment, after the electronic device collects the real-time collection data described above, the real-time collection data can be imported into a preset supervised model, and the valve control script matched with the current environment can be automatically generated by the supervised model. Since the supervised model is generated by collecting a large amount of historical running data and performing big data analysis on the historical running data, the supervised model can effectively guide the valve control and improve the accuracy of the valve control. It should be noted that the electronic device can add the collection data collected this time to the training set after completing the purification operation of the raw material gas once, and then calibrate the supervised model by using the training set to improve the accuracy of the supervised model.

[0069] In some possible implementations, by preprocessing and feature extraction on real-time collected data, a data set available for modeling is obtained, and according to the obtained data set, a supervised optimization model of valve switching timing and duration is established by using a supervised model such as a support vector machine or a neural network. The electronic device deploys the established optimization model to the hydrogen production system, and receives real-time collection data of the raw gas parameters. If it is detected that the raw gas parameters change, the changed parameters are input into the optimization model, and an adjusted valve control script is obtained by model calculation. According to the output result of the optimization model, the electronic device can timely adjust the valve switching timing and duration to adapt to the changing process conditions. A plurality of valve switching plans are set in the hydrogen production cavity, and according to the change trend of the raw gas composition, flow and other parameters, the optimal control plan under the current working condition is judged by using a pattern recognition algorithm. According to the judgment result, the control system switches to the optimal control plan in advance, reduces the transition time in the valve switching process, and improves the response speed and stability of the system. The temperature, pressure and other key parameters of the adsorber are monitored and analyzed in real time, and a reinforcement learning algorithm is used to dynamically optimize the adsorption and regeneration process through continuous trial and error and feedback. According to the optimization result of the reinforcement learning algorithm, the control system adjusts the valve switching timing and duration in real time, dynamically adapts to the change of the process conditions, and improves the purification efficiency of hydrogen and the product quality.

[0070] In S203, each valve is controlled to switch according to the valve switching timing, so as to adsorb impurities in the raw gas by the adsorber corresponding to each valve.

[0071] In this embodiment, after the electronic device determines the valve control script, the electronic device can control the on-off state of each valve based on the valve switching timing in the valve control script and the duration corresponding to each on-off state, and after the raw gas flows into the adsorption area corresponding to the valve, the corresponding adsorber can be started to adsorb impurities in the raw gas. It should be noted that if the data collection module is arranged in the adsorption chamber, the valve control script can be calibrated, for example, the valve control timing can be time calibrated, so as to improve the accuracy of the valve control script.

[0072] In a possible implementation, the electronic device can preset valve switching modes (including the step length of the control valve and the corresponding power value of the driving module) in different working conditions, that is, different valves can correspond to one or more valve switching modes. The electronic device can use the K-means clustering algorithm to match the valve switching modes in different working conditions according to the change trend of the raw material gas parameters to obtain the corresponding valve switching modes, and switch to the valve switching modes in advance, such as controlling the driving module of the valve to operate at a preset power or adjusting the step length, to reduce the transition time of valve switching and enable the system to quickly respond to changes in working conditions. For the operating parameters of the adsorber, the operating data can be collected based on a preset collection period, the Q-learning reinforcement learning algorithm can be used, the target range of temperature and pressure can be set, the valve switching time and duration can be continuously adjusted, and the optimal control strategy can be learned according to the feedback information of the adsorption effect. The learned strategy is used to guide the real-time adjustment of the valve operation of the control system, to ensure the adsorption effect and improve the purification efficiency and quality of hydrogen.

[0073] In S204, the raw material gas obtained after adsorption through all the valves is collected to obtain target hydrogen gas of a preset purity.

[0074] In this embodiment, the electronic device can obtain target hydrogen gas of a preset purity after performing all the adsorption operations, that is, after the impurities in the raw material gas are adsorbed by the adsorbers in all the adsorption chambers and corresponding adsorption areas, and store the target hydrogen gas in the finished gas storage chamber. Subsequently, the hydrogen gas in the finished gas storage chamber can be packaged to obtain related high-purity hydrogen gas.

[0075] It can be seen from the above that the hydrogen purification method based on big data analysis provided by the embodiment of the application can improve the accuracy of valve control and the matching degree between the valve control script and the actual scene. According to the valve control script, each valve is controlled to switch, and the corresponding adsorber is used to adsorb impurities in the raw material gas to improve the purity of hydrogen in the raw material gas. After the adsorption operation of the corresponding adsorber of all valves, the corresponding raw material gas is output as finished gas to obtain target hydrogen of a preset purity, so as to achieve the purpose of hydrogen purification. Compared with the existing hydrogen production technology, in the embodiment of the application, the control of the valve is not controlled by fixed time sequence logic, but when hydrogen production and purification are performed, the real-time collection data in the current cavity is imported into the supervision model to generate a valve control script matched therewith, so as to improve the matching degree between the valve control script and the actual scene, thereby improving the accuracy of valve control, and then improving the adsorption effect of each valve corresponding adsorber, so as to improve the purity of hydrogen in the finished gas, and also improve the accuracy of hydrogen production management, without repeatedly adsorbing impurities, thereby improving the production efficiency of hydrogen.

[0076] Figure 3 The specific implementation flowchart of S203 in the hydrogen purification method based on big data analysis provided by the second embodiment of the application is shown. Referring to FIG. 6, compared with the embodiment shown in FIG. 5, Figure 3 Figure 2 The hydrogen purification method based on big data analysis provided by the embodiment of the application comprises S2031-S2033 in S203, and the specific description is as follows:

[0077] In S2031, for the adsorber of the Nth valve, the adsorption calibration coefficient is determined according to the second component information of the raw material gas when flowing through the N-1th valve and the first adsorption parameter corresponding to the N-1th valve; N is a positive integer greater than 1 and not greater than the total number of valves of the hydrogen production cavity; the adsorption calibration coefficient is:

[0078]

[0079] wherein, Adsob(N) is the adsorption calibration coefficient; Adsob(N-1) is the first adsorption parameter; Compent adj [N-1] is the concentration value of the i-th gas in the second component information; BaseCom i [N-1] is the concentration value of the i-th gas in the second component information; BaseCom i ​is the expected concentration value of the i-th gas when the raw gas flows through the N-1th valve; M is the total number of gases in the second component information; P is the total number of valves; Adsob(j) is the adsorption parameter corresponding to the j-th valve.

[0080] In this embodiment, the electronic device can collect the second component information corresponding to the raw gas when the raw gas passes through the valve of the previous adsorption bin, so as to determine whether there is a deviation between the current gas component and the expected expected gas component, so as to determine whether the current adsorption efficiency meets the requirements. The electronic device can calibrate the operating parameters of the adsorber in the adsorption region corresponding to the subsequent valve according to the component information collected by the valve of the previous adsorption sequence.

[0081] In this embodiment, the electronic device can calculate the adsorption deviation of different gas components, that is, calculate the deviation between the concentration value of a certain gas in the second component information and the expected concentration value. The above-mentioned gas can be an impurity gas or hydrogen. According to the concentration value deviation corresponding to each gas, the corresponding adsorption deviation can be obtained, and the corresponding adsorption calibration coefficient is determined according to the adsorption deviation. The above-mentioned adsorption calibration coefficient is also related to the adsorption parameter (i.e. the first adsorption parameter) corresponding to the adsorber of the previous adsorption sequence. If the value of the adsorption parameter is larger, the ability of abnormal calibration is stronger, so the value of the subsequent adsorption calibration coefficient is smaller. Conversely, if the value of the adsorption parameter is smaller, the ability of abnormal calibration is weaker, and the value of the corresponding adsorption calibration coefficient is larger.

[0082] Further, as another embodiment of the present application, the S2031 in the process of collecting the second component information can include the following steps:

[0083] In S2031.1, a plurality of sub-threads corresponding to the number of component detection modules deployed in the adsorption region corresponding to the N-1th valve are created according to the number of component detection modules deployed in the adsorption region corresponding to the N-1th valve; each sub-thread corresponds to one of the component detection modules.

[0084] In this embodiment, in the case that the raw gas contains a large number of gas impurities, one or more component detection modules can be configured according to the impurity types. Different component detection modules can be used to collect the concentration value, or the distribution density value, etc. of one or more impurities. The electronic device can configure a plurality of sub-threads corresponding to the number of component detection modules according to the number of component detection modules, and each thread is used to receive the detection data fed back by one component detection module, that is, to generate the raw data of the second component information.

[0085] In S2031.2, the original acquisition data corresponding to each of the component detection modules is respectively acquired by each sub-thread, and the original acquisition data corresponding to each sub-thread is processed in parallel by multiple sub-threads to obtain the second component information.

[0086] In this embodiment, the electronic device can be provided with multiple interfaces, different interfaces being used to receive original data sent by different component detection modules, wherein the original data can be processed by corresponding sub-threads. Due to the parallel operation of multiple sub-threads, the original data fed back by multiple component detection modules can be processed at the same time, thereby improving the efficiency of original data processing and the efficiency of generating second component information.

[0087] In this embodiment, due to the large number of valves in the PSA and the time limit requirement for the cooperation between the valves, the running environment of the adsorber also needs a certain running time. In order to improve the management accuracy and accurately control the adsorber, it is necessary to reduce the time required for related operations as much as possible. Therefore, the multi-thread parallel processing of original data can reduce the time required for calculation, thereby improving the accuracy of adsorber control.

[0088] In S2032, the expected adsorption parameter of the Nth valve is calibrated according to the adsorption calibration coefficient to obtain the second adsorption parameter of the Nth valve.

[0089] In S2033, when the raw gas flows through the Nth valve, the adsorber is controlled to perform adsorption operation on the raw gas according to the second adsorption parameter.

[0090] In this embodiment, after the electronic device calculates the adsorption calibration coefficient for the adsorber, the expected running parameter of the adsorber can be calibrated according to the calibration adsorption coefficient. The expected running parameter of the adsorber can include multiple dimensional characteristic values, such as adsorption temperature, adsorption environment pressure, and adsorbent dosage. The electronic device can calculate the calibration adsorption coefficient corresponding to different dimensions, and adjust the corresponding characteristic values according to the calibration adsorption coefficient of the corresponding dimension, so as to obtain the adjusted characteristic values of all dimensions, and obtain the corresponding second adsorption parameter. When the raw gas flows through the adsorption area corresponding to the valve, the adsorber is controlled to run in the state of the second adsorption parameter to adsorb impurities in the raw gas.

[0091] In a possible implementation, the electronic device can be provided with a calibration threshold value, and if it is detected that the adsorption calibration coefficient is less than or equal to the calibration threshold value, it is identified as not needing adjustment. Since the running parameter calibration is performed on the adsorber, the running parameter of the adsorber is affected, so the adsorption environment that has been stabilized needs to be changed, which may reduce the actual adsorption effect due to the change in the environment. In order to avoid the above situation, in the scenario where the adsorption calibration coefficient is less than or equal to the calibration threshold value, the running parameter of the adsorber does not need to be adjusted, that is, the original adsorption parameter is maintained; otherwise, if the adsorption calibration coefficient is greater than the calibration threshold value, the second adsorption parameter after calibration can be determined, and the running parameter of the adsorber is set based on the second adsorption parameter after calibration.

[0092] In the embodiment of the present application, by collecting the corresponding second component information when the raw gas flows through the valve of the previous adsorption order, the accuracy of the running parameter of the adsorber can be improved by calibrating the parameter of the adsorber of the next adsorption order according to the second component information, thereby improving the purity of the target hydrogen.

[0093] Figure 4 A specific implementation flowchart of S2033 in the hydrogen purification method based on big data analysis provided by the third embodiment of the present application is shown. Referring to Figure 4 Compared with Figure 3 The embodiment of the present application provides a hydrogen purification method based on big data analysis, and S2033 before S2033 can include S401, S2033 can include S402-S404, and specific descriptions are as follows:

[0094] Before determining the adsorption calibration coefficient of the adsorber of the Nth valve according to the second component information of the raw gas flowing through the N-1th valve and the first adsorption parameter corresponding to the N-1th valve, the method further includes:

[0095] In S401, when the raw gas flows through the N-1th valve, the running state of the adsorber of the Nth valve is set according to the expected adsorption parameter corresponding to the Nth valve; the expected adsorption parameter includes multiple dimension indicators; the dimension indicators include: gas pressure value, adsorption efficiency and regeneration efficiency.

[0096] In this embodiment, the adsorber needs a certain time length to build a corresponding adsorption environment in the adsorption area, for example, to raise the temperature of the adsorption area to a preset temperature value, or to adjust the air pressure of the adsorption area to a preset air pressure value, etc. In order to improve the timeliness and accuracy of environment building, after determining the valve control script, the adsorber in the adsorption area corresponding to each valve can be environment built according to the expected adsorption parameters recorded in the valve control script, that is, the running state of the adsorber of each valve is set based on the expected adsorption parameters, so that a stable adsorption environment can be built in advance, such as setting the temperature value in the adsorption area to a preset value to enable the adsorbent in the adsorber to adsorb impurities with a preset adsorption efficiency, and the dosage of the adsorbent in the adsorber can also be adjusted to adjust the regeneration efficiency of the overall adsorbent.

[0097] Correspondingly, the adsorption operation of the adsorber on the raw material gas according to the second adsorption parameters when the raw material gas flows through the Nth valve includes:

[0098] In S402, the deviation value of the expected adsorption parameters and the second adsorption parameters corresponding to each dimension index is calculated.

[0099] In S403, the adjustment order of each dimension index is determined based on the order of the deviation value of each dimension index from large to small.

[0100] In S404, the running state of the adsorber corresponding to each dimension index is adjusted in turn based on the adjustment order.

[0101] In this embodiment, the electronic device can adjust the expected adsorption parameters by the adsorption calibration coefficient, and different dimensions of features can be adjusted by different adsorption calibration coefficients, so the deviation values of different dimension indexes are different. The electronic device can calculate the deviation value of the dimension index corresponding to each dimension, that is, the difference between the dimension index in the expected adsorption parameters and the dimension index in the second adsorption parameters.

[0102] In this embodiment, since the construction of the adsorption environment needs a certain time length, the greater the above deviation value, the longer the required time length. Therefore, the electronic device can determine the adjustment order of each dimension index according to the order of the deviation value of different dimension indexes from large to small, that is, the greater the deviation value of the dimension index, the earlier the corresponding adjustment order, and vice versa. The adjustment order of the running parameter of the adsorber corresponding to each dimension index is adjusted according to the corresponding adjustment order.

[0103] In the embodiment of the present application, the adsorption environment can be constructed before the raw gas enters the adsorption area of the valve, and the adjustment order is determined according to the deviation value corresponding to different dimension indicators, which can improve the probability that the adsorption environment corresponds to the environment corresponding to the second adsorption parameter when the raw gas enters the adsorption area, thereby improving the accuracy of control.

[0104] Figure 5 The specific implementation flowchart of the hydrogen purification method based on big data analysis provided by the fourth embodiment of the present application is shown before S201. Referring to Figure 5 , compared with Figures 2-4 any one of the embodiments, the hydrogen purification method based on big data analysis provided by the present embodiment further comprises S501-S505 before S201, which are specifically described as follows:

[0105] In S501, the test running data corresponding to each valve of the hydrogen production cavity is obtained; the test running data includes the state switching time length and the sealing coefficient of the valve when the test result is obtained before the hydrogen production starts.

[0106] In the present embodiment, the historical test running data of each valve in the hydrogen production cavity is obtained, including parameters such as valve state switching time length and sealing coefficient; a valve performance evaluation model is established according to the obtained historical test running data, which is used to evaluate the state switching time length and sealing performance of the valve; before the hydrogen production starts, the real-time test running data of each valve is obtained, and the real-time data is input into the valve performance evaluation model for evaluation; the evaluation result of the valve performance evaluation model is compared with the preset threshold value to determine whether the switching time length and sealing performance of each valve meet the standard; if the evaluation result shows that the valve switching time length or sealing performance does not meet the standard, a warning information is sent to prompt the relevant personnel to perform valve maintenance or replacement; according to the valve performance evaluation result, the hydrogen production process parameters are dynamically adjusted, such as adjusting the reaction temperature, pressure, etc., to adapt to the actual performance of the valve; the valve test evaluation result and the hydrogen production process parameter adjustment are recorded in the system log for subsequent analysis and optimization of the hydrogen production process.

[0107] In S502, the first switching time sequence curve and the second sealing time sequence curve corresponding to each valve are constructed through the historical running data corresponding to the hydrogen production cavity.

[0108] In the embodiment, historical operation data of the hydrogen production cavity is acquired, including the switching state and the sealing state of each valve changing over time; the acquired historical operation data is preprocessed to remove outliers and invalid data, obtaining effective valve state data; according to the preprocessed valve state data, the switching time point and the sealing time point of each valve are extracted to form a time sequence; wherein the time sequence includes a first time sequence based on the switching time point, and a second time sequence based on the sealing coefficient; the first switching time sequence curve is constructed based on the first time sequence, and the second sealing time sequence curve is obtained based on the second time sequence.

[0109] In S503, a first slope deviation between the first switching time sequence curve and the first coordinate is determined according to the state switching time length in the first coordinate corresponding to the first coordinate system; the first slope deviation is used to adjust the time sequence factor in the supervision model.

[0110] In the embodiment, the state switching time length is obtained by collecting the trial operation stage before hydrogen production, that is, the reaction speed of the current valve of the hydrogen production system can be reflected, and the connection line between the first coordinate and the latest collected coordinate point in the first time sequence curve is connected as the slope corresponding to the first coordinate. Then, according to the slope of the latest collected coordinate point in the first time sequence curve and the slope corresponding to the first coordinate, the first slope deviation is calculated. The first slope deviation can be used to determine the time length required to control the valve, so as to improve the accuracy of the valve switching time sequence.

[0111] In S504, a second slope deviation between the second sealing time sequence curve and the second coordinate is determined according to the sealing coefficient in the second coordinate corresponding to the second coordinate system; the second slope deviation is used to adjust the valve thrust factor in the supervision model.

[0112] In the embodiment, the valve may be aged during use, so the corresponding thrust may differ due to the use time, thereby causing the actual moving position of the valve to have a deviation when the valve is pushed with the same force, and then affecting the sealing property of the valve. Therefore, the second slope deviation can be calculated to calibrate the thrust of the valve, so as to improve the accuracy of the valve control.

[0113] In the embodiment, the sealing coefficient is collected in a trial run stage before hydrogen production, that is, the expected sealing coefficient of the valve of the hydrogen production system when the corresponding thrust is controlled, and the line connecting the second coordinate and the latest collected coordinate point in the second sealing time sequence curve is taken as the slope corresponding to the second coordinate. Then, the second slope deviation is calculated according to the slope of the latest collected coordinate point in the second time sequence curve and the slope corresponding to the second coordinate.

[0114] In S505, the supervision model is calibrated according to the first slope deviation and the second slope deviation corresponding to all valves.

[0115] In the embodiment, the electronic device can calibrate the supervision model according to the two slope deviations to improve the accuracy and timeliness of the model.

[0116] Figure 6 A specific implementation flowchart of the hydrogen purification method based on big data analysis provided by the fifth embodiment of the application before S505 is shown. Referring to Figure 6 , compared with Figure 5 the embodiment, the hydrogen purification method based on big data analysis provided by the embodiment before S505 further includes S601-S602, which are specifically described as follows:

[0117] In S601, the aging coefficient corresponding to each valve is calculated according to the second slope deviation and the first slope deviation.

[0118] In S602, if any of the aging coefficients is greater than a preset aging threshold, the first abnormal information corresponding to the valve is generated.

[0119] In the embodiment, before the hydrogen purification operation is performed, a pre-test process is performed, the corresponding state switching duration and the corresponding sealing coefficient when the valve is controlled are collected, and the slope deviation between the time sequence curve constructed with the historical data is calculated according to the two coefficients. The slope deviation can determine whether the valve operation is aging, and then the aging coefficient can be calculated. The greater the values of the two slope deviations, the greater the corresponding aging coefficient. The aging coefficient can be calculated according to a preset conversion function.

[0120] In the embodiment, if it is detected that the aging coefficient corresponding to any valve in the hydrogen production cavity is greater than a preset aging threshold, it is indicated that the valve needs to be maintained, and then the first abnormal information of the valve with the aging coefficient greater than the aging threshold can be generated.

[0121] In the embodiment of the present application, the electronic device can monitor the aging degree of the valve through the above-mentioned slope deviation, thereby realizing timely identification of the valve with aging use and generating corresponding abnormal information, and improving the timeliness of the aging detection of the hydrogen production system.

[0122] Figure 7 A specific implementation flowchart of the hydrogen gas purification method based on big data analysis provided by the sixth embodiment of the present application is shown after S201. Referring to Figure 8 , compared with Figures 2-6 any one of the embodiments, the hydrogen gas purification method based on big data analysis provided by the present embodiment further comprises S701-S702 after S201, which are specifically described as follows:

[0123] In S701, the air flow rate corresponding to the raw material gas adsorbed by all the valves is collected.

[0124] In S702, if the air flow rate is greater than the preset flow rate threshold, the second abnormal information of valve gas leakage is generated. In the present embodiment, the electronic device can collect the air flow rate corresponding to the raw material gas through the flow rate detection module on the valve when the raw material gas is delivered to the adsorption area corresponding to each valve. Since the deviation between the corresponding air flow rate and the flow rate threshold is small in the case of no gas leakage or good valve sealing, it is close to the preset flow rate threshold. On the contrary, if the above-mentioned air flow rate is greater than the above-mentioned flow rate threshold, there may be a gas leakage, and at this time, the corresponding second abnormal information can be generated to prompt the administrator that there is a gas leakage in the valve, so as to realize automatic detection and identification of abnormalities.

[0125] In the present embodiment, Figure 8 A structure block diagram of the hydrogen gas purification device based on big data analysis provided by an embodiment of the present application is shown, which comprises units for executing Figure 2 each step realized by the first device in the corresponding embodiment. For details, please refer to Figure 2 and Figure 2 the related description in the corresponding embodiment. For ease of illustration, only the part related to the present embodiment is shown.

[0126] Referring to Figure 8 , the hydrogen gas purification device based on big data analysis comprises:

[0127] The real-time collection unit 81 is used to acquire real-time collection data of the raw material gas in the hydrogen production cavity; the real-time collection data comprises first component information and flow data of the raw material gas; the hydrogen production cavity contains a plurality of valves;

[0128] A valve control script generation unit 82 is configured to analyze the real-time collected data using a preset supervisory model to obtain a valve control script corresponding to the hydrogen production chamber; the supervisory model is obtained by performing a big data analysis based on historical data of the hydrogen production chamber; and the valve control script is configured to determine the valve switching timing of the hydrogen production chamber.

[0129] a valve control unit 83 for controlling each of the valves to switch according to the valve switching sequence, so as to adsorb impurities from the feed gas through the adsorber corresponding to each valve;

[0130] The purification unit 84 is used to collect the raw gas obtained after adsorption by all the valves to obtain target hydrogen with a preset purity.

[0131] It should be understood that Figure 8 In the structural block diagram of the device shown, each module is used to execute Figures 2 to 7 The steps in the corresponding embodiments, and Figures 2 to 7 Each step in the corresponding embodiment has been explained in detail in the above embodiment. Figures 2 to 7 as well as Figures 2 to 7 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0132] Figure 9 This is a structural block diagram of an electronic device provided by another embodiment of the present application. Figure 9 As described above, the electronic device 900 of this embodiment includes: a processor 910, a memory 920, and a computer program 930 stored in the memory 920 and executable by the processor 910, such as a program for a hydrogen purification method based on big data analysis. When the processor 910 executes the computer program 930, the steps in each embodiment of the hydrogen purification method based on big data analysis are implemented, such as Figure 2 Alternatively, the processor 910 executes the computer program 930 to implement the above Figure 8 The functions of each module in the corresponding embodiment are, for example, Figure 8 For details on the functions of units 81 to 84, please refer to Figure 8 Related description in the corresponding embodiment.

[0133] Exemplarily, the computer program 930 may be divided into one or more modules, one or more of which are stored in the memory 920 and executed by the processor 910 to complete the present application. One or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 930 in the electronic device 900. For example, the computer program 930 may be divided into various unit modules, and the specific functions of each module are as described above.

[0134] The electronic device 900 can include, but is not limited to, a processor 910, a memory 920. Those skilled in the art can understand that the electronic device 900 can include more or less components than those shown, or combine some components, or include different components, for example, the electronic device can also include an input / output device, a network access device, a bus, etc. Figure 9 The electronic device 900 is merely an example and does not constitute a limitation on the electronic device 900, and can include more or less components than those shown, or combine some components, or include different components, for example, the electronic device can also include an input / output device, a network access device, a bus, etc.

[0135] The processor 910 can be a central processing unit, and can also be other general-purpose processors, digital signal processors, application-specific integrated circuits, ready programmable gate arrays or other programmable logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0136] The memory 920 can be an internal storage unit of the electronic device 900, such as a hard disk or memory of the electronic device 900. The memory 920 can also be an external storage device of the electronic device 900, such as a plug-in hard disk, a smart memory card, a flash memory card, etc. provided on the electronic device 900. Further, the memory 920 can include both the internal storage unit and the external storage device of the electronic device 900.

[0137] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for purifying hydrogen based on big data analysis, characterized by, The method comprises: acquiring real-time collection data of raw gas in a hydrogen production cavity; the real-time collection data comprises first component information and flow data of the raw gas; the hydrogen production cavity comprises a plurality of valves; performing data analysis on the real-time collection data by a preset supervision model to obtain a valve control script corresponding to the hydrogen production cavity; the supervision model is obtained by big data analysis based on historical data of the hydrogen production cavity; and the valve control script is used to determine a valve switching time sequence of the hydrogen production cavity; controlling each valve to switch according to the valve switching time sequence, so that the raw gas is adsorbed by an adsorber corresponding to each valve; collecting the raw gas after being adsorbed by all the valves to obtain target hydrogen gas with a preset purity; the method of controlling each valve to switch according to the valve switching time sequence, so that the raw gas is adsorbed by an adsorber corresponding to each valve, comprises: for an adsorber of an Nth valve, determining an adsorption calibration coefficient according to second component information of raw gas when flowing through an (N-1) th valve and first adsorption parameters corresponding to the (N-1) th valve; N is a positive integer greater than 1 and not greater than the total number of valves of the hydrogen production cavity; the adsorption calibration coefficient is: Adsob(N) = Adsob(N-1) + Compent(N-1) * (BaseCom(N-1) - Adsob(N-1)) adj (N) is the adsorption calibration coefficient; Adsob(N-1) is the first adsorption parameter; Compent(N-1) is the concentration value of the i-th gas in the second component information; BaseCom(N-1) is the expected concentration value of the i-th gas when the raw gas flows through the N-1th valve; M is the total number of gases in the second component information; P is the total number of valves; Adsob(j) is the adsorption parameter corresponding to the jth valve. i (N) is the adsorption calibration coefficient; Adsob(N-1) is the first adsorption parameter; Compent(N-1) is the concentration value of the i-th gas in the second component information; BaseCom(N-1) is the expected concentration value of the i-th gas when the raw gas flows through the N-1th valve; M is the total number of gases in the second component information; P is the total number of valves; Adsob(j) is the adsorption parameter corresponding to the jth valve. i (N) is the adsorption calibration coefficient; Adsob(N-1) is the first adsorption parameter; Compent(N-1) is the concentration value of the i-th gas in performing parameter calibration on expected adsorption parameters of the Nth valve according to the adsorption calibration coefficient to obtain second adsorption parameters of the Nth valve; when the raw gas flows through the Nth valve, controlling the adsorber to perform adsorption operation on the raw gas according to the second adsorption parameters.

2. The hydrogen purification method according to claim 1, characterized by, the method of determining, for an adsorber of an Nth valve, an adsorption calibration coefficient according to second component information of raw gas when flowing through an (N-1) th valve and first adsorption parameters corresponding to the (N-1) th valve, comprises: creating a plurality of sub-threads corresponding to a module number of a component detection module deployed in a middle part of an adsorption area corresponding to the (N-1) th valve; each sub-thread corresponds to one of the component detection modules; obtaining, by each sub-thread, raw collection data corresponding to each of the component detection modules, and performing parallel processing on the raw collection data corresponding to each of the sub-threads to obtain the second component information.

3. The hydrogen purification method according to claim 1, characterized by, before the method of determining, for an adsorber of an Nth valve, an adsorption calibration coefficient according to second component information of raw gas when flowing through an (N-1) th valve and first adsorption parameters corresponding to the (N-1) th valve, further comprises: when the raw gas flows through the (N-1) th valve, setting an operating state of an adsorber of the Nth valve according to expected adsorption parameters corresponding to the Nth valve; the expected adsorption parameters comprise a plurality of dimension indicators; the dimension indicators comprise: a gas pressure value, an adsorption efficiency, and a regeneration efficiency; correspondingly, the method of controlling the adsorber to perform adsorption operation on the raw gas according to the second adsorption parameters when the raw gas flows through the Nth valve, comprises: calculating deviation values of the expected adsorption parameters and the second adsorption parameters corresponding to each dimension indicator; determine an adjustment order corresponding to each of the dimension indicators based on an order from large to small of bias values corresponding to the dimension indicators; adjust the adsorber in a running state corresponding to each of the dimension indicators in sequence based on the adjustment order.

4. The hydrogen purification method according to any one of claims 1 to 3, characterized by, Before the data analysis on the real-time collected data is performed by the preset supervision model to obtain the valve control script corresponding to the hydrogen production cavity, the method further includes: obtain test running data corresponding to each valve of the hydrogen production cavity; the test running data includes a state switching duration of the valve when a test result is obtained before hydrogen production starts and a sealing coefficient; construct a first switching time sequence curve and a second sealing time sequence curve corresponding to each valve based on historical running data corresponding to the hydrogen production cavity; determine a first slope bias between the first switching time sequence curve and a first coordinate based on a first coordinate of a first coordinate system corresponding to the first switching time sequence curve according to the state switching duration; the first slope bias is used to adjust a time sequence factor in the supervision model; determine a second slope bias between the second sealing time sequence curve and a second coordinate based on a second coordinate of a second coordinate system corresponding to the second sealing time sequence curve according to the sealing coefficient; the second slope bias is used to adjust a valve thrust factor in the supervision model; calibrate the supervision model according to the first slope bias and the second slope bias corresponding to all valves.

5. The hydrogen purification method according to claim 4, characterized by, Before the calibration of the supervision model according to the first slope bias and the second slope bias, the method further includes: calculate an aging coefficient corresponding to each valve according to the second slope bias and the first slope bias; if any of the aging coefficients is greater than a preset aging threshold, generate first abnormal information corresponding to the valve.

6. The hydrogen purification method according to any one of claims 1 to 3, characterized by, After the raw material gas obtained after the adsorption of all the valves is collected to obtain target hydrogen gas of a preset purity, the method further includes: collect an air flow rate corresponding to the raw material gas output after the adsorption of all the valves; if the air flow rate is greater than a preset flow rate threshold, generate second abnormal information of valve gas leakage.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method of any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. The computer program is executed by the processor to implement the method of any one of claims 1 to 6.

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