A method for inferring state transitions in time-varying system simulation

Abstract

The application discloses a state transition method in deducible time-varying system simulation, and belongs to the technical field of deducible continuous system simulation. The method comprises the following steps: step 1, constructing a pipeline model, and establishing a mapping relationship between the position and state of a research object through the pipeline model; step 2, realizing state prediction of the research object according to the mapping relationship of the pipeline model; and realizing state transition according to the state prediction. The application has the beneficial effect of providing a state transition method in deducible time-varying system simulation, which is low in research and application cost and high in stability.

Description

Technical Field

[0001] This application relates to the field of simulation technology for deducible continuous systems, and more specifically, to a state transition method in the simulation of deducible time-varying systems. Background Technology

[0002] Simulation of continuous systems sometimes requires the system to repeatedly transition between multiple states. For example, in civil aviation maintenance simulators, to compare the state and effects of the airborne system before and after a failure, the simulator needs to repeatedly transition between states before and after the fault setting. This allows trainees to repeatedly compare the differences between the normal and faulty system states, gaining a comprehensive understanding of the fault and achieving the desired training effect. If the fault is triggered during the aircraft's cruise phase, the aircraft needs to quickly reach the cruise phase before setting the fault observation effect, and then quickly land to enter the maintenance state, saving virtual maintenance training time and improving maintenance training efficiency.

[0003] Continuous systems exhibit continuous state changes over time. To achieve state transitions, the system needs to know the state of the target transition point, i.e., state prediction. Numerous algorithms, such as data fitting and state estimation, exist to address this problem. However, these algorithms are often relatively complex and require large amounts of historical data. When the simulation object is sufficiently complex, these algorithms become inadequate. Civil aviation simulators, which simulate the structure and system principles of civil aircraft and demonstrate operational effects, are extremely complex systems. Other systems, such as engineering machinery simulators, are similarly complex. These systems typically have numerous state variables. Using current data fitting or state estimation algorithms, each variable requires a large amount of historical data for prediction and estimation, and a significant portion of this historical data is simply unavailable. Therefore, these methods are currently ineffective for state transitions in continuous systems.

[0004] Therefore, there is a need for a state transition method in the simulation of time-varying systems that is low in research and application costs and highly stable. Summary of the Invention

[0005] The summary section of this application is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] To address the technical problems mentioned in the background section, some embodiments of this application provide a method for state transitions in the simulation of a time-varying system, comprising the following steps:

[0007] Step 1: Construct a pipeline model to establish a mapping relationship between the position and state of the research object;

[0008] Step 2: Predict the state of the research object based on the mapping relationship of the pipeline model, and realize the state transition based on the state prediction.

[0009] Furthermore, the process of constructing the pipeline model is as follows: the position of the pipeline is divided into multiple stages; a univariate function of position and state on the pipeline is established.

[0010] Furthermore, the starting point of the stage is a feature point; the state of the feature point is a feature state.

[0011] Furthermore, the stage in which the pipeline starts is located is the initial stage; the state of the pipeline starting point is the initial state.

[0012] Furthermore, the unary function is:

[0013] S x =h(s x )=f(x)(0≤x≤L); (1)

[0014] Among them, S x =h(s x ) is a function of the set of state variables, representing the state of the object at position x; s x ={s1(x),s2(x),s3(x),..,.sN(x)} is the set of state variables representing the external attributes of the research object at position x; N is the number of external attributes of the research object; x is the position of the research object on the pipeline; L represents the total length of the pipeline.

[0015] Furthermore, the pipeline behavior includes necessary behaviors and interference behaviors; the necessary behaviors include pipeline processes and fault disturbances; the interference behaviors are used to induce state transitions of the research object.

[0016] Further, in step 2, the process of realizing state transition is as follows: sorting the necessary behaviors between adjacent feature points; obtaining a complete set of initial state variables for the initial stage and passing the complete set of initial state variables to the system; the research object first directly transitions from the current state to the initial state according to the complete set of initial state variables; then the research object performs a chain transition from the pipeline starting point according to the sorted necessary behaviors until it transitions to the target state.

[0017] Furthermore, the set of feature points is represented as:

[0018] P = {p1, p2, ... p} m}; (2)

[0019] Where m is the number of stages.

[0020] Furthermore, the stage is represented as:

[0021] ph i , (1≤i≤m). (3)

[0022] The beneficial effect of this application is that it provides a method for state transitions in the simulation of deducible time-varying systems that is low in research and application cost and highly stable. Attached Figure Description

[0023] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application.

[0024] Furthermore, throughout the accompanying drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements are not necessarily drawn to scale.

[0025] In the attached diagram:

[0026] Figure 1 This is a schematic diagram of a pipeline model according to an embodiment of this application;

[0027] Figure 2 This is a schematic diagram of feature points on the pipeline model;

[0028] Figure 3 This is a schematic diagram of the upper stage of the pipeline model;

[0029] Figure 4 This is a schematic diagram indicating the ph0 stage. Detailed Implementation

[0030] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0031] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0032] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0033] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0034] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] An example is a method for state transitions in the simulation of a time-varying system, referring to... Figure 1 This includes the following steps:

[0036] Step 1: Construct a pipeline model to establish a mapping relationship between the position and state of the research object;

[0037] The process of constructing the pipeline model is as follows: the pipeline is divided into multiple stages, and these stages are further categorized into known stages and complex stages based on pipeline behavior; a univariate function relating position and state on the pipeline is established. State transitions of the research object in known stages employ direct transitions, while state transitions in complex stages employ chained transitions. Furthermore, in general, the system contains both known and complex stages; in some simple systems, all stages can be known stages, in which case direct transitions may be sufficient for state transitions; conversely, in some complex systems, all stages can be complex stages, in which case chained transitions may be sufficient for state transitions.

[0038] The pipeline model includes:

[0039] Behavioral Layer: This layer represents the operational behaviors of assembly line workers towards the research object. It is the driving event of the assembly line model and includes necessary behaviors and interference behaviors. Necessary behaviors include assembly line processes and fault disturbances. Necessary behaviors involve the research object experiencing each assembly line process and fault disturbance. The research object moves along the assembly line from the starting point, experiencing each process, and its state continuously changes until the final state. During this process, fault disturbances also cause continuous changes in the research object's state. Interference behaviors are used to induce state transitions in the research object; interference behaviors refer to the assembly line worker picking up the research object and changing its position, which prevents the research object from undergoing normal assembly line processing, thus triggering a state transition.

[0040] Functional layer: Represents the functional model of the research object, used to represent the relationship between the state and behavior of the research object.

[0041] State layer: Used to represent the state of the research object, it is the output of the functional model of the research object after being driven by behavior. This application classifies the state attributes of the research object into internal attributes and external attributes. Internal attributes refer to attributes irrelevant to the research, while external attributes are the opposite. Based on this, the research object can be regarded as a black box, with internal attributes placed inside the black box and no longer concerned, while external attributes represent the exterior of the black box and become the main research content of this application. Taking a civil aircraft as an example, a flight of a civil aircraft can be divided into 10 flight phases. For training purposes, civil aviation simulators expect the simulator state to repeatedly transition between the 10 flight phases. According to the definition of flight phase, to achieve the transition, only the state quantities that can characterize the flight phase need to be considered, such as flight altitude, engine start status, and ground speed, etc., and other state quantities that are irrelevant to these, such as navigation beacon frequency, instrument display pages, and cabin air conditioning temperature, are not concerned. These state quantities that are irrelevant to the research content are classified as internal attributes, and others are classified as external attributes. Thus, the research object is represented as a black box.

[0042] Position layer: used to represent the position of the research object on the pipeline; the position layer includes multiple stages divided according to the pipeline sequence, the starting point of the stage is a feature point; the state of the feature point is a feature state; through the reasonable division of stages on the pipeline, when the research object transitions from the current state, its target state is each feature state, that is, the target state of the research object is not arbitrarily selected on the pipeline, and the state transition of the research object is from the current state to each feature state; the stage where the pipeline starting point is located is the initial stage; the state of the pipeline starting point is the initial state.

[0043] like Figure 2 As shown, the set of feature points is represented as follows:

[0044] P = {p1, p2, ... p} m}; (2)

[0045] Where m is the number of stages.

[0046] like Figure 3 As shown, the stage is represented as follows:

[0047] ph i ,(1≤i≤m); (3)

[0048] On the assembly line, feature point p i to p i+1 The portion between (1≤i<m) is called a stage, and the feature point p m The portion between the end of the pipeline and the final destination is called the stage (ph). m .

[0049] The position and state of the research object form a mapping relationship; each position of the research object in the pipeline is mapped to a fixed set of states, that is, a change in the position of the research object means a change in its state; the state of the research object is predicted based on the mapping relationship; transition control is performed based on the state prediction to realize the state transition.

[0050] The unary function is:

[0051] S x =h(s x )=f(x)(0≤x≤L); (1)

[0052] Among them, S x =h(s x ) is a function of the set of state variables, representing the state of the object at position x; s x ={s1(x),s2(x),s3(x),..,.sN(x)} is the set of state variables representing the external attributes of the research object at position x; N is the number of external attributes of the research object; x is the position of the research object on the pipeline; L represents the total length of the pipeline.

[0053] On the assembly line, the object under study is at position x, and its set of state variables is s. x ={s1(x),s2(x),s3(x),...,sN(x)}, represented by the state vector s x =(s1(x),s2(x),s3(x),...,sN(x)) T At feature point p i The set of state variables is The state vector is

[0054] Step 2: Predict the state of the research object based on the mapping relationship of the pipeline model; realize the state transition based on the state prediction.

[0055] In step 2, the process of realizing state transition is as follows: sorting the necessary behaviors between adjacent feature points; obtaining a complete set of initial state variables for the initial stage and passing the complete set of initial state variables to the system; the research object first directly transitions from the current state to the initial state according to the complete set of initial state variables; then the research object performs a chain transition from the pipeline starting point according to the sorted necessary behaviors until it transitions to the target state.

[0056] The specific method for direct transition is as follows: Obtain a complete set of characteristic state variables for the known stage, and transfer the complete set of characteristic state variables to the system; the research object first directly transitions from the current state to the target state based on the complete set of characteristic state variables. Let the state transition matrix be B, then... In the formula, B has infinitely many solutions, meaning that at any given time, there are infinitely many ways to achieve the instantaneous state transition. We can take the diagonal matrix as a solution to the above formula, and let...

[0057]

[0058] That will satisfy Right now

[0059]

[0060] The diagonal matrix B completely eliminates the correlation between the state variables in the solution. It can be seen that its practical meaning is "state replacement," which can be achieved by directly replacing the corresponding state before the jump with the feature state of the feature point where the research object is located after the jump. The algorithm implementation is as follows:

[0061] Algorithm 1: Direct Transition

[0062] Input: Feature point p after the jump i .

[0063] Output: The state of the research object after the jump.

[0064]

[0065] The direct transition method is simple to implement, as long as the set of variables representing the characteristic state is complete, and the program implementation ensures that the system can continue to run on the basis of the new state variables.

[0066] When dealing with some complex systems, the difficulty of direct transitions increases dramatically. Firstly, due to S... x In a simulation system, the values ​​of each state variable are fixed. When an external disturbance, such as a fault, is added to the system, a direct transition will cause the fault to disappear. If the fault is to be retained after the transition, the transition must be performed first, and then the fault must be reinjected, which may introduce instability into the system. Secondly, it is difficult to obtain a complete set of state variables in practical applications, especially since the number of variables may change as the system runs, further increasing the difficulty of obtaining a complete set of state variables. Even ignoring a single variable can potentially pose a threat to the system's safety and stability.

[0067] The specific method for performing chain transitions is as follows: The necessary actions between adjacent feature points are ordered; a complete set of initial state variables is obtained and transmitted to the system; the research object first transitions directly from its current state to the initial state based on the complete set of initial state variables; then, the research object sequentially performs chain transitions from the pipeline starting point according to the ordered necessary actions until it reaches the target state. That is, the research object jumps from position x to the target feature point p. iAt that time, its state does not immediately transition to the characteristic state of the target point, but first uses a direct transition method to jump to the pipeline start point p1. The state of the object under study first directly transitions to the initial state by resetting, and the initial state set is represented as follows. Then, all processes in stage ph1 are simulated sequentially along the production line, while the production line is run at high speed to achieve rapid state evolution. Then, stage ph2 is entered, and the processes in stage ph2 are simulated sequentially... This cycle repeats, and the position of the research object moves continuously from point p1 to point p2, p3, etc., in a short period of time, until p... i The state of the research object also changes from the initial state. Rapidly evolved to p i The characteristic state of a point

[0068] During the state reset process, the system state transitions directly, and the state vector... in,

[0069]

[0070] During production, workers must observe the state of materials on the assembly line and can only complete a process step under specific conditions. Similarly, in this model, to achieve a chain transition, the processes between every two feature points must first be ordered, and the state evolution of the research object must be analyzed to understand the triggering conditions of each process. Timely simulation of the processes ensures that the research object can jump from any current point to the target feature point in a very short time. This method does not require considering how to obtain a complete set of feature state variables, thus eliminating variable omissions and ensuring high system stability.

[0071] The algorithm sorts the processes at each stage and edits the judgment and triggering of the execution conditions for each process into a function, allowing it to be completed automatically under program control. Sequential calls can realize the state transition from point p1 to each feature point, achieving a chain transition. A fault is injected into the execution conditions of a certain process. Based on the system functional model, the fault disturbance will produce the expected fault effect. As the production line operates, a fault occurring at a certain stage can be simulated, and the effect of the fault can be observed at another stage. The algorithm implementation is as follows:

[0072] Algorithm 2: Chain Transition

[0073] Input: The feature point index i after the jump (equivalent to feature point p) i ).

[0074] Output: The system state after the jump.

[0075]

[0076]

[0077] As shown in Algorithm 2, the chain transition has no requirements on the initial characteristic state set of each stage, only on the process and trigger of each stage. According to the production process of the assembly line, the process of each stage is known and clear. As long as the program logic is correct, there are almost no stability problems, which greatly improves the system stability under the chain transition.

[0078] The key to chain transitions is the ability to correctly sequence all necessary behaviors within each stage, grasp the preconditions for each step, and write self-triggering functions for them. This requires developers to have a deep understanding of the system's functional principles and a thorough grasp of the necessary behaviors at each stage, placing high demands on development skills and creating a high barrier to entry. However, if developers possess this ability, the state transitions achieved through chain transitions are highly recommended due to their high stability. Note that "necessary behaviors" here refer to behaviors that only affect the attributes of the research object of interest in the study.

[0079] When a system needs to inject a fault, once the fault injection procedure is implemented within a certain process, almost no additional work is required to prepare for the fault effect. This is because the fault is injected into the system's functional model, and the fault effect will be generated within the functional model and radiate outwards. The rapid operation of the pipeline only accelerates the evolution of the system's state; it does not change the system's operating logic, much less alter the system's intended state at each stage.

[0080] For simple systems, where most stages are known, direct transitions can be used alone. Direct transitions are simple, straightforward, and easy to implement. For complex systems, which have many complex stages, chained transitions can ensure high system stability and provide more flexible fault injection. Since complex systems often contain some known stages, direct transitions and chained transitions can be used in combination depending on the characteristics of the modeling object.

[0081] Furthermore, the method of this application is applied to an aircraft maintenance trainer to achieve jumps between ten flight phases in flight simulation. Its pipeline model includes:

[0082] Behavior layer: This includes necessary behaviors and intervention behaviors. Necessary behaviors refer to all operations that enable the aircraft to complete its flight path, as well as fault injections from the trainer aircraft; intervention behaviors refer to operations performed by the trainee aircraft to control flight phase transitions.

[0083] Functional layer: This is the airborne system functional model, which completes the functional simulation of each airborne system and realizes the various functions and instrument indications of the aircraft.

[0084] State: Indicates the changes in the state variables of each airborne system after the aircraft accepts the pilot's input.

[0085] Location layer: Represents the "position" of an aircraft during a complete flight route. The location layer is divided into ten stages: ph1, ph2, ph3, ..., ph 10 This corresponds to ten flight phases. After the aircraft receives input from the pilot, it outputs continuous and nonlinear states of various airborne systems through the functional model. These states are ultimately mapped to the aircraft's position on the position layer.

[0086] When the aircraft cockpit simulation module starts, it first completes initialization. Following system power-on and self-test, some variables are preset, and then the various airborne systems begin operation. As analyzed earlier, the "flight phase transition" uses a chain transition method. However, if each transition starts from the defined ph1 phase, it means the system needs to power off and then power on again. Trainees will see all displays and panel indicator lights turn off and then on again, and then wait another 40 seconds for the system self-test, resulting in a poor user experience. Considering that the initialization, power-on, and self-test processes of the aircraft maintenance trainer do not involve any interference input, and that most systems have such an initialization process without the interference input referred to in this application, the chain transition method is further optimized.

[0087] In the system initialization process without any interference input, a ph0 phase was added before the ph1 phase, such as... Figure 4 As shown, system initialization, power-on, and self-test are completed in phase ph0, while phase ph1 begins with the preset variables after the system self-test, completing the system functions of flight phase 1. Thus, if the target flight phase ph is set... i In a chain transition, there is no need to jump to the ph0 stage; you can jump directly to the ph1 stage to start. This can achieve the task objective and also bring a better user experience.

[0088] Furthermore, the method of this application was verified by simulation. In the simulation verification, the test aircraft was tested by jumping directly from flight phase 1 to flight phase 6, and from flight phase 6 to flight phase 10, and the test results are shown in Table 1.

[0089] Table 1 Summary of Test Results

[0090]

[0091] As shown in Table 1, after the trainee sets the jump command for the flight phase, the aircraft can quickly jump from the current flight phase to the target flight phase and continue flying as planned. Under conditions of no oscillation in the state variables, the entire process takes less than 200ms. If oscillations exist in the state variables, the state stabilizes after 3-5 seconds, achieving the state transition after the flight phase jump. Test results show that all aircraft parameters can complete the state transition within 200ms, and the aircraft as a whole stabilizes within 6 seconds, meeting the transition requirements. Compared with flight simulations without phase jumps, this significantly improves flight simulation efficiency and meets the needs of classroom experiments and practical training.

[0092] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

[0093] In summary, the method of this application establishes a mapping relationship between the state and position of the research object based on the pipeline model, realizes the state prediction of the research object based on the mapping relationship, and realizes the state transition based on the state prediction; thus greatly reducing the modeling workload, the model is simple and stable, and supports the stable transition of the research object's state, with the advantages of low research and application cost and high stability.

Claims

1. A method for state transitions in the simulation of a time-varying system that can be extrapolated, characterized in that: Includes the following steps: Step 1: Construct a pipeline model to establish a mapping relationship between the position and state of the research object; The process of constructing the pipeline model is as follows: the pipeline is divided into multiple stages; Establish a univariate function relating position and state on the assembly line; The pipeline model includes: Behavioral layer: used to represent the operational behavior of assembly line workers towards the research object. It is the driving event of the assembly line model, including necessary behaviors and intervention behaviors; Functional layer: Represents the functional model of the research object, used to represent the relationship between the state and behavior of the research object; State layer: used to represent the state of the research object, and is the output of the functional model of the research object after receiving behavioral drive; Location layer: Used to represent the position of the research object on the production line; Step 2: Predict the state of the research object based on the mapping relationship of the pipeline model, and realize the state transition based on the state prediction.

2. The state transition method in the simulation of a time-varying system according to claim 1, characterized in that: The starting point of the stage is the feature point; the state of the feature point is the feature state.

3. The state transition method in the simulation of a time-varying system according to claim 2, characterized in that: The stage in which the pipeline starts is located is the initial stage; the state of the pipeline starting point is the initial state.

4. The state transition method in the simulation of a time-varying system according to claim 3, characterized in that: The unary function is: ；（1） in, Let x be a function of the set of state variables, representing the state of the object under study at position x. Let N be the set of state variables representing the external attributes of the research object at position x; N be the number of external attributes of the research object; x be the position of the research object on the pipeline; and L be the total length of the pipeline.

5. The state transition method in the simulation of a time-varying system according to claim 4, characterized in that: The pipeline behavior includes necessary behaviors and interference behaviors; the necessary behaviors include pipeline processes and fault disturbances; the interference behaviors are used to induce state transitions of the research object.

6. The state transition method in the simulation of a time-varying system according to claim 5, characterized in that: In step 2, the process of realizing the state transition is as follows: sorting the necessary behaviors between adjacent feature points; obtaining a complete set of initial state variables for the initial stage, and passing the complete set of initial state variables to the system; The research object first transitions directly from the current state to the initial state based on a complete set of initial state variables; Then, the research object performs a chain transition from the starting point of the pipeline according to the necessary actions after sorting, until it reaches the target state.

7. The state transition method in the simulation of a time-varying system according to claim 6, characterized in that: The set of feature points is represented as follows: ；(2) Where m is the number of stages.

8. The state transition method in the simulation of a time-varying system according to claim 7, characterized in that: The stage is represented as follows: 。(3)

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