Petri net-based control instruction recognition and multi-system collaborative control method and device, and storage medium

Abstract

The application discloses a control instruction recognition and multi-system collaborative control method and device based on a Petri net and a storage medium. The method comprises the following steps: standardizing an engine test time card, adding a structured label to generate a parseable text; extracting the label content to construct a discrete event sequence and perform visual display; constructing a Petri net model based on the discrete event sequence, defining a library, a transition and a token; designing a multi-system collaborative control strategy comprising a PID controller and a consistency controller; generating a control instruction sequence according to a current identification state of the Petri net, combining the collaborative control strategy to drive the linkage of a gas supply, air intake and exhaust subsystem model, and automatically executing a test process. The application realizes automatic recognition of a test process and dynamic generation of a control instruction, improves the response speed and control quality through multi-system collaborative control, and significantly improves the automation level and execution efficiency of an aero-engine test.

Description

Technical Field

[0001] This invention belongs to the field of aero-engine test control technology, specifically relating to a method, device and storage medium for control command recognition and multi-system collaborative control based on Petri nets. Background Technology

[0002] Flight environment simulation systems typically include multiple subsystems that need to work together, such as air supply, intake, and exhaust subsystems. The control precision and coordination of these subsystems directly affect the accuracy and reliability of the experimental results. Petri nets, as a mathematical representation of discrete parallel systems, consist of a graphical structure of places, transitions, directed arcs, and tokens, and are suitable for describing asynchronous concurrent computer system models. A transition is allowed when each input place has a token; the transition will occur, consuming input place tokens and generating new tokens for the output place.

[0003] Currently, engine testing relies heavily on manual experience. Test personnel must manually analyze test timing programs or cards, determining test steps and matching corresponding control timing commands based on experience. The specific process involves issuing test commands based on the test timing card, establishing test point states according to the manual commands and referring to the paper timing card, and then conducting the test. The entire process depends entirely on manual operation and input. The test process requires on-site adjustment of various valves, actuators, and equipment, as well as coordination of interactions and preparations between different subsystems, posing certain safety risks and potential for misoperation, resulting in low efficiency.

[0004] Therefore, there is an urgent need for an intelligent command recognition and multi-system collaborative control method that can automatically parse the test process corresponding to the test time sequence card through machine recognition methods, transform it into a discrete event sequence, and perform automatic planning and matching based on Petri nets to quickly generate accurate collaborative control command sequences, drive the synchronous operation of each subsystem model, and improve the operating efficiency and safety of the control system. Summary of the Invention

[0005] The purpose of this invention is to address the problems of low efficiency in existing aero-engine environmental simulation testing tasks due to reliance on manual command matching, coupling effects on control quality caused by independent control of multiple systems, and lack of multi-system coordinated environmental parameter matching and collaborative optimization capabilities. This invention provides a Petri net-based control command identification and multi-system collaborative control method, device, and storage medium. This aims to improve the automation level and execution efficiency of aero-engine testing, reduce operational risks and the probability of errors caused by manual intervention, and enhance the response speed, safety, and reliability of multi-system collaborative control.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a Petri net-based control command recognition and multi-system cooperative control method for aero-engine testing, comprising the following steps: S1: Standardize the engine test timing card, add structured tags, and generate a machine-readable text file; S2: Parse the parsable text file, extract the tag content to construct a discrete event sequence, and visualize it; S3: Construct a Petri net model based on the discrete event sequence, including defining places, transitions and tokens, where places represent the state of test points, transitions represent the switching and execution logic of test tasks, and tokens represent the demand and availability of test resources; S4: Define transition triggering rules, which include time-triggered and condition-triggered rules, used to describe the transition activation conditions and the token update method after activation; S5: Design a multi-system collaborative control strategy, including PID controllers and consistency controllers for the air supply, intake, and exhaust subsystems; S6: Generate a sequence of control commands based on the current identifier status of the Petri net, and in conjunction with the aforementioned collaborative control strategy, drive the air supply subsystem, intake subsystem, and exhaust subsystem models to work together to automatically execute the test process.

[0008] Furthermore, the structured tags in step S1 include: Labels used to identify test points and their core parameters; labels used to identify specific operating instructions; labels used to identify test task descriptions.

[0009] Furthermore, the transition triggering rules in step S4 include: time triggering rule: when the input library is in an active state and the duration reaches a preset threshold, a corresponding transition is triggered; condition triggering rule: when the number of tokens in the input library meets a preset guard condition, a corresponding transition is triggered; after the transition is triggered, the tokens in the input library are consumed, and new tokens are generated in the output library.

[0010] Furthermore, the guard conditions are used to determine the sufficiency of resource tokens, including determining the quantity, status, or logical combination of multiple types of resource tokens.

[0011] Furthermore, the multi-system coordinated control strategy in step S5 is as follows: a global objective function containing the state variables of the air supply, intake, and exhaust subsystems is constructed, and a consistency controller is designed based on the Laplace matrix representing the coupling relationship between the subsystems to coordinate the control output of each subsystem, so that each subsystem satisfies the constraints of the global objective function during the dynamic adjustment process.

[0012] Furthermore, the expression for the consensus controller is:

[0013] in, Let i be the state variable of the i-th subsystem. For system status rate of change, This represents the state variable of the j-th subsystem that is coupled with the i-th subsystem. System status rate of change, Laplace matrix OK Column elements, It is a gain constant greater than 0.

[0014] Furthermore, in step S6: the gas supply subsystem valves are used to control the gas supply pressure; the intake subsystem valves are used to control the intake pressure and temperature; the exhaust subsystem valves are used to control the exhaust pressure; and each valve is adjusted in conjunction with the instruction sequence and collaborative control strategy generated by the Petri net.

[0015] In a second aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the above-described method.

[0016] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the above-described method.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: This invention standardizes experimental timing cards, adds structured tags, extracts discrete event sequences using text extraction technology, and uses Petri nets for planning and matching to quickly generate experimental control command sequences. Combined with a multi-system collaborative control strategy, this drives the operation of an environmental simulation system. Compared to traditional manual scheduling methods, the "structured analysis + Petri net-driven modeling" approach employed in this invention significantly reduces control command response latency and improves the reproducibility of the experimental process. Simulation verification shows that the method described in this invention can shorten experimental configuration time by approximately 85% and reduce the number of control transitions by 70%, demonstrating good engineering adaptability. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a Petri net-based control command recognition and multi-system cooperative control method provided in an embodiment of the present invention; Figure 2 This is a typical test point effect diagram corresponding to the task recognition efficiency in the embodiments of the present invention; Figures 3-a to 3-e This is an example diagram of engine test task modeling and control timing instructions based on the Petri net method in an embodiment of the present invention; Figure 4 This is a schematic diagram of valve coordinated control in multiple systems according to an embodiment of the present invention. Detailed Implementation

[0020] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0021] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] This invention provides a Petri net-based control command recognition and multi-system cooperative control method to address the problems of low efficiency in current environmental simulation experiments due to reliance on manual command matching, coupling effects on control quality caused by independent control of multiple systems, and lack of multi-coordinated environmental parameter matching and cooperative optimization capabilities. Based on the discrete event sequence obtained by this method, a Petri net model is constructed. The Petri net is then used for planning and matching to rapidly generate experimental control command sequences, thereby driving the environmental simulation system model and improving experimental efficiency and safety. The overall process of the method described in this invention is as follows: Figure 1 As shown, the specific steps include: S1: Standardize the engine test timing card, add structured tags, and generate a machine-readable text file.

[0023] Specifically, the engine test timing card content is standardized based on the engine's current state (e.g., 70% idle), including formatting and error correction. Then, test points (representing the engine's state under different simulation environments) are organized according to a preset logical structure, and operation and test task sequences are set. Structured tags are added to test point identifiers and parameters, operation instructions, and test task descriptions, generating a machine-readable text file.

[0024] The structured tags specifically include: tags for identifying test points and their core parameters, tags for identifying specific operational instructions, and tags for identifying test task descriptions. By adding these structured tags, the original unstructured electronic test cards are transformed into a computer-understandable and parseable format, laying the foundation for subsequent automated processing. This step transforms test procedures that rely on human experience for interpretation into standardized, machine-readable data, eliminating the ambiguity and uncertainty caused by manual interpretation.

[0025] S2: Parse the parsable text file, extract the tag content to construct a discrete event sequence, and visualize it.

[0026] Specifically, the parsable text file generated in step S1 is read, and the experimental point parameters, operation instructions, and experimental task content are extracted based on the structured tags to construct a time-series discrete event sequence. This discrete event sequence is organized in chronological order, clearly describing the order of occurrence and logical relationships of each event during the experiment.

[0027] Meanwhile, a graphical user interface provides a visual display, facilitating operator understanding and monitoring of the experimental process, and offering file selection and content confirmation functions. Operators can intuitively view the upcoming experimental steps, parameter settings for each experimental point, and task execution sequence through the visual interface, allowing for manual verification before the experiment begins to ensure the correctness of the process. This step transforms machine-readable data into human-understandable graphical information, enabling a human-computer interaction confirmation process that balances automation efficiency with the safety of human supervision.

[0028] S3: Construct a Petri net model based on the discrete event sequence, including defining places, transitions and tokens, where places represent the state of test points, transitions represent the switching and execution logic of test tasks, and tokens represent the demand and availability of test resources.

[0029] Specifically, Petri nets are a mathematical representation of discrete parallel systems, consisting of three parts: places, transitions, and tokens, used to represent state evolution. In this embodiment of the invention, each test point is mapped to a place, indicating that the test point is in an active or completed state; the switching operations between test points and the task execution within a test point are mapped to transitions; the demand and availability of test resources are mapped to tokens, which exist in resource places, and resource flow is triggered by transitions.

[0030] The system identifier update caused by the changes between test points satisfies the following dynamic relationship:

[0031] in, As the initial identifier, It is an incidence matrix. Transition sequence The excitation frequency vector.

[0032] By constructing a Petri net model, the abstract experimental process is transformed into a dynamic system model with rigorous mathematical definitions. The introduction of places, transitions, and tokens allows for the precise description and monitoring of system state changes. This step provides a formal mathematical description of the experimental process, giving subsequent control command generation a rigorous theoretical foundation.

[0033] S4: Define transition triggering rules, which include time-triggered and condition-triggered rules, used to describe the transition activation conditions and the token update method after activation.

[0034] Specifically, defining transition triggering rules includes: Time-triggered rule: When the input library is active and the duration reaches a preset threshold, a corresponding strain transition is triggered. This rule is suitable for experimental scenarios where a certain period of time is required before proceeding to the next step, such as temperature stabilization time or pressure holding time.

[0035] Conditional Triggering Rule: When the number of tokens in the input warehouse meets the preset guard conditions, a corresponding transition is triggered. The guard conditions are used to determine the sufficiency of resource tokens, including the determination of the quantity, status, or logical combination of multiple types of resource tokens. This rule applies to operations that require specific resource conditions to be met, such as multiple valves simultaneously in position or multiple measurement values ​​meeting requirements.

[0036] Generally, this involves triggering a condition-triggered rule followed by a time-triggered rule.

[0037] Furthermore, during transition activation and triggering, tokens in the input place are consumed, and new tokens are generated in the output place. By defining explicit triggering rules, the Petri net model possesses the ability to evolve dynamically. Time-based triggering ensures the strict execution of temporal constraints, while conditional triggering ensures the satisfaction of resource conditions; both together guarantee the correct execution of the experimental process. This step formalizes the time and resource constraints in the experimental process into a mathematical model, enabling the Petri net to accurately reflect various constraints in the actual experimental process.

[0038] S5: Design a multi-system collaborative control strategy, including PID controllers and consistency controllers for the air supply, intake, and exhaust subsystems.

[0039] Specifically, the PID controller employs discrete PID control for the air supply subsystem, intake subsystem, and exhaust subsystem, with the following expressions:

[0040] Among them, K p For proportional gain, K i For integral gain, K d Let be the differential gain, k be the current sampling number, e(i) be the sampling error at time i, e(k) be the sampling error at time k, e(k-1) be the sampling error at time k-1, and u(k) represent the output at time k. The PID controller, as the basic control layer, ensures that each subsystem can track its own setpoint, achieving basic closed-loop control functionality.

[0041] To improve the speed and efficiency of multi-system control and reduce valve jitter in each subsystem, a collaborative control method is introduced. Specifically, the multi-system collaborative control strategy described in this embodiment of the invention involves: constructing a global objective function that includes the state variables of the air supply, intake, and exhaust subsystems; and designing a consistency controller based on the Laplace matrix characterizing the coupling relationship between the subsystems to coordinate the control outputs of each subsystem, ensuring that each subsystem satisfies the constraints of the global objective function during dynamic adjustment.

[0042] Specifically, such as Figure 4 As shown, the intake subsystem's I-3 valve (main intake pressure control valve) and I-4 valve (auxiliary intake temperature regulating valve) work in concert with the exhaust subsystem's I-5 valve (main exhaust pressure control valve) to achieve coordinated control of three core states (intake pressure, intake temperature, and exhaust pressure). The local objective functions for each valve are defined as follows:

[0043] in, The system state vectors correspond to the intake chamber pressure, intake temperature, and exhaust pressure, respectively. For valves The local objective function, These are the intake chamber pressure setting, intake chamber temperature setting, and exhaust pressure setting, respectively.

[0044] Therefore, the global objective function is as follows:

[0045] A distributed controller can be designed based on the global objective function, such that all system states xi approach the optimal value corresponding to the global objective function.

[0046] according to Figure 4 The coupling relationship between the three valves shown can be used to obtain the system's Laplace matrix:

[0047] The consensus controller can be designed using the Laplace matrix and the objective function as follows:

[0048] in, For constant gain, Laplace matrix OK Column elements, System status rate of change, This represents the state variable of the j-th subsystem that is coupled with the i-th subsystem. System status The rate of change. The above consensus algorithm, combined with each valve mode, can ensure that the intake pressure, intake temperature, and exhaust pressure all converge to the target value. The controller can be represented as:

[0049] in, Outputs for different valve controllers This is a consistent term. The effect of this cooperative control strategy is that, through the coupling relationship described by the Laplace matrix, the three valves can sense each other's state changes during the adjustment process, thus coordinating their actions rather than adjusting independently. This not only speeds up the system's response but also reduces the repeated valve jitter caused by independent adjustment, improving control quality and system stability. Furthermore, from... Figure 4 As shown, valves I-1 and I-2 of the gas supply subsystem are controlled independently and are not controlled in coordination with other valves.

[0050] S6: Generate a sequence of control commands based on the current identifier status of the Petri net, and in conjunction with the aforementioned collaborative control strategy, drive the air supply subsystem, intake subsystem, and exhaust subsystem models to work together to automatically execute the test process.

[0051] Specifically, the Petri net identifier status is monitored in real time. When a transition triggers a change in the place token, a corresponding control command sequence is generated based on the activated test point place and the triggered transition. This control command sequence, combined with a multi-system collaborative control strategy, drives the coordinated execution of the gas supply subsystem, intake subsystem, and exhaust subsystem models. Specifically, valves in the gas supply subsystem control the gas supply pressure; valves in the intake subsystem control the intake pressure and temperature; and valves in the exhaust subsystem control the exhaust pressure. Each valve adjusts in conjunction with the command sequence generated by the Petri net and the collaborative control strategy. When the token flow in the Petri net model indicates the need to move to the next test point, the system automatically generates the corresponding control command, invokes the collaborative control strategy designed in step S5, and adjusts the valve openings of each subsystem, enabling the entire test process to proceed automatically. This step achieves a closed-loop mapping from the Petri net state to actual physical control. By monitoring the token distribution in real time, the system can accurately determine the current test stage and automatically generate the next control command. Combined with the collaborative control strategy, intelligent linkage of multiple valves is achieved, avoiding delays and errors caused by manual intervention, and significantly improving the automation level and execution efficiency of the test.

[0052] Task identification was performed using the above method, and the corresponding typical experimental results for task identification efficiency are as follows: Figure 2 As shown, several typical tests, including ground tests, high-altitude calibration, and thrust transient tests, are identified. Figure 2 As can be seen, the total time is exactly equal to the sum of the hold time and the transition time. Therefore, the recognition accuracy of the task identification in the typical test using the above method is 100%.

[0053] In a specific embodiment, the modeling of engine test tasks based on the Petri net method is described in detail below with reference to Figure 3. Figure 3 shows an example Petri net model for three different test points. Each test point corresponds to a location, denoted as P1, P2, and P3, representing the activation or completion status of test point 1, test point 2, and test point 3, respectively. Figure 3-a In the initial state, once the parameters of the gas source, electricity, and fuel meet the triggering conditions for transition 1, test point 1 (windmill start-up test) is executed. At this time, the token is located at P1, indicating that test point 1 is in an active state, such as... Figure 3-bAs shown, the test procedure for test point 1 is executed as follows: windmill runs for 3 minutes - initiation - MC for 3 minutes - 85% for 3 minutes. When the task of test point 1 is completed and the triggering conditions of transition 2 are met (e.g., the time threshold is reached or the guard condition is met), transition 2 is activated. After transition 2 is activated, the token in P1 is consumed, and a new token is generated in P2, indicating that the system switches from test point 1 to test point 2. At the same time, transition 3 is also activated. Figure 3-c The execution process of test point 2 (high-altitude calibration test) is demonstrated. When the token arrives at P2, the task corresponding to test point 2 begins execution. The specific test flow for test point 2 includes: MC 3 minutes - 85% 3 minutes - 90% 3 minutes - 95% 3 minutes - ZJ middle 3 minutes - ZD maximum 3 minutes - 85% 3 minutes. Depending on the task requirements, multiple transitions may be triggered concurrently or sequentially, reflecting concurrent operations and resource allocation during the test. When the task at test point 2 is completed and the triggering conditions for transition 4 are met, transition 4 is activated. After transition 4 is activated, the token in P2 is consumed, and a new token is generated in P3, indicating that the system switches from test point 2 to test point 3. Simultaneously, transition 5 is also triggered. Figure 3-d The execution process of Test Point 3 (conducting an assisted start test) is demonstrated. When the token arrives at P3, the task corresponding to Test Point 3 begins execution. The specific test procedure for Test Point 3 includes: MC 3 minutes - TC 3 minutes - Engine assisted start - 85% 3 minutes. The test ends when the task of Test Point 3 is completed and the triggering conditions of Transition 6 are met. Figure 3-e As shown.

[0054] from Figures 3-a to 3-e During the evolution of the test, the dynamic distribution of tokens reflects the progress of the entire test process. As shown in the diagram, when test point 1 satisfies transition 7, test point 2 satisfies transition 8, and test point 3 satisfies transition 9, the test enters a safe test point. The entry condition for a safe test point is the determination that a surge has occurred. After eliminating the fault, if transition 10 is satisfied, test point 1 is re-executed; if transition 11 is satisfied, test point 2 is re-executed; if transition 12 is satisfied, test point 3 is re-executed; if the safe test point satisfies transition 13, the test ends.

[0055] Using the Petri net-based modeling method described above, the complex engine testing process is transformed into a dynamic system with rigorous mathematical definitions. Each place, transition, and token in the model has a clear physical meaning: a place corresponds to the state of the test point, a transition corresponds to the logical judgments and condition checks for task execution, and a token corresponds to the availability and allocation of resources. This modeling approach not only makes the testing process more intuitive and understandable but also provides a formal mathematical foundation for the subsequent generation of automatic control commands.

[0056] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The memory stores the computer program and data, and can be a storage medium such as random access memory (RAM), read-only memory (ROM), flash memory, hard disk, or solid-state drive. The processor executes the computer program stored in the memory. When the processor executes the computer program, it implements the Petri net-based control instruction recognition and multi-system cooperative control method described in steps S1 to S6 above. Specifically, the processor executes program instructions to complete the structured parsing of the test time sequence card, construction of discrete event sequences, Petri net model state updates, transition trigger rule judgment, cooperative control strategy calculation, and generation and output of control instruction sequences, thereby driving the air supply, intake, and exhaust subsystem models to perform the test process in a coordinated manner. This electronic device can be integrated into an aero-engine test control system as a host control computer or an embedded control unit.

[0057] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon. The computer-readable storage medium can be any medium that contains, stores, transmits, propagates, or transmits a program, such as, but not limited to: a disk, optical disk, magneto-optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or solid-state drive. When the computer program is executed by a processor, it can also realize all the technical contents of the methods described in steps S1 to S6 above, completing the automatic identification and multi-system collaborative control of aero-engine test tasks. By deploying this computer program on the computing device of the test control system, the industrial application of the method of the present invention can be realized.

[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A Petri net-based control command recognition and multi-system cooperative control method for aero-engine testing, characterized in that, Includes the following steps: S1: Standardize the engine test timing card, add structured tags, and generate a machine-readable text file; S2: Parse the parsable text file, extract the tag content to construct a discrete event sequence, and visualize it; S3: Construct a Petri net model based on the discrete event sequence, including defining places, transitions and tokens, where places represent the state of test points, transitions represent the switching and execution logic of test tasks, and tokens represent the demand and availability of test resources; S4: Define transition triggering rules, which include time-triggered and condition-triggered rules, used to describe the transition activation conditions and the token update method after activation; S5: Design a multi-system collaborative control strategy, including PID controllers and consistency controllers for the air supply, intake, and exhaust subsystems; S6: Generate a sequence of control commands based on the current identifier status of the Petri net, and in conjunction with the aforementioned collaborative control strategy, drive the air supply subsystem, intake subsystem, and exhaust subsystem models to work together to automatically execute the test process.

2. The method according to claim 1, characterized in that, The structured tags in step S1 include: Labels used to identify test points and their core parameters; labels used to identify specific operating instructions; labels used to identify test task descriptions.

3. The method according to claim 1, characterized in that, The transition triggering rules in step S4 include: Time-triggered rule: When the input library is in an active state and the duration reaches a preset threshold, a corresponding transition is triggered; Conditional triggering rule: When the number of tokens in the input warehouse meets the preset guard conditions, a corresponding transition is triggered; Once a transition is triggered, the tokens in the input repository are consumed, and new tokens are generated in the output repository.

4. The method according to claim 3, characterized in that, The guard conditions are used to determine the sufficiency of resource tokens, including the determination of the quantity, status, or logical combination of multiple types of resource tokens.

5. The method according to claim 1, characterized in that, The multi-system collaborative control strategy in step S5 is as follows: A global objective function is constructed that includes the state variables of the air supply, intake, and exhaust subsystems. A consistency controller is designed based on the Laplace matrix that characterizes the coupling relationship between the subsystems to coordinate the control output of each subsystem, so that each subsystem satisfies the constraints of the global objective function during the dynamic adjustment process.

6. The method according to claim 5, characterized in that, The expression for the consistency controller is: in, Let i be the state variable of the i-th subsystem. System status rate of change, This represents the state variable of the j-th subsystem that is coupled with the i-th subsystem. System status rate of change, Laplace matrix OK Column elements, It is a gain constant greater than 0.

7. The method according to claim 1, characterized in that, In step S6: The valves in the gas supply subsystem are used to control the gas supply pressure; the valves in the air intake subsystem are used to control the air intake pressure and temperature; the valves in the exhaust subsystem are used to control the exhaust pressure; and each valve is adjusted in conjunction with the command sequence and coordinated control strategy generated by the Petri net.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

Images