Phosphine production equipment control method and device, equipment and medium

Abstract

The invention provides a phosphine production equipment control method and device, equipment and a medium, and belongs to the technical field of automatic control, and the method comprises the steps: mapping a real-time operation parameter into a corresponding fuzzy linguistic variable based on a preset membership function; based on the fuzzy linguistic variable of the refining unit, determining a target adjusting unit according to a preset top fuzzy rule; determining a first adjustment instruction corresponding to the target adjustment unit according to a preset middle-layer fuzzy rule based on the target adjustment unit and the fuzzy linguistic variable of each production equipment unit; based on the target adjustment unit, the first adjustment instruction and the fuzzy linguistic variable of the reaction unit, determining a second adjustment instruction of the reaction unit according to a preset bottom fuzzy rule; and issuing the first adjustment instruction and the second adjustment instruction to the corresponding production equipment units so as to realize control of each production equipment unit. According to the phosphine production equipment control method and device, the equipment and the medium provided by the invention, the equipment adjustment accuracy can be improved.

Description

Technical Field

[0001] The present application belongs to the field of automatic control technology, and more specifically, relates to a method and device, equipment, and medium for controlling phosphine production equipment. Background Art

[0002] Phosphine (PH3) has a wide range of applications, including as a dopant in the semiconductor industry, a raw material in organic synthesis, and a fumigant in grain storage (for pest control). The typical phosphine production process includes a reaction unit, a dehydration unit, a collection and compression unit, and a refining unit. The reaction unit generates crude phosphine, and its reaction rate and raw material ratio directly affect the subsequent processing load. The dehydration unit must match the upstream gas volume and humidity. Incomplete dehydration can lead to corrosion in the collection and compression unit equipment and reduced product purity. The pressure parameters of the collection and compression unit must be linked to the output of the reaction unit and the feed requirements of the refining unit. Pressure imbalances can cause leaks or a sudden drop in purification efficiency. The purification accuracy of the refining unit depends on the pretreatment results of the preceding unit. Excessive impurities in the preceding unit can lead to overload of the refining unit and a surge in energy consumption. The parameters of each device are interconnected, and any imbalance in any link can trigger a chain reaction.

[0003] Therefore, it is urgent to propose a control method for phosphine production equipment to achieve efficient coordination of various equipment, improve the accuracy of equipment adjustment and the stability of the phosphine production process. Summary of the Invention

[0004] The purpose of this application is to provide a method and device, equipment, and medium for controlling phosphine production equipment to improve the accuracy of equipment adjustment and the stability of the phosphine production process.

[0005] In a first aspect of an embodiment of the present application, a method for controlling phosphine production equipment is provided. The phosphine production equipment includes a controller and a plurality of production equipment units communicatively connected to the controller. The plurality of production equipment units include a reaction unit, a water removal unit, a gas collection and compression unit, and a refining unit, which are sequentially arranged. The method is executed by the controller and includes: Obtain real-time operating parameters of each production equipment unit; Mapping the real-time operating parameters to corresponding fuzzy linguistic variables based on a preset membership function; Based on the fuzzy linguistic variables of the refining unit, a target adjustment unit is determined according to a preset top-level fuzzy rule; the target adjustment unit is a production equipment unit that requires parameter adjustment; Based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit, determining a first adjustment instruction corresponding to the target adjustment unit according to a preset middle-level fuzzy rule; If the production equipment unit requiring parameter adjustment indicated in the first adjustment instruction includes the reaction unit, determining a second adjustment instruction for the reaction unit according to a preset underlying fuzzy rule based on the target adjustment unit, the first adjustment instruction, and the fuzzy linguistic variables of the reaction unit; The first adjustment instruction and the second adjustment instruction are issued to corresponding production equipment units to achieve control of each production equipment unit.

[0006] A second aspect of the embodiments of the present application provides a phosphine production equipment control device, which is provided in a controller. The controller is provided in the phosphine production equipment and is communicatively connected to multiple production equipment units in the phosphine production equipment. The multiple production equipment units include a reaction unit, a water removal unit, a gas collection and compression unit, and a refining unit, which are arranged in sequence. The device includes: Data acquisition module, used to obtain real-time operating parameters of each production equipment unit; A data mapping module, configured to map the real-time operating parameters into corresponding fuzzy linguistic variables based on a preset membership function; A top-level processing module, configured to determine a target adjustment unit based on the fuzzy linguistic variables of the refining unit and in accordance with preset top-level fuzzy rules; the target adjustment unit is a production equipment unit requiring parameter adjustment; a middle-level processing module, configured to determine, based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit, a first adjustment instruction corresponding to the target adjustment unit according to a preset middle-level fuzzy rule; a bottom-level processing module configured to determine, when the production equipment units requiring parameter adjustment indicated in the first adjustment instruction include the reaction unit, a second adjustment instruction for the reaction unit based on the target adjustment unit, the first adjustment instruction, and the fuzzy linguistic variables of the reaction unit, according to a preset bottom-level fuzzy rule; The instruction issuing module is used to issue the first adjustment instruction and the second adjustment instruction to the corresponding production equipment unit to realize the control of each production equipment unit.

[0007] In a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned phosphine production equipment control method when executing the computer program.

[0008] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned phosphine production equipment control method are implemented.

[0009] The beneficial effects of the phosphine production equipment control method, device, equipment, and medium provided in the embodiments of the present application are: The embodiments of the present application take into account that the reaction unit, water removal unit, gas collection and compression unit, and refining unit in the phosphine production equipment are a sequentially connected process-based structure, and the parameters of each unit are interrelated (for example, the raw material ratio of the reaction unit affects the load of the water removal unit, which in turn affects the product quality of the refining unit). Therefore, the control of each production equipment unit based on a fuzzy neural network can combine the uncertainty processing capability of fuzzy logic with the self-learning advantages of neural networks. By processing fuzzy information (such as "humidity is too high") through fuzzy rules, and using the dynamic optimization and adjustment strategy of the neural network, the stability of phosphine production and the purity of the product can be guaranteed, and human intervention can be reduced.

[0010] Furthermore, considering the interconnectedness between various production equipment units, directly establishing unified control rules for all units would lead to an explosion in the number of rules. Therefore, the embodiments of the present application pre-set top-level fuzzy rules, middle-level fuzzy rules, and bottom-level fuzzy rules for hierarchical adjustment to reduce the control difficulty of complex systems. Specifically, considering that the refining unit is the final product output link, its operating status (such as product purity and concentration) directly reflects production quality. Therefore, the top-level fuzzy rules use the fuzzy linguistic variables of the refining unit as the core to determine the target adjustment unit. Essentially, this is to reversely locate the source of the problem from the final result, avoid blindly adjusting irrelevant units, and thus improve control efficiency. The middle-level fuzzy rules preliminarily determine the adjustment direction (first adjustment instruction) based on the status of the target adjustment unit and each production equipment unit, narrowing the adjustment range. The bottom-level fuzzy rules refine the adjustment instructions (second adjustment instructions) for the core reaction unit (as the production source, its adjustment influence is the greatest and most sensitive), avoiding chain problems caused by drastic fluctuations in reaction unit parameters.

[0011] Therefore, the embodiment of the present application realizes the logic of "reverse tracing - layered refinement - core focus" through hierarchical fuzzy control, which reduces the control complexity while improving the accuracy of equipment adjustment and the stability of the phosphine production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0013] Figure 1 A schematic diagram of a phosphine production process according to an embodiment of the present application; Figure 2A schematic flow chart of a method for controlling phosphine production equipment provided in one embodiment of the present application; Figure 3 This is a structural block diagram of a phosphine production equipment control device provided in one embodiment of the present application; Figure 4 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0014] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0016] Please refer to Figure 1 In this embodiment, the phosphine production equipment includes a controller and multiple production equipment units communicatively connected to the controller, and the multiple production equipment units include a reaction unit, a water removal unit, a collection and compression unit, a refining unit, a filling unit, a spraying unit and a tail salt unit. The reaction unit is the source equipment. Raw materials such as alkali solution (NaOH), lime milk and yellow phosphorus are added to the reaction unit to generate crude phosphine. The crude phosphine enters the dehydration unit for dehydration treatment, and the dehydrated crude phosphine enters the collection and compression unit. The condensed water from the dehydration unit returns to the reaction unit. The collection and compression unit is used to collect the dehydrated phosphine gas, pressurize it with a compressor and store it in a buffer tank to stabilize the gas pressure to match the feed requirements of the subsequent refining unit. The refining unit removes impurities (such as trace phosphorus vapor and acidic gases) in the gas through an adsorption tower to improve the purity of the phosphine. The filling unit is used to fill the refined high-purity phosphine into cylinders according to specifications. The non-condensable gas not collected in the compression unit and the waste gas generated by the refining unit are collected and sent to the spray unit for neutralization treatment and then discharged in compliance with the standards. The salt solution generated by the reaction unit and the spray liquid after failure are transported to the tail salt unit for deep processing, and the solid by-products generated during the reaction process are collected.

[0017] Please refer to Figure 2 , Figure 2 This is a flow chart of a method for controlling a phosphine production device according to an embodiment of the present application. The method for controlling a phosphine production device according to this embodiment may be executed by a controller. The method may include: S101: Acquire real-time operating parameters of each production equipment unit.

[0018] In this embodiment, the real-time operating parameters of each production equipment unit can be acquired via sensors and transmitted to the controller. For example, the real-time operating parameters of the reaction unit can include temperature, pressure, and feedstock flow rate; the real-time operating parameters of the water removal unit can include outlet humidity and desiccant status; the real-time operating parameters of the gas collection and compression unit can include tank pressure and delivery flow rate; and the real-time operating parameters of the refining unit can include product purity and impurity content.

[0019] S102: Mapping the real-time operating parameters to corresponding fuzzy linguistic variables based on a preset membership function.

[0020] In this embodiment, considering that the reaction unit, water removal unit, gas collection and compression unit, and refining unit in the phosphine production equipment are a process-based structure connected in series, and the parameters of each unit are interrelated (for example, the raw material ratio of the reaction unit affects the load of the water removal unit, which in turn affects the product quality of the refining unit), the use of a fuzzy neural network can combine the uncertainty processing capability of fuzzy logic with the self-learning advantages of neural networks. By processing fuzzy information (such as "humidity is too high") through fuzzy rules, and using the dynamic optimization and adjustment strategy of the neural network, the stability of phosphine production and the purity of the product can be guaranteed, and human intervention can be reduced.

[0021] Specifically, the real-time operating parameters in numerical form can be mapped into fuzzy language variables based on the preset membership function. For example, the reaction temperature of 85°C and the purity of 99.5% can be converted into fuzzy language descriptions that conform to human cognition, such as "high temperature" and "medium purity", providing a basis for subsequent fuzzy control.

[0022] S103: Based on the fuzzy linguistic variables of the refined unit, a target adjustment unit is determined according to a preset top-level fuzzy rule; the target adjustment unit is a production equipment unit that requires parameter adjustment.

[0023] In this embodiment, considering that the reaction unit is the source equipment, its real-time operating parameters (such as reaction temperature and pressure) have the greatest impact on product quality and safety; the refining unit is the final quality control link, and its status directly reflects the production effect. Therefore, this embodiment is guided by the refining unit and fine-tunes the reaction unit.

[0024] Specifically, using the fuzzy linguistic variables of the refining unit (such as "low purity" and "excessive impurities") as input, the unit that needs adjustment, i.e., the target adjustment unit, is located according to the preset top-level fuzzy rules. For example, the preset top-level fuzzy rules may be: if the product purity of the refining unit does not meet the standard and the inlet moisture content exceeds the standard, the water removal unit is determined as the target adjustment unit; if the product purity of the refining unit does not meet the standard and the inlet pressure fluctuates greatly, the gas collection and compression unit is determined as the target adjustment unit; if the product purity of the refining unit does not meet the standard and the inlet impurities and pressure are normal, the refining unit is determined as the target adjustment unit; if the product purity of the refining unit does not meet the standard and the inlet impurities and pressure are normal, the refining unit is determined as the target adjustment unit; if the product purity of the refining unit does not meet the standard and the reaction byproducts in the inlet impurities exceed the standard, the reaction unit is determined as the target adjustment unit.

[0025] S104: Based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit, determine a first adjustment instruction corresponding to the target adjustment unit according to a preset middle-level fuzzy rule.

[0026] In this embodiment, a cross-unit coordinated first adjustment instruction is generated according to preset mid-level fuzzy rules, combining the fuzzy linguistic variables of the target adjustment unit and each production equipment unit. For example, the preset mid-level fuzzy rules may be: if the target adjustment unit is a dehydration unit and the outlet humidity exceeds the standard, the first adjustment instruction output is to increase the adsorption intensity; if the target adjustment unit is a collection and compression unit and the outlet pressure fluctuates significantly, the first adjustment instruction output is to adjust the compressor frequency; if the target adjustment unit is a collection and compression unit, and the humidity of the dehydration unit is normal, the current pressure of the collection and compression unit is low, and the gas production rate of the reaction unit is high, resulting in the entrainment of light components, the first adjustment instruction output is: increase the pressure of the collection and compression unit to 0.4 MPa, and reduce the temperature of the reaction unit to reduce the formation of light components.

[0027] S105: If the production equipment unit that needs parameter adjustment indicated in the first adjustment instruction includes a reaction unit, a second adjustment instruction for the reaction unit is determined according to a preset underlying fuzzy rule based on the target adjustment unit, the first adjustment instruction and the fuzzy linguistic variables of the reaction unit.

[0028] In this embodiment, if the production equipment unit requiring parameter adjustment indicated in the first adjustment instruction includes a reaction unit, this indicates that the target adjustment unit is the reaction unit, or if the target adjustment unit is another production equipment unit, but the first adjustment instruction for the other production equipment unit involves the reaction unit. In this case, guided by the needs of the target adjustment unit and combined with the fuzzy linguistic variables of the reaction unit itself, the second adjustment instruction for the reaction unit is further determined according to the preset underlying fuzzy rules, thereby refining the control of the reaction unit.

[0029] For example, the preset underlying fuzzy rule may be: the target adjustment unit is the reaction unit, and the temperature of the reaction unit is relatively high, the catalyst activity is medium, and the raw material ratio is balanced, then the second adjustment instruction for the reaction unit is: turn on the first level cooling, and reduce the temperature from a relatively high range to a medium range within 3 minutes.

[0030] For example, in the above embodiment, the target adjustment unit is a gas collection and compression unit. The preset underlying fuzzy rule may be: the target adjustment unit is a gas collection and compression unit, the first adjustment instruction includes "the reaction unit lowers the temperature to reduce the generation of light components", and the reaction unit "temperature is high, pressure is medium, and raw material flow is normal", then the second adjustment instruction for the reaction unit is: turn on the auxiliary cooling system to reduce the reaction temperature from high to medium within 5 minutes.

[0031] If the production equipment units that require parameter adjustment indicated in the first adjustment instruction do not include a reaction unit, it indicates that the reaction unit does not need to be adjusted. Therefore, the step of "determining the second adjustment instruction of the reaction unit according to the preset underlying fuzzy rules based on the target adjustment unit, the first adjustment instruction and the fuzzy linguistic variables of the reaction unit" is no longer executed. This can reduce the computing load of the controller and improve the response speed.

[0032] S106: Send the first adjustment instruction and the second adjustment instruction to the corresponding production equipment units to achieve control of each production equipment unit.

[0033] In this embodiment, based on the first adjustment instruction and the second adjustment instruction, the controller will send them to the corresponding production equipment unit according to the directionality of the instruction (such as sending the "increase water removal power" instruction to the water removal unit and sending the "lower reaction temperature" instruction to the reaction unit), thereby realizing the control of each production equipment unit.

[0034] As can be seen from the above, this embodiment takes into account that the reaction unit, water removal unit, gas collection and compression unit, and refining unit in the phosphine production equipment are a process-based structure connected in series, and the parameters of each unit are interrelated (for example, the raw material ratio of the reaction unit affects the load of the water removal unit, which in turn affects the product quality of the refining unit). Therefore, the use of a fuzzy neural network can combine the uncertainty processing capability of fuzzy logic with the self-learning advantages of neural networks. By processing fuzzy information (such as "humidity is too high") through fuzzy rules, and with the help of the neural network dynamic optimization and adjustment strategy, the stability of phosphine production and the purity of the product can be guaranteed, and human intervention can be reduced.

[0035] Furthermore, considering the interconnectedness between various production equipment, directly establishing unified control rules for all units would lead to an explosion in the number of rules. Therefore, this embodiment predefines a hierarchical adjustment system consisting of top-level, middle-level, and bottom-level fuzzy rules to reduce the control difficulty of complex systems. Considering that the refining unit, as the final product output link, has a direct impact on production quality due to its operational status (such as product purity and concentration), the top-level fuzzy rules use the fuzzy linguistic variables of the refining unit as the core to determine the target adjustment unit. This essentially works by working backward from the final result to identify the source of the problem, avoiding blindly adjusting irrelevant units and thus improving control efficiency. The middle-level fuzzy rules preliminarily determine the adjustment direction (first adjustment instruction) based on the status of the target adjustment unit and each production equipment unit, narrowing the adjustment range. The bottom-level fuzzy rules refine the adjustment instructions (second adjustment instructions) for the core reaction unit (as the production source, its adjustment has the greatest impact and is the most sensitive), thus avoiding chain reactions caused by drastic fluctuations in reaction unit parameters.

[0036] Therefore, this embodiment implements the logic of "reverse tracing - layered refinement - core focus" through hierarchical fuzzy control, which reduces the control complexity while improving the accuracy of equipment adjustment and the stability of the phosphine production process.

[0037] In one embodiment of the present application, before mapping the real-time operation parameters to corresponding fuzzy linguistic variables based on a preset membership function, the method further includes: Obtaining a historical operating parameter sequence of each production equipment unit within a first time period before the current moment; the historical operating parameter sequence is a historical data sequence corresponding to each real-time operating parameter; Extract features from historical operating parameter sequences to obtain time series feature parameters; Based on the preset membership function, the real-time operation parameters are mapped to corresponding fuzzy linguistic variables, including: Based on the membership functions corresponding to the real-time operation parameters and the time series characteristic parameters, the real-time operation parameters and the time series characteristic parameters are mapped into fuzzy linguistic variables respectively.

[0038] In this embodiment, given that real-time operating parameters change over time during phosphine production, relying solely on real-time operating parameters makes it difficult to capture trend changes, which can easily lead to lag in regulation. To avoid this problem, the controller can obtain a corresponding historical operating parameter sequence based on the real-time operating data of each production equipment unit within a first time period (e.g., 30 minutes) before the current moment. For example, the temperature / pressure time series curve of the reaction unit, the humidity change sequence of the dehydration unit, the pressure fluctuation data of the compressed air unit, and the purity trend of the refining unit.

[0039] On this basis, feature extraction of historical operating parameter sequences can yield corresponding time series characteristic parameters. These can include trend characteristic parameters (e.g., the average heating rate of a reaction unit), fluctuation characteristic parameters (e.g., the standard deviation of the pressure of a compressed air collection unit), and deviation characteristic parameters (e.g., cumulative deviation). Real-time operating parameters and time series characteristic parameters are mapped to fuzzy linguistic variables using their respective membership functions for subsequent fuzzy control. Time series characteristic parameters can characterize parameter change trends (e.g., "continued rapid temperature rise"), thereby triggering early intervention (e.g., pre-cooling), avoiding lagging adjustments based solely on real-time operating parameters, and reducing production fluctuations.

[0040] In one embodiment of the present application, for each real-time operating parameter, the preset membership function is set in the following manner: If the real-time operating parameter belongs to the first type of operating parameter, a preset membership function is set based on the trapezoidal function; the first type of operating parameter is a real-time operating parameter with a safety limit; If the real-time operating parameter belongs to the second type of operating parameter, a preset membership function is set based on the triangular function; the second type of operating parameter is a real-time operating parameter whose fluctuation value is less than the first threshold; If the real-time operating parameter belongs to the third type of operating parameter, a preset membership function is set based on the Gaussian function; the third type of operating parameter is a real-time operating parameter whose corresponding target control error is less than a second threshold.

[0041] In this embodiment, the real-time operating parameters of each production equipment unit can be categorized, taking into account the characteristics of different types of real-time operating parameters. The first category of operating parameters refers to real-time operating parameters with safety limits, such as temperature and pressure, which all have corresponding safety limit intervals. The second category of operating parameters refers to real-time operating parameters with a gentle change trend and no drastic nonlinear changes, such as humidity. The third category of operating parameters refers to real-time operating parameters that are sensitive to subtle changes and require high control accuracy, such as purity. The first and second thresholds are both preset constants, and those skilled in the art can set the specific values ​​of the first and second thresholds based on actual needs.

[0042] For the first type of operating parameters, the membership function of this type of operating parameters is constructed based on the trapezoidal function, which can maintain a stable membership in the middle interval (such as membership = 1 in the safety limit interval) and quickly transition in the critical area (such as the "dangerous" membership suddenly increases when approaching the upper limit), which can enhance the sensitivity to the safety threshold and avoid boundary blurring caused by fuzzification.

[0043] For the second type of operating parameters, the membership function of this type of operating parameters is constructed based on the triangular function. Since the triangular function has a simple structure (the vertex corresponds to the optimal value and the two sides change linearly), it can simplify the calculation and accurately reflect the symmetrical distribution of "low / normal / high", adapting to the characteristics of stable fluctuations.

[0044] For the third type of operating parameters, the membership function of this type of operating parameters is constructed based on the Gaussian function. Since the Gaussian function has a bell-shaped distribution, the membership changes smoothly near the mean (reflecting the gradual characteristic of "approaching the target") and decays rapidly at both ends (enhancing sensitivity to deviations), which can capture tiny fluctuations and meet the fuzzy requirements of high-precision control.

[0045] From the above, it can be concluded that this embodiment constructs the corresponding membership function based on the characteristics of the real-time operating parameters, which can make the obtained fuzzy language variables more accurate, provide a reliable basis for subsequent rule reasoning, reduce adjustment errors caused by fuzzification deviations, and improve the stability and control accuracy of the production system.

[0046] In one embodiment of the present application, for each time series characteristic parameter, the preset membership function is set as follows: If the time series characteristic parameter belongs to the first type of characteristic parameter or the second type of characteristic parameter, a preset membership function is set based on the triangular function; the first type of characteristic parameter is a parameter used to characterize the rate of change of the corresponding real-time operating parameter, and the second type of characteristic parameter is a parameter used to characterize the degree of fluctuation of the corresponding real-time operating parameter; If the time series characteristic parameter belongs to the third type of characteristic parameter, a preset membership function is set based on the Gaussian function; the third type of characteristic parameter is a parameter used to characterize the degree to which the corresponding real-time operating parameter deviates from the target value.

[0047] In this embodiment, considering the characteristics of different types of time series characteristic parameters, the time series characteristic parameters of each production equipment unit can be classified. The first type of time series characteristic parameters refers to characteristic parameters that characterize the rate of change of real-time operating parameters, such as the temperature change rate; the second type of time series characteristic parameters refers to characteristic parameters that characterize the degree of fluctuation of real-time operating parameters, such as the pressure standard deviation; and the third type of time series characteristic parameters refers to characteristic parameters that characterize the degree of deviation of real-time operating parameters (from the target value), such as the cumulative deviation.

[0048] For the first type of time series characteristic parameters, when mapped into fuzzy linguistic variables, it is necessary to quickly identify "change rate too slow / normal / too fast". The membership function of this type of time series characteristic parameters can be constructed based on the triangular function. Since the triangular function has a symmetrical linear change characteristic, the vertex can correspond to the "normal change rate", and the slopes on both sides can intuitively reflect the degree of "deviation from normal" (for example, the faster the change rate, the linearly increasing membership of "too fast"). The calculation is simple and can quickly respond to dynamic changes.

[0049] For the second type of time series characteristic parameters, when mapping them into fuzzy linguistic variables, they need to be divided into "small / medium / large fluctuations". The linear transition characteristics of the triangular function can clearly distinguish different fluctuation levels and meet the accuracy requirements without complex curves, avoiding the interference caused by overfitting small fluctuations. Therefore, for the second type of time series characteristic parameters, the membership function of this type of time series characteristic parameters can also be constructed based on the triangular function.

[0050] For the third type of time series characteristic parameters, when mapping them into fuzzy linguistic variables, it is necessary to capture the subtle differences of "close to the target / slight deviation / significant deviation". The membership function of this type of characteristic parameters can be constructed based on the Gaussian function. Since the Gaussian function has a bell-shaped distribution, the membership changes smoothly near the mean (target value) (deviations of 0.1% and 0.3% can be distinguished), and decays rapidly at both ends (enhancing sensitivity to significant deviations of more than 1%). It can accurately characterize the continuity and nonlinear characteristics of the "bias degree" and meet the needs of high-precision control for identifying subtle differences.

[0051] From the above, it can be concluded that this embodiment constructs the corresponding membership function based on the characteristics of the time series feature parameters, which can make the obtained fuzzy language variables more accurate, provide a reliable basis for subsequent rule reasoning, reduce adjustment errors caused by fuzzification deviations, and improve the stability and control accuracy of the production system.

[0052] In one embodiment of the present application, the phosphine production equipment control method further includes: For each real-time operating parameter, obtaining a change rate of the real-time operating parameter at multiple first time points within a second time period; If the ratio of the number of second time points to the number of first time points is greater than the first proportion, the center value of the preset membership function is moved along the first direction by the first step length, and the width of the preset membership function is reduced by M times; wherein the second time point is a time point at which the change rate of the real-time operating parameter is greater than the change rate threshold among the multiple first time points, and the first direction is positively correlated with the change rate; If the ratio of the number of the second time points to the number of the first time points is less than or equal to the first proportion, the width of the preset membership function is expanded N times.

[0053] In this embodiment, considering that real-time operating parameters such as pressure and temperature are constantly changing during the phosphine production process, in order to make the fuzzy control rules more consistent with the changes in real-time operating parameters, this embodiment dynamically adjusts the center value and width of the membership function in accordance with the changes in real-time operating parameters.

[0054] Specifically, for each real-time operating parameter, the rate of change (e.g., the temperature increase per second) of a first time point is collected at fixed intervals (e.g., 10 seconds) over a second time period (e.g., 10 minutes), generating multiple data points of rate of change. By setting a rate of change threshold (e.g., temperature change rate > 0.5°C / second), second time points with larger rates of change are selected, and the ratio of their number to the total number of first time points is calculated.

[0055] If the ratio of the number of second time points to the number of first time points is greater than the first proportion (such as 30%), it indicates that the real-time operating parameter changes rapidly (such as a continuous sudden rise in temperature). The center value of the corresponding membership function is moved by the first step length (such as +2°C) in the direction of change (such as the direction of temperature increase), and the width is reduced by M times (such as 0.2 times) to focus on the high change interval.

[0056] If the ratio of the number of second time points to the number of first time points is less than or equal to the first ratio, it indicates that the real-time operating parameter changes smoothly. The width of the membership function is expanded N times (for example, by 0.1 times, that is, expanded to 1.1 times the original width) to enhance the tolerance to small fluctuations.

[0057] From the above, it can be concluded that this embodiment dynamically adjusts the center value and width of the membership function by tracking the change rate of real-time operating parameters, so that the mapped fuzzy linguistic variables can be more in line with the real-time status (for example, when the temperature rises suddenly, the center of the membership function moves up accordingly, and the "high" status can be quickly identified), avoiding the failure of fuzzy rules caused by dynamic changes in real-time operating parameters and improving the level of intelligent control.

[0058] In one embodiment of the present application, the phosphine production equipment control method further includes: Based on the fuzzy linguistic variables corresponding to the real-time operating parameters of each production equipment unit, the fault status of the production equipment unit is determined according to the preset fault fuzzy rules; If there is a production equipment unit with a minor fault status, the production equipment unit with the minor fault is determined as a first fault unit; based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit, a first adjustment instruction corresponding to the target adjustment unit is determined according to a preset middle-level fuzzy rule, including: Based on the target adjustment unit, the fuzzy linguistic variables of each production equipment unit and the fault state of the first fault unit, a first adjustment instruction corresponding to the target adjustment unit is determined according to a preset middle-level fuzzy rule.

[0059] In this embodiment, fault fuzzy rules can be set in advance (such as "temperature fluctuates violently and pressure rises suddenly → serious fault"), and on this basis, based on the fuzzy language variables corresponding to the real-time operating parameters of each production equipment unit, the fault status of each production equipment unit (no fault / minor fault / serious fault) can be determined according to the preset fault fuzzy rules.

[0060] From the above determination results, units with a "minor fault" status (e.g., "slightly high humidity" for the dehumidification unit) are selected and marked as the first faulty unit. During the mid-level fuzzy rule reasoning process, in addition to the target regulation unit (e.g., the gas collection and compression unit) and the conventional fuzzy linguistic variables of each unit (e.g., "low pressure" and "normal gas production rate"), the fault status of the first faulty unit (e.g., "slightly high humidity" for the dehumidification unit) is also considered. Based on this, the first regulation instruction for the target regulation unit is determined (e.g., "increase the pressure of the gas collection and compression unit to 0.4 MPa, and simultaneously reduce the load of the reaction unit by 5% to relieve the pressure of the dehumidification unit").

[0061] From the above, it can be concluded that this embodiment determines the fault status of each production equipment unit based on the preset fault fuzzy rules, and adds minor faults to the middle-level fuzzy rule reasoning process. The first adjustment instruction can be adjusted according to the minor fault status, and then the minor fault can be intervened in time to avoid downtime losses caused by further deterioration of the fault.

[0062] In one embodiment of the present application, the phosphine production equipment control method further includes: Based on the fuzzy linguistic variables corresponding to the real-time operating parameters of each production equipment unit, the fault status of the production equipment unit is determined according to the preset fault fuzzy rules; If there is a production equipment unit whose fault status is a serious fault, a third adjustment instruction is output according to a preset emergency processing rule.

[0063] In this embodiment, fault fuzzy rules can be set in advance (such as "temperature fluctuates violently and pressure rises suddenly → serious fault"), and on this basis, based on the fuzzy language variables corresponding to the real-time operating parameters of each production equipment unit, the fault status of each production equipment unit (no fault / minor fault / serious fault) can be determined according to the preset fault fuzzy rules.

[0064] If the above judgment results indicate that there is an equipment unit with a "serious fault" status (such as over-temperature and over-pressure of the reactor, gas leakage detection exceeding the standard, etc.), it is necessary to immediately follow the preset emergency processing rules (such as "immediately cut off the raw material feed of the reaction unit and start the emergency cooling system", "close the upstream and downstream valves of the leakage unit and start the inert gas purge", "trigger the sound and light alarm and link the shutdown program"), output the third adjustment instruction, and realize automatic emergency control.

[0065] From the above, it can be concluded that this embodiment pre-sets fault fuzzy rules to quickly identify serious faults and output the third adjustment instruction, which can cut off the risk source (such as stopping material, cooling, and isolation) within seconds or minutes, minimize the duration of the danger, and reduce the probability of safety accidents.

[0066] Corresponding to the phosphine production equipment control method of the above embodiment, Figure 3 This is a block diagram of the structure of a phosphine production equipment control device provided in one embodiment of the present application. The device is provided in a controller, which is provided in the phosphine production equipment. The controller is in communication with multiple production equipment units in the phosphine production equipment. The multiple production equipment units include a reaction unit, a water removal unit, a gas collection and compression unit, and a refining unit. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 3 The phosphine production equipment control device 20 includes: a data acquisition module 21, a data mapping module 22, a top-level processing module 23, a middle-level processing module 24, a bottom-level processing module 25 and an instruction issuing module 26. Among them, the data acquisition module 21 is used to obtain the real-time operating parameters of each production equipment unit; A data mapping module 22 is used to map the real-time operating parameters into corresponding fuzzy linguistic variables based on a preset membership function; The top-level processing module 23 is used to determine the target adjustment unit based on the fuzzy linguistic variables of the refined unit according to the preset top-level fuzzy rules; the target adjustment unit is the production equipment unit that needs to be parameter adjusted; The middle-level processing module 24 is configured to determine a first adjustment instruction corresponding to the target adjustment unit according to a preset middle-level fuzzy rule based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit; A bottom-level processing module 25 is configured to determine a second adjustment instruction for the reaction unit according to a preset bottom-level fuzzy rule based on the target adjustment unit, the first adjustment instruction, and the fuzzy linguistic variables of the reaction unit, when the production equipment unit requiring parameter adjustment indicated in the first adjustment instruction includes a reaction unit; The instruction issuing module 26 is used to issue the first adjustment instruction and the second adjustment instruction to the corresponding production equipment unit to achieve control of each production equipment unit.

[0067] In one embodiment of the present application, before mapping the real-time operating parameters to corresponding fuzzy linguistic variables based on a preset membership function, the data acquisition module 21 is further configured to: Obtaining a historical operating parameter sequence of each production equipment unit within a first time period before the current moment; the historical operating parameter sequence is a historical data sequence corresponding to each real-time operating parameter; Extract features from historical operating parameter sequences to obtain time series feature parameters; Based on the preset membership function, the real-time operation parameters are mapped to corresponding fuzzy linguistic variables, including: Based on the membership functions corresponding to the real-time operation parameters and the time series characteristic parameters, the real-time operation parameters and the time series characteristic parameters are mapped into fuzzy linguistic variables respectively.

[0068] In one embodiment of the present application, the data mapping module 22 is further configured to: For each real-time operating parameter, if the real-time operating parameter belongs to a first-category operating parameter, a preset membership function is set based on a trapezoidal function; the first-category operating parameter is a real-time operating parameter with a safety limit; If the real-time operating parameter belongs to the second type of operating parameter, a preset membership function is set based on the triangular function; the second type of operating parameter is a real-time operating parameter whose fluctuation value is less than the first threshold; If the real-time operating parameter belongs to the third type of operating parameter, a preset membership function is set based on the Gaussian function; the third type of operating parameter is a real-time operating parameter whose corresponding target control error is less than a second threshold.

[0069] In one embodiment of the present application, the data mapping module 22 is further configured to: For each time series characteristic parameter, if the time series characteristic parameter belongs to the first type of characteristic parameter or the second type of characteristic parameter, a preset membership function is set based on the triangular function; the first type of characteristic parameter is a parameter used to characterize the rate of change of the corresponding real-time operating parameter, and the second type of characteristic parameter is a parameter used to characterize the degree of fluctuation of the corresponding real-time operating parameter; If the time series characteristic parameter belongs to the third type of characteristic parameter, a preset membership function is set based on the Gaussian function; the third type of characteristic parameter is a parameter used to characterize the degree to which the corresponding operating parameter deviates from the target value.

[0070] In one embodiment of the present application, the data mapping module 22 is further configured to: For each real-time operating parameter, obtaining a change rate of the real-time operating parameter at multiple first time points within a second time period; If the ratio of the number of second time points to the number of first time points is greater than the first proportion, the center value of the preset membership function is moved along the first direction by the first step length, and the width of the preset membership function is reduced by M times; wherein the second time point is a time point at which the change rate of the real-time operating parameter is greater than the change rate threshold among the multiple first time points, and the first direction is positively correlated with the change rate; If the ratio of the number of the second time points to the number of the first time points is less than or equal to the first proportion, the width of the preset membership function is expanded N times.

[0071] In one embodiment of the present application, the middle-level processing module 24 is further configured to: Based on the fuzzy linguistic variables corresponding to the real-time operating parameters of each production equipment unit, the fault status of the production equipment unit is determined according to the preset fault fuzzy rules; If there is a production equipment unit with a minor fault status, the production equipment unit with the minor fault is determined as a first fault unit; based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit, a first adjustment instruction corresponding to the target adjustment unit is determined according to a preset middle-level fuzzy rule, including: Based on the target adjustment unit, the fuzzy linguistic variables of each production equipment unit and the fault state of the first fault unit, a first adjustment instruction corresponding to the target adjustment unit is determined according to a preset middle-level fuzzy rule.

[0072] In one embodiment of the present application, the middle-level processing module 24 is further configured to: Based on the fuzzy linguistic variables corresponding to the real-time operating parameters of each production equipment unit, the fault status of the production equipment unit is determined according to the preset fault fuzzy rules; If there is a production equipment unit whose fault status is a serious fault, a third adjustment instruction is output according to a preset emergency processing rule.

[0073] See also Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 4 The electronic device 300 in the embodiment shown can be a controller, including: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 3 The functions of the data acquisition module 21, the data mapping module 22, the top-level processing module 23, the middle-level processing module 24, the bottom-level processing module 25 and the instruction issuing module 26 are shown.

[0074] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0075] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0076] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also contain preset constants such as a first threshold, a second threshold, and a rate of change threshold.

[0077] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present application can execute the implementation method described in the phosphine production equipment control method provided in the embodiments of the present application, and can also execute the implementation method of the electronic device described in the embodiments of the present application, which will not be repeated here.

[0078] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0079] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0080] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0081] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0083] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0084] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0085] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for controlling a phosphine production facility, wherein the phosphine production facility comprises a controller and a plurality of production equipment units communicatively connected to the controller, wherein the plurality of production equipment units comprise a reaction unit, a water removal unit, a gas collection and compression unit, and a refining unit, which are sequentially arranged. The method is executed by the controller, and includes: Obtain real-time operating parameters of each production equipment unit; Mapping the real-time operating parameters to corresponding fuzzy linguistic variables based on a preset membership function; Based on the fuzzy linguistic variables of the refining unit, a target adjustment unit is determined according to a preset top-level fuzzy rule; the target adjustment unit is a production equipment unit that requires parameter adjustment; Based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit, determining a first adjustment instruction corresponding to the target adjustment unit according to a preset middle-level fuzzy rule; If the production equipment unit requiring parameter adjustment indicated in the first adjustment instruction includes the reaction unit, determining a second adjustment instruction for the reaction unit according to a preset underlying fuzzy rule based on the target adjustment unit, the first adjustment instruction, and the fuzzy linguistic variables of the reaction unit; The first adjustment instruction and the second adjustment instruction are issued to corresponding production equipment units to achieve control of each production equipment unit.

2. The phosphine production equipment control method according to claim 1, characterized in that: Before mapping the real-time operation parameters to corresponding fuzzy linguistic variables based on a preset membership function, the method further includes: Obtaining a historical operating parameter sequence of each production equipment unit within a first time period before the current moment; the historical operating parameter sequence is a historical data sequence corresponding to each real-time operating parameter; Extracting features from the historical operating parameter sequence to obtain time series feature parameters; The mapping of the real-time operating parameters into corresponding fuzzy linguistic variables based on a preset membership function includes: Based on the membership functions corresponding to the real-time operation parameters and the time series characteristic parameters respectively, the real-time operation parameters and the time series characteristic parameters are mapped into fuzzy linguistic variables respectively.

3. The phosphine production equipment control method according to claim 1, characterized in that: For each real-time operating parameter, the preset membership function is set in the following manner: If the real-time operating parameter belongs to a first type of operating parameter, setting the preset membership function based on a trapezoidal function; the first type of operating parameter is a real-time operating parameter with a safety limit; If the real-time operating parameter belongs to the second category of operating parameters, setting the preset membership function based on the triangular function; the second category of operating parameters is a real-time operating parameter whose fluctuation value is less than the first threshold; If the real-time operating parameter belongs to the third category operating parameter, the preset membership function is set based on the Gaussian function; the third category operating parameter is a real-time operating parameter whose corresponding target control error is less than a second threshold.

4. The phosphine production equipment control method according to claim 2, characterized in that: For each time series characteristic parameter, the preset membership function is set in the following manner: If the time series characteristic parameter belongs to the first type of characteristic parameter or the second type of characteristic parameter, the preset membership function is set based on the triangular function; the first type of characteristic parameter is a parameter used to characterize the rate of change of the corresponding real-time operating parameter, and the second type of characteristic parameter is a parameter used to characterize the degree of fluctuation of the corresponding real-time operating parameter; If the time series characteristic parameter belongs to the third type of characteristic parameter, the preset membership function is set based on the Gaussian function; the third type of characteristic parameter is a parameter used to characterize the degree to which the corresponding real-time operating parameter deviates from the target value.

5. The phosphine production equipment control method according to claim 1, characterized in that: Also includes: For each real-time operating parameter, obtaining a change rate of the real-time operating parameter at multiple first time points within a second time period; If the ratio of the number of second time points to the number of first time points is greater than the first proportion, the center value of the preset membership function is moved along the first direction by the first step length, and the width of the preset membership function is reduced by M times; wherein the second time point is a time point in the plurality of first time points at which the rate of change of the real-time operating parameter is greater than a change rate threshold, and the first direction is positively correlated with the change rate; If the ratio of the number of the second time points to the number of the first time points is less than or equal to the first proportion, the width of the preset membership function is expanded N times.

6. The phosphine production equipment control method according to claim 1, characterized in that: Also includes: Based on the fuzzy linguistic variables corresponding to the real-time operating parameters of each production equipment unit, the fault status of the production equipment unit is determined according to the preset fault fuzzy rules; If there is a production equipment unit with a minor fault status, the production equipment unit with the minor fault is determined as a first fault unit; and determining the first adjustment instruction corresponding to the target adjustment unit according to a preset middle-level fuzzy rule based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit, includes: Based on the target adjustment unit, the fuzzy linguistic variables of each production equipment unit and the fault state of the first fault unit, a first adjustment instruction corresponding to the target adjustment unit is determined according to a preset middle-level fuzzy rule.

7. The phosphine production equipment control method according to claim 1, characterized in that: Also includes: Based on the fuzzy linguistic variables corresponding to the real-time operating parameters of each production equipment unit, the fault status of the production equipment unit is determined according to the preset fault fuzzy rules; If there is a production equipment unit whose fault status is a serious fault, a third adjustment instruction is output according to a preset emergency processing rule.

8. A phosphine production equipment control device, provided in a controller, wherein the controller is provided in the phosphine production equipment, and the controller is communicatively connected with a plurality of production equipment units in the phosphine production equipment, wherein the plurality of production equipment units include a reaction unit, a water removal unit, a gas collection and compression unit, and a refining unit provided in sequence, characterized in that: The phosphine production equipment control device includes: Data acquisition module, used to obtain real-time operating parameters of each production equipment unit; A data mapping module, configured to map the real-time operating parameters into corresponding fuzzy linguistic variables based on a preset membership function; A top-level processing module, configured to determine a target adjustment unit based on the fuzzy linguistic variables of the refining unit and in accordance with preset top-level fuzzy rules; the target adjustment unit is a production equipment unit requiring parameter adjustment; a middle-level processing module, configured to determine, based on the target adjustment unit and the fuzzy linguistic variables of each production equipment unit, a first adjustment instruction corresponding to the target adjustment unit according to a preset middle-level fuzzy rule; a bottom-level processing module configured to determine, when the production equipment units requiring parameter adjustment indicated in the first adjustment instruction include the reaction unit, a second adjustment instruction for the reaction unit based on the target adjustment unit, the first adjustment instruction, and the fuzzy linguistic variables of the reaction unit, according to a preset bottom-level fuzzy rule; The instruction issuing module is used to issue the first adjustment instruction and the second adjustment instruction to the corresponding production equipment unit to realize the control of each production equipment unit.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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