Valve flow characteristic control method for precision equipment

Abstract

The present invention discloses a valve flow characteristic control method for precision equipment, which involves the field of automatic control. The method includes: obtaining valve distribution nodes of precision equipment, using distribution nodes as flow characteristic monitoring nodes, generating a monitoring network, storing flow characteristic monitoring data sets, performing pressure relief hazard correlation analysis based on the data sets, outputting internal hazard related factors, performing pressure relief hazard assessment, outputting pressure relief hazard indicators, decomposing the monitoring network, obtaining a first decomposition network and a second decomposition network, directly controlling valves in the first decomposition network, and performing step control on valves in the second decomposition network. The method solves the technical problems that the prior art lacks comprehensive and accurate monitoring and differentiated control of precision equipment valve systems, and is difficult to deal with pressure relief hazards caused by factors such as valve flow characteristics, and achieves refined management and effective control of precision equipment valve systems, reducing the technical effect of pressure relief hazards.

Description

Technical Field

[0001] The present application relates to the field of automatic control, and in particular to a method for controlling flow characteristics of valves used in precision equipment. Background Art

[0002] In precision equipment-related scenarios, the safe and stable operation of the valve system is of vital importance. It is particularly important to carry out refined management and effective control of the valve system to avoid the risk of pressure relief. Scientific and efficient management of the valve system to better meet the needs of different valve states has become the key to ensuring the safe operation of precision equipment. However, traditional precision equipment valve management is usually one-sided and partial, mainly relying on some conventional means, with insufficient overall understanding of the entire valve system, lack of in-depth mining and analysis of relevant data, and difficulty in comprehensively and accurately assessing the pressure relief risk status under each valve state. The formulation of management plans is relatively simple and rigid, and cannot adapt well to the complex and changeable valve conditions and pressure relief risk situations of precision equipment in actual operation.

[0003] At the current stage, relevant technologies lack comprehensive and accurate monitoring and differentiated control of precision equipment valve systems, making it difficult to deal with technical problems caused by pressure relief hazards due to factors such as valve flow characteristics. Summary of the invention

[0004] This application provides a valve flow characteristic control method for precision equipment, which uses the method of obtaining valve distribution nodes of precision equipment;

[0005] A valve flow monitoring network is generated based on valve distribution nodes as flow characteristic monitoring nodes. Each flow characteristic monitoring node is used to store the flow characteristic monitoring data set of the corresponding valve. A pressure relief hazard correlation analysis is performed on each flow characteristic monitoring data set based on the valve flow monitoring network, and an internal hazard correlation factor corresponding to each node is output. A pressure relief hazard assessment is performed based on the internal hazard correlation factor corresponding to each node, and a pressure relief hazard index based on the valve flow monitoring network is output. The valve flow monitoring network is decomposed based on the pressure relief hazard index to obtain a first decomposition network and a second decomposition network. The valves in the first decomposition network are directly controlled, and the valves in the second decomposition network are step-controlled, thereby achieving refined management and effective control of the valve system of precision equipment and reducing the technical effect of pressure relief hazard.

[0006] The present application provides a valve flow characteristic control method for precision equipment, including:

[0007] Obtain valve distribution nodes of precision equipment; generate a valve flow monitoring network based on the valve distribution nodes as flow characteristic monitoring nodes, each flow characteristic monitoring node is used to store the flow characteristic monitoring data set of the corresponding valve; perform pressure relief hazard correlation analysis on each flow characteristic monitoring data set based on the valve flow monitoring network, and output the internal hazard correlation factor corresponding to each node; perform pressure relief hazard assessment based on the internal hazard correlation factor corresponding to each node, and output a pressure relief hazard index based on the valve flow monitoring network; decompose the valve flow monitoring network according to the pressure relief hazard index to obtain a first decomposition network and a second decomposition network, wherein the first decomposition network is composed of nodes whose pressure relief hazard index is less than a preset hazard threshold, and the second decomposition network is composed of nodes whose pressure relief hazard index is greater than or equal to the preset hazard threshold; directly control the valves in the first decomposition network, and perform step control on the valves in the second decomposition network.

[0008] The valve flow characteristic control method for precision equipment proposed in this application is to first obtain the valve distribution nodes of the precision equipment, generate a valve flow monitoring network based on the valve distribution nodes as flow characteristic monitoring nodes, each flow characteristic monitoring node is used to store the flow characteristic monitoring data set of the corresponding valve, and perform pressure relief hazard correlation analysis on each flow characteristic monitoring data set according to the valve flow monitoring network, output the internal hazard correlation factor corresponding to each node, perform pressure relief hazard assessment based on the internal hazard correlation factor corresponding to each node, output the pressure relief hazard index based on the valve flow monitoring network, decompose the valve flow monitoring network according to the pressure relief hazard index, obtain a first decomposition network and a second decomposition network, directly control the valves in the first decomposition network, and perform step control on the valves in the second decomposition network, thereby achieving refined management and effective control of the valve system of precision equipment and reducing the technical effect of pressure relief hazard. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0010] Figure 1 A flow chart of a method for controlling valve flow characteristics of precision equipment provided in an embodiment of the present application.

[0011] Figure 2A schematic diagram of a step control process for a valve flow characteristic control method for precision equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0013] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0014] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0015] The present application embodiment provides a valve flow characteristic control method for precision equipment, such as Figure 1 As shown, the method includes:

[0016] Step S100, obtaining valve distribution nodes of precision equipment.

[0017] Specifically, we conduct a comprehensive survey and understanding of the precision equipment, and preliminarily grasp the approximate area of ​​the valve by consulting the technical drawings, manuals and other information of the equipment. We then use professional testing equipment, such as infrared thermal imagers and ultrasonic flaw detectors, to scan and detect the equipment area by area. The testing equipment will accurately locate the position of the valve based on the differences in physical properties between the valve and other components, such as temperature changes and sound wave reflections. Ultimately, we determine the specific position of the valve and mark it as a valve distribution node. We repeat the above process to find out all the valve distribution nodes in the equipment, providing accurate basic information for subsequent work such as building a flow monitoring network.

[0018] Step S200: generating a valve flow monitoring network according to the valve distribution nodes as flow characteristic monitoring nodes, wherein each flow characteristic monitoring node is used to store a flow characteristic monitoring data set of a corresponding valve.

[0019] Specifically, the valve distribution nodes of the precision equipment that have been obtained are identified, and these nodes are set as flow characteristic monitoring nodes one by one, and a valve flow monitoring network is established. The network connects these monitoring nodes to each other, and a dedicated storage space is reserved for each flow characteristic monitoring node to store the flow characteristic monitoring data set of the corresponding valve, including the valve corresponding to the node, the flow value at different times, and other relevant characteristic data, such as pressure, temperature, etc., which are recorded and stored in the data set storage space of the corresponding monitoring node as the flow characteristic monitoring data of the valve. In the valve flow monitoring network, each monitoring node carries rich flow characteristic data about the corresponding valve.

[0020] Step S300: performing pressure relief risk correlation analysis on each flow characteristic monitoring data set according to the valve flow monitoring network, and outputting an internal risk correlation factor corresponding to each node.

[0021] Specifically, after constructing the valve flow monitoring network and obtaining various flow characteristic monitoring data sets, a pressure relief hazard correlation analysis is performed, and each data set is interpreted and extracted in detail. The flow characteristic monitoring data set contains different pressure values, flow indicators, abnormal conditions, etc., as well as corresponding time points. By calculating the change rate between adjacent flow characteristic monitoring data sets, a series of dynamic information on flow characteristic monitoring data changes can be obtained. The hazard correlation factor is obtained by multiplying the sum of the flow characteristic monitoring data change rates by a weight coefficient, adding the average flow characteristic monitoring data value multiplied by another weight coefficient, and adding the time when the flow characteristic monitoring data continues to rise multiplied by the third weight coefficient. By performing such analysis and calculation on the data set corresponding to each node, the internal hazard correlation factors corresponding to each node are finally output. These factors reflect the degree of correlation between each node in terms of pressure relief hazard, thereby providing an important basis for further safety assessment and decision-making.

[0022] Step S400, performing pressure relief risk assessment according to the internal risk-related factors corresponding to each node, and outputting a pressure relief risk index based on the valve flow monitoring network.

[0023] Specifically, after obtaining the internal hazard related factors corresponding to each node, a pressure relief hazard assessment is carried out, the internal hazard related factors of each node are comprehensively analyzed, and all internal hazard related factors are weighted and summed. Based on the internal hazard related factors of each node, an indicator that comprehensively reflects the degree of pressure relief hazard of the entire valve flow monitoring network is obtained, namely, the pressure relief hazard index, which intuitively displays the dangerous conditions faced by the current system in terms of pressure relief. Further risk response and decision-making are carried out based on the indicator, such as taking corresponding maintenance measures or adjusting operating parameters, etc., to reduce potential pressure relief hazards and ensure the safe and stable operation of the system.

[0024] In a possible implementation, the pressure relief risk assessment is performed according to the internal risk-related factors corresponding to each node, and the pressure relief risk index based on the valve flow monitoring network is output. Step S400 further includes step S410, obtaining the internal risk-related factors, wherein the internal risk-related factors are obtained from the valve internal flow monitoring data, including the flow coefficient, valve opening, pressure difference on both sides, and flow abnormality index. Specifically, starting the monitoring system to collect the valve internal flow monitoring data includes various sensors and detection equipment to accurately obtain the required data, wherein the flow coefficient is determined by in-depth analysis and calculation of the valve internal structure and fluid characteristics, the valve opening is obtained by using an angle sensor or other related measuring device installed on the valve, the pressure difference on both sides of the valve is measured by a high-precision pressure sensor, and the flow abnormality index is evaluated by using flow monitoring equipment and data analysis algorithms. By collecting and processing the valve internal flow monitoring data, the specific content of the internal risk-related factors is successfully obtained. The internal risk-related factors comprehensively reflect the potential dangers that the valve may face in the current state, and provide a key data basis and basis for subsequent more in-depth analysis, evaluation and taking corresponding countermeasures.

[0025] Step S420, perform pressure relief risk assessment according to the internal risk-related factors, obtain the internal-pressure relief risk index, and output the internal-pressure relief risk index as the pressure relief risk index. Specifically, clarify the internal risk-related factors obtained, such as the flow coefficient, valve opening, pressure difference on both sides, and flow abnormality index, set a weight for the flow coefficient, give the valve opening a corresponding weight according to its impact on the risk, give the pressure difference on both sides a weight according to its importance, and set a corresponding weight for the flow abnormality index. By multiplying each internal risk-related factor with their respective weights, and then adding these product results, a comprehensive result is obtained, and the calculated internal-pressure relief risk index is output as the pressure relief risk index, so that the subsequent judgment and corresponding measures can be taken according to this index to ensure the safety and stability of the system. Therefore, this implementation method achieves the technical effect of providing accurate evaluation for subsequent analysis, decision-making or set thresholds.

[0026] In a possible implementation, a pressure relief risk assessment is performed according to the internal risk-related factors, an internal-pressure relief risk index is obtained, and the internal-pressure relief risk index is output as a pressure relief risk index. Step S420 further includes step S421, analyzing the valve flow monitoring network to obtain external risk-related factors, wherein the external risk-related factors are obtained by valve distribution characteristics, including the number of connected nodes, the importance of the location, and the historical failure rate of the node. Specifically, the valve flow monitoring network is deeply analyzed, and for each valve, external risk-related factors are obtained, including the number of connected nodes and the importance of the location, which are determined by an established evaluation system to obtain the importance of the location of each valve and the historical failure rate of the node. These factors are combined to determine the external risk-related factors, and the number of external connected nodes is multiplied by a weight coefficient, and the importance of the location is multiplied by the weight coefficient, and the historical failure rate of the node is multiplied by the weight coefficient to obtain the external risk-related factors. By performing similar analysis and calculation on each valve in the network, a comprehensive set of external risk-related factors is obtained, which provides an important data basis for subsequent evaluation and decision-making.

[0027] Step S422, perform pressure relief hazard assessment according to the external hazard related factors to obtain an external-pressure relief hazard index. Specifically, clarify the external hazard related factors that have been obtained, such as the number of connected nodes, the score of the importance of the location, and the historical failure rate of the node, and calculate the external-pressure relief hazard index by multiplying the number of connected nodes by the corresponding weight coefficient, the position importance score by the corresponding weight coefficient, and the historical failure rate of the node by the corresponding weight coefficient. The external-pressure relief hazard index that can reflect the degree of pressure relief hazard is calculated for subsequent further analysis and decision-making.

[0028] Step S423, weighted calculation is performed according to the external-pressure relief hazard index and the internal-pressure relief hazard index to update the pressure relief hazard index. Specifically, according to the external-pressure relief hazard index and the internal-pressure relief hazard index that have been obtained, the weight ratio used for weighted calculation is determined, and then weighted calculation is performed. By multiplying the external-pressure relief hazard index and the internal-pressure relief hazard index by their respective weighting coefficients, and then adding the results, an updated pressure relief hazard index is obtained, and the operation of performing weighted calculation according to the external-pressure relief hazard index and the internal-pressure relief hazard index to update the pressure relief hazard index is completed.

[0029] Step S500, decomposing the valve flow monitoring network according to the pressure relief hazard index to obtain a first decomposition network and a second decomposition network, wherein the first decomposition network is composed of nodes whose pressure relief hazard index is less than a preset hazard threshold, and the second decomposition network is composed of nodes whose pressure relief hazard index is greater than or equal to the preset hazard threshold.

[0030] Specifically, after obtaining the pressure relief hazard index, the valve flow monitoring network is decomposed, and a preset hazard threshold is set according to actual needs. The pressure relief hazard index corresponding to each node is checked one by one. If the pressure relief hazard index of a node is less than the preset hazard threshold, then the node is divided into the first decomposition network. If the pressure relief hazard index of the node is greater than or equal to the preset hazard threshold, it belongs to the second decomposition network. Repeat the judgment and division until all nodes are classified into the corresponding decomposition network. In this way, the valve flow monitoring network is successfully decomposed into the first decomposition network (including nodes whose pressure relief hazard index is less than the preset hazard threshold) and the second decomposition network (including nodes whose pressure relief hazard index is greater than or equal to the preset hazard threshold), so as to manage and process nodes with different hazard levels in a more targeted manner in the future, reduce potential risks and ensure the safe operation of the entire system.

[0031] Step S600: directly control the valves in the first decomposition network, and perform step control on the valves in the second decomposition network.

[0032] Specifically, the valve flow monitoring network is decomposed to obtain the first decomposition network and the second decomposition network, and different control strategies are implemented. For the first decomposition network, since the pressure relief hazard index of its nodes is less than the preset hazard threshold, it means that the risk is relatively low, and the valves in these nodes are directly controlled. For example, if the current state of a valve is 50% open, it is directly adjusted to 70% open as needed. For the second decomposition network, the pressure relief hazard index of its nodes is greater than or equal to the preset hazard threshold, indicating that the risk is high and needs to be controlled more cautiously, that is, step control is adopted. Assuming that the current state of a valve is open, the step control may first adjust it by a smaller amplitude, observe it for a period of time, and then decide whether to continue to adjust it based on further monitoring data and analysis. In this step-by-step and phased manner, the valve is accurately controlled to avoid adverse consequences caused by too hasty adjustments. By directly controlling the valves in the first decomposition network, conventional adjustment and management can be quickly achieved, while step control of the valves in the second decomposition network can be more meticulously dealt with in higher risk situations to ensure the safety and stability of the system during the control process. Differentiated control strategies are implemented according to the characteristics of different networks to achieve the best control effect and ensure the safe operation of the system.

[0033] In one possible implementation, the valves in the first decomposition network are directly controlled, and the valves in the second decomposition network are step-controlled. Step S600 further includes step S610, performing control optimization on the M nodes corresponding to the first decomposition network, and performing control optimization on the N nodes corresponding to the second decomposition network to generate a first initial solution set and a second initial solution set, wherein the first initial solution set includes M initial control parameter solutions, and the second initial solution set includes N initial control parameter solutions. Specifically, control optimization is started for the M nodes of the first decomposition network, different control strategies and parameter settings are considered, different control methods are set, and the possible impacts of these methods on the node are analyzed one by one. After evaluation and comparison, a relatively better one is selected from these methods and determined as an initial control parameter solution for the node. The above process is repeated to obtain M initial control parameter solutions for different nodes, which together constitute the first initial solution set. Similar control optimization is performed on the N nodes of the second decomposition network. For each node, many control possibilities are explored and screened, and a trade-off is made. Finally, a better one is selected as the initial control parameter solution for the node, and a second initial solution set containing N initial control parameter solutions is obtained. The control optimization of all nodes and the generation of the initial solution set are completed in this step.

[0034] Step S620, perform pressure relief stability assessment based on the first initial solution set and the second initial solution set to obtain first stability and second stability. Specifically, obtain the first initial solution set and the second initial solution set, perform pressure relief stability assessment, analyze each initial control parameter solution in the first initial solution set one by one, and take each solution as a factor combination that affects pressure relief stability. Calculate a stability value corresponding to each factor combination through the factor combination, the degree of influence of different parameters on the pressure release rate, or the effect on the overall balance of the system. Combine all these values ​​to obtain a result that can represent the overall pressure relief stability of the first initial solution set, and obtain the first stability. Perform the same operation on the second initial solution set to obtain each stability value corresponding to the second initial solution set, and then integrate and process them to finally obtain a result reflecting the overall pressure relief stability of the second initial solution set, that is, the second stability.

[0035] Step S630, calculating the first stability and the second stability to obtain fitting stability, if the fitting stability does not meet the expected stability threshold, optimizing with the expected stability threshold as the target, and outputting a first optimization solution set and a second optimization solution set. Specifically, the influence of each parameter in the first initial solution set on the system stability is comprehensively considered, and a numerical value representing the first stability is obtained by weighted summing the parameters. The second initial solution set is also calculated accordingly to obtain the second stability. The first stability and the second stability are weighted and summed according to different weights to obtain the fitting stability. Check whether the fitting stability meets the expected stability threshold. If not, start the optimization with the expected stability threshold as the target. First, try to fine-tune the parameter solution in the first initial solution set, recalculate the first stability according to the new parameter solution, and then calculate the new fitting stability in combination with the second initial solution set. If this new fitting stability is closer to or reaches the expected stability threshold, then it is a step in the right direction. If not, continue to try other adjustment methods, and perform similar exploration and adjustment on the parameters in the second initial solution set. After a large number of calculations and comparisons, gradually approach the expected stability target until the most suitable parameter configuration is found, forming the final first optimization solution set and the second optimization solution set, outputting the first optimization solution set and the second optimization solution set. The optimized result has significantly improved in stability compared with the initial solution set, and is closer to or reaches the expected stability threshold.

[0036] Step S640, directly control the valves in the first decomposition network according to the first optimal solution set, and perform step control on the valves in the second decomposition network according to the second optimal solution set. Specifically, the first optimal solution set and the second optimal solution set are obtained, the valve control in the first decomposition network is started, and the corresponding valve is directly and accurately controlled and adjusted according to the specific parameter settings in the first optimal solution set, and instructions are sent to the valve through the control system to adjust its opening, and the operation is performed according to the clear instructions of the first optimal solution set to achieve accurate control of parameters such as fluid flow and pressure in the first decomposition network. For the valves in the second decomposition network, a step control method is adopted, and the opening is gradually adjusted from the initial opening to the final target opening, and the adjustment is performed in several steps. The opening is first increased by a small amplitude, and after a period of stability, it is increased by a small amplitude again, and finally the target opening is reached. In the adjustment process of each step, it is necessary to determine the appropriate time interval and adjustment amplitude according to the specific parameters and system feedback. This implementation method achieves precise and targeted regulation of two different decomposition networks by directly controlling the valves in the first decomposition network according to the first optimal solution set, and performing step-by-step control on the valves in the second decomposition network according to the second optimal solution set. Therefore, this implementation method achieves the technical effects of optimizing system operation, improving efficiency and ensuring stability.

[0037] In a possible implementation, the M nodes corresponding to the first decomposition network are controlled and optimized, and the N nodes corresponding to the second decomposition network are controlled and optimized to generate a first initial solution set and a second initial solution set, wherein the first initial solution set includes M initial control parameter solutions, and the second initial solution set includes N initial control parameter solutions. Step S610 further includes step S611, analyzing the node transitivity of the first decomposition network to obtain P transmission paths. Specifically, for the first decomposition network, analyzing its node transitivity involves the connection relationship and information transmission mode between each node in the network, checking and tracking the nodes in the network one by one, analyzing the different connection paths from each node to other nodes, continuously recording and counting possible transmission paths, determining the transmission paths that each node can follow along different connection combinations after some calculation and tracking, summarizing the number of transmission paths of all nodes, and finally obtaining P transmission paths, processing and analyzing a large amount of data and connection relationships to accurately obtain all possible transmission paths, providing a basis for subsequent further research and application, and ensuring the accuracy and efficiency of the analysis.

[0038] Step S612: Combine the M initial control parameter solutions according to the P transmission paths, output P transmission combinations, and feed back the initial control parameter solution of the previous node in the P transmission combinations to the initial control parameter solution of the next node for transmission optimization. Specifically, check and track the nodes in the first decomposition network one by one, determine the connection relationship and information transmission mode between the nodes, obtain the number of transmission paths starting from each node through calculation and tracking, combine the M initial control parameter solutions according to the obtained P transmission paths, each transmission path corresponds to a transmission combination, that is, transfer the initial control parameter solution of the previous node to the next node, feed back the initial control parameter solution of the previous node to the initial control parameter solution of the next node, perform transmission optimization, continuously adjust and optimize the initial control parameter solution, so that the transmission process is more accurate and efficient. In the transmission optimization process, the initial control parameter solution of the previous node can be adjusted and optimized according to the specific optimization objectives and constraints to improve the accuracy and efficiency of transmission, realize the analysis of the node transmission of the first decomposition network, and realize the optimization of the transmission process by combining and optimizing the initial control parameter solution.

[0039] In a possible implementation, step S610 further includes step S613, analyzing the node transitivity of the second decomposition network to obtain Q transmission paths. Specifically, the node transitivity of the second decomposition network is analyzed, starting from the starting node of the network, the connection with other nodes and possible information transmission paths are determined, considering its direct connection with other nodes and the indirect connection that may be formed through the intermediate nodes, and the transmission path starting from the node is determined. Similar analysis and calculation are performed on each node, and all transmission paths are summarized and counted. After adding the paths found by each node, the total number of transmission paths Q is obtained. The specific calculation process will vary according to the specific structure and connection conditions of the second decomposition network, and the information transmission method and the number of paths between nodes are determined. Finally, Q transmission paths are obtained, which provide a basis for subsequent further processing and application.

[0040] Step S614, the N initial control parameter solutions are combined according to the Q transfer paths, Q transfer combinations are output, and the initial control parameter solution of the previous node in the Q transfer combinations is fed back to the initial control parameter solution of the next node for transfer optimization. Specifically, Q transfer paths are clearly obtained, and there are N initial control parameter solutions, for example, there are five transfer paths and three initial control parameter solutions. For the first transfer path, one of the three initial control parameter solutions is selected as the parameter solution of the starting point, which is assumed to be a larger value. Then another parameter solution is selected as the parameter solution of the intermediate node, which may be a medium-sized value. Then a parameter solution is selected as the terminal node, which is a smaller value. This constitutes the first transfer combination. All five transfer paths are combined in this way to obtain five transfer combinations. When the larger numerical parameter solution of the starting node is transferred to the intermediate node, the parameter solution of the intermediate node is increased according to the rule to become a slightly larger value. Then this slightly larger value is transferred to the terminal node, and the parameter solution of the terminal node is changed again according to the rule. Other transfer combinations are also transferred and optimized in a similar manner. Through step-by-step processing, the optimization process of each transfer combination is realized.

[0041] In a possible implementation, the first stability and the second stability are calculated to obtain the fitting stability. If the fitting stability does not meet the expected stability threshold, the expected stability threshold is used as the target for optimization, and the first optimization solution set and the second optimization solution set are output. Step S630 further includes step S631, obtaining the first stability and the second stability corresponding to the first initial solution set and the second initial solution set, respectively. Specifically, the first initial solution set and the second initial solution set are clarified, the stability of the elements in the solution set is analyzed one by one, starting from its physical characteristics and chemical properties, considering its mutual relationship with other elements in this solution set, such as the interaction force and mutual influence between them, to obtain the value of its stability, and the stability values ​​of all elements are comprehensively weighted averaged to obtain the first stability value of the first initial solution set as a whole, for the second initial solution set, the characteristics and mutual relationship of each element therein are analyzed, the stability score of each element is calculated, and then a comprehensive operation is performed to obtain the value of the second stability, and finally the first stability and the second stability corresponding to the first initial solution set and the second initial solution set are determined, respectively, which provides an important basis for subsequent further processing and decision-making.

[0042] Step S632, compare the first stability and the second stability with the expected stability threshold, and obtain the stability difference comparison result. Specifically, the first stability value corresponding to the first initial solution set and the second stability value corresponding to the second initial solution set are obtained, and an expected stability threshold is set, and the comparison is performed, and the difference with the expected stability threshold is calculated to obtain the stability difference between the first stability and the expected stability threshold. Similarly, the second stability difference is calculated, and the difference is analyzed and compared to obtain the stability difference comparison result, which can clearly show the degree of difference between the first initial solution set and the second initial solution set and the expected stability threshold. The first stability and the second stability are compared with the expected stability threshold, and an intuitive stability difference comparison result is obtained, which provides an important basis for taking corresponding adjustment strategies or further decisions based on the results.

[0043] Step S633, priority identification is performed according to the stability difference comparison result, and priority segmentation optimization is performed using the priority identification result until the expected stability threshold is met, and the first optimization solution set and the second optimization solution set are output. Specifically, the stability difference comparison result is obtained, and a priority mark is performed. The larger the stability difference, the higher the priority. Assuming that the difference between the first stability and the expected stability threshold is 15, and the difference between the second stability and the expected stability threshold is 8, the first initial solution set is marked as high priority and the second initial solution set is marked as low priority. Priority segmented optimization is performed, starting with the first initial solution set with high priority, and gradually adjusting and optimizing the elements or parameters in the first initial solution set. Each time, the value of a key parameter is increased or decreased according to the set step size adjustment, and the first round of optimization is performed to obtain a new stability value of the first initial solution set. The difference is reduced, and the second round of optimization is continued. The above process is repeated until the expected stability threshold is met, the optimization stops, and the first optimization solution set is obtained. A similar optimization operation is performed on the second initial solution set with low priority, and other step sizes are set to obtain the second optimization solution set. Through the segmented optimization process performed in sequence according to the priority mark, the first optimization solution set and the second optimization solution set are finally output, the expected stability threshold is reached, and the goal of the technical solution is achieved.

[0044] In one possible implementation, Figure 2As shown, the valves in the first decomposition network are directly controlled according to the first optimal solution set, and the valves in the second decomposition network are step-controlled according to the second optimal solution set, and step S640 further includes step S641, obtaining N real-time control parameter sets corresponding to the N valves in the second decomposition network. Specifically, the N real-time control parameter sets corresponding to the N valves in the second decomposition network are obtained, and the relevant parameters thereof, such as flow control parameters, pressure control parameters, and the control parameter set composed of the parameters are read in real time through the sensors or monitoring devices connected to the real-time control parameter sets, to obtain the real-time control parameter sets.

[0045] Step S642, identifying the N optimal solutions of the control parameters corresponding to the second optimal solution set. Specifically, the second optimal solution set is obtained, which is a set composed of multiple data points, and the parts corresponding to the N control parameters are identified one by one. According to the characteristics and identifiers of the pre-set control parameters, including the specific format and range of the data, the data parts matching these parameters are searched one by one in the second optimal solution set, and the optimal solution of the parameter is determined by analyzing its characteristics and comparing it with the control parameters. The set is deeply understood, and a data segment that meets the characteristics of another parameter is found, and it is marked as the optimal solution of the parameter, and it is repeated until the optimal solutions of all N control parameters are found. For example, N is 3, that is, the three control parameters are speed, angle, and power. In the second optimal solution set, after careful analysis and judgment, we identify a data item with a data value of 30 meters per second. According to its association with the speed parameter, it is determined that this is the optimal solution for speed. Then, a data part representing 45 degrees is found, which is determined to be the optimal solution for angle. Similarly, the optimal solution for power is obtained.

[0046] Step S643, according to the N real-time control parameter sets and the N control parameter optimal solutions, obtain the control parameter step length. Specifically, it is clear that there are N real-time control parameter sets and N control parameter optimal solutions. For each real-time control parameter set and the corresponding control parameter optimal solution, analyze and process them one by one, calculate the difference between the real-time value of the parameter contained in a real-time control parameter set and the optimal solution value of the parameter in the corresponding control parameter optimal solution, and obtain the control parameter step length. For other control parameters, calculate their respective control parameter step lengths in a similar manner, perform such calculation operations on all N control parameters, and thus obtain the complete N control parameter step lengths. The control parameter step length will provide an important basis and guidance for subsequent control adjustment or optimization, helping us to more accurately and effectively realize the control and optimization of the system or equipment, so that the system or equipment can be adjusted in the desired direction and degree to achieve a better operating state or performance.

[0047] Step S644, taking the control parameter step length as the adaptation target, performing fitness control optimization on the step length, and obtaining the step length obtained by optimization. Specifically, determine the control parameter step length, set the control parameter step length as the adaptation target, set an initial step length, calculate the difference between the current step length and the control parameter step length, and construct a fitness function according to the difference value. The function is used to measure the fitness of the current step length. The specific form of the fitness function can be determined according to the actual situation and needs. By continuously adjusting the value of the step length, such as adjusting a small increment or decrement each time, recalculating the value of the fitness function, if the new fitness function value is better, that is, closer to the control parameter step length, the adjusted step length is retained; otherwise, continue to try other adjustments. After multiple iterative adjustments, the final step length is obtained. By continuously repeating the above calculation and adjustment steps until the optimal step length is found, its fitness reaches the best state, which is most consistent with the control parameter step length, ensuring efficient and accurate acquisition of the optimized step length.

[0048] Step S645: perform step control on the valves in the second decomposition network according to the step length obtained by optimization. Specifically, determine the valve parameters to be controlled, such as the valve opening, flow rate, etc., obtain the optimized step length through the previous optimization process, set the valve to the initial state, such as fully closed or opened to a certain extent, and gradually adjust the control parameters of the valve according to the step length obtained by optimization. For example, if the step length is to increase the opening by a certain degree, the valve opening is gradually increased according to the step length. In the process of adjusting the valve, monitor the state or related parameters of the system in real time and compare it with the desired target. According to the monitoring results, it can be determined whether the valve needs to be further adjusted or the current state is maintained. According to the needs, repeat the above steps and continue to adjust the valve according to the step length until a satisfactory control effect is achieved or a specific condition is met. Once the desired control state is achieved, keep the valve position or parameter stable to maintain the stable operation of the system, continuously monitor the performance of the system, and perform necessary optimization and adjustment according to the actual situation to ensure that the control effect of the valve always meets the requirements. By performing step control on the valve according to the step length obtained by optimization, precise control of the system can be achieved, and control efficiency and stability can be improved.

[0049] The embodiment of the present application obtains valve distribution nodes of precision equipment, uses the valve distribution nodes as flow characteristic monitoring nodes, generates a valve flow monitoring network, each flow characteristic monitoring node is used to store a flow characteristic monitoring data set of the corresponding valve, performs pressure relief hazard correlation analysis on each flow characteristic monitoring data set according to the valve flow monitoring network, outputs an internal hazard correlation factor corresponding to each node, performs pressure relief hazard assessment according to the internal hazard correlation factor corresponding to each node, outputs a pressure relief hazard index based on the valve flow monitoring network, decomposes the valve flow monitoring network according to the pressure relief hazard index, obtains a first decomposition network and a second decomposition network, directly controls the valves in the first decomposition network, and performs step control on the valves in the second decomposition network, thereby achieving refined management and effective control of the valve system of precision equipment and reducing the technical effect of pressure relief hazard.

[0050] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A valve flow characteristic control method for precision equipment, characterized in that: The method comprises: Obtain valve distribution nodes of precision equipment; According to the valve distribution nodes as flow characteristic monitoring nodes, a valve flow monitoring network is generated, and each flow characteristic monitoring node is used to store a flow characteristic monitoring data set of a corresponding valve; Performing pressure relief hazard correlation analysis on each flow characteristic monitoring data set according to the valve flow monitoring network, and outputting the internal hazard correlation factor corresponding to each node; Performing a pressure relief hazard assessment according to the internal hazard-related factors corresponding to each node, and outputting a pressure relief hazard index based on the valve flow monitoring network; Decomposing the valve flow monitoring network according to the pressure relief danger index to obtain a first decomposition network and a second decomposition network, wherein the first decomposition network is composed of nodes whose pressure relief danger index is less than a preset danger threshold, and the second decomposition network is composed of nodes whose pressure relief danger index is greater than or equal to the preset danger threshold; Directly control the valves in the first decomposition network and perform step control on the valves in the second decomposition network; Among them, the pressure relief hazard assessment is carried out according to the internal hazard related factors corresponding to each node, including: Acquire internal risk-related factors, wherein the internal risk-related factors are acquired from the flow monitoring data inside the valve, including the flow coefficient, valve opening, pressure difference between both sides, and flow abnormality index; Performing a pressure relief risk assessment according to the internal risk-related factors, obtaining an internal-pressure relief risk index, and outputting the internal-pressure relief risk index as a pressure relief risk index; The valves in the first decomposition network are directly controlled, and the valves in the second decomposition network are step-controlled, and the method includes: Performing control optimization on M nodes corresponding to the first decomposition network and performing control optimization on N nodes corresponding to the second decomposition network to generate a first initial solution set and a second initial solution set, wherein the first initial solution set includes M initial control parameter solutions and the second initial solution set includes N initial control parameter solutions; Performing a pressure relief stability assessment according to the first initial solution set and the second initial solution set to obtain a first stability and a second stability; Calculating the first stability and the second stability to obtain fitting stability, and if the fitting stability does not meet the expected stability threshold, performing optimization with the expected stability threshold as a target, and outputting a first optimization solution set and a second optimization solution set; Directly control the valves in the first decomposition network according to the first optimal solution set, and step-control the valves in the second decomposition network according to the second optimal solution set; The method of performing step control on the valves in the second decomposition network according to the second optimal solution set includes: Obtaining N real-time control parameter sets corresponding to the N valves in the second decomposition network; Identifying N optimal solutions of control parameters corresponding to the second optimal solution set; According to the N real-time control parameter sets and the N control parameter optimal solutions, obtaining a control parameter step size; Taking the control parameter step length as the adaptation target, performing fitness control optimization on the step length, and obtaining the optimized step length; The valves in the second decomposition network are step-controlled according to the step length obtained by optimization.

2. The valve flow characteristic control method for precision equipment according to claim 1, characterized in that: The pressure relief hazard assessment is performed according to the internal hazard related factors corresponding to each node, and the method further includes: Analyze the valve flow monitoring network to obtain external risk-related factors; The external hazard related factors are obtained from the valve distribution characteristics, including the number of connected nodes, the importance of the location, and the historical failure rate of the nodes; Perform pressure relief hazard assessment based on the external hazard related factors to obtain an external-pressure relief hazard index; A weighted calculation is performed based on the external-pressure relief hazard index and the internal-pressure relief hazard index to update the pressure relief hazard index.

3. The valve flow characteristic control method for precision equipment according to claim 1, characterized in that: Optimizing with the expected stability threshold as a target, outputting a first optimal solution set and a second optimal solution set, the method comprising: Obtain a first stability and a second stability corresponding to the first initial solution set and the second initial solution set respectively; Comparing the first stability and the second stability with the expected stability threshold, to obtain a stability difference comparison result; Priority identification is performed according to the stability difference comparison result, and priority segmentation optimization is performed using the priority identification result until the expected stability threshold is met, and a first optimization solution set and a second optimization solution set are output.

4. The valve flow characteristic control method for precision equipment according to claim 1, characterized in that: Obtaining M initial control parameter solutions, the method further includes: Analyze the node transitivity of the first decomposition network to obtain P transit paths; The M initial control parameter solutions are combined according to the P transfer paths, P transfer combinations are output, and the initial control parameter solution of the previous node in the P transfer combinations is fed back to the initial control parameter solution of the next node for transfer optimization.

5. The valve flow characteristic control method for precision equipment according to claim 1, characterized in that: Obtaining N initial control parameter solutions, the method further includes: Analyze the node transitivity of the second decomposition network to obtain Q transit paths; The N initial control parameter solutions are combined according to the Q transfer paths, Q transfer combinations are output, and the initial control parameter solution of the previous node in the Q transfer combinations is fed back to the initial control parameter solution of the next node for transfer optimization.

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