A state transition method for deductive continuous system simulation

Abstract

The application discloses a pipeline model for deducible continuous system simulation, and belongs to the technical field of deducible continuous system simulation. The pipeline model comprises a behavior layer, a function layer, a state layer and a position layer. The behavior layer is used for representing the operation behaviors of pipeline workers on a research object, and the operation behaviors include necessary behaviors and intervention behaviors. The function layer is used for representing the relationship between the state and the behavior of the research object. The state layer is used for representing the state of the research object. The position layer is used for representing the position of the research object. The position and the state of the research object form a mapping relationship. The state prediction of the research object is realized according to the mapping relationship. The state transition control is performed according to the state prediction, and the state transition is realized. The pipeline model for deducible continuous system simulation has the advantages of low application cost and high 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 for simulation of deducible continuous 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 for simulation of deducible continuous 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 state transition method for simulation of a deducible continuous system, used to predict the state of the system and realize state transitions based on the state predictions, including:

[0007] Behavioral layer: used to represent the operational behaviors of assembly line workers towards the research object, including necessary behaviors and intervention behaviors;

[0008] Functional layer: used to represent the relationship between the state and behavior of the research object;

[0009] State layer: used to represent the state of the research object;

[0010] Location layer: used to represent the location of the research object;

[0011] The position and state of the research object are mapped; the state of the research object is predicted based on the mapping; and transition control is performed based on the state prediction to achieve state transition.

[0012] Furthermore, the necessary actions include assembly line processes and fault disturbances.

[0013] Furthermore, the interference behavior is used to induce a state transition of the research object.

[0014] Furthermore, the location layer includes multiple stages divided in pipeline sequence, with the starting point of each stage being a feature point.

[0015] Furthermore, the state of the feature point is the feature state; the state of the pipeline starting point is the initial state.

[0016] Furthermore, the mapping relationship is a unary function:

[0017] (1)

[0018] in, A function of the set of state variables, representing the state of the object under study at its position. The state of being; For the research subjects in location The set of state variables that characterize its external attributes; To determine the number of external attributes of the research object; The location of the research object on the assembly line; This represents the total length of the production line.

[0019] Furthermore, the set of feature points is represented as:

[0020] (2)

[0021] in, The number of stages.

[0022] Furthermore, the stage is represented as:

[0023] (3)

[0024] Furthermore, the method of realizing state transition includes direct transition; the method of direct transition is as follows: obtain a complete set of feature state variables, pass the complete set of feature state variables to the system; according to the complete set of feature state variables, the research object directly transitions from the current state to the target state.

[0025] Furthermore, the method for achieving state transition also includes chain transition; the method of chain transition is as follows: the necessary behaviors between adjacent feature points are sorted; the research object returns from the current state to the initial state, and then, according to the sorted necessary behaviors, state transitions are performed sequentially in a pipeline order until the target state is reached.

[0026] The beneficial effect of this application is that it provides a state transition method for simulation of deducible continuous systems that has low research and application costs and high stability. Attached Figure Description

[0027] 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.

[0028] 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.

[0029] In the attached diagram:

[0030] Figure 1 This is a schematic diagram of a pipeline model according to an embodiment of this application;

[0031] Figure 2 This is a schematic diagram of feature points on the pipeline model;

[0032] Figure 3 This is a schematic diagram of the upper stage of the pipeline model. Detailed Implementation

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] An embodiment provides a state transition method for simulation of a deducible continuous system, used to predict the system's state and perform transition control based on the state prediction, referring to... Figure 1 ,include:

[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 this 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 each stage is a feature point; the state of each feature point is a feature state; through the reasonable division of stages on the pipeline, when the research object transitions from its current state, its target state is each of the feature states, 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 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] (2)

[0045] in, The number of stages.

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

[0047] (3)

[0048] On the assembly line, feature points arrive The portion between these points is called a stage, or feature point. The portion between the end of the production line and the end point is called a stage. .

[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 mapping relationship is a unary function:

[0051] (1)

[0052] in, A function of the set of state variables, representing the state of the object under study at its position. The state of being; For the research subjects in location The set of state variables that characterize its external attributes; To determine the number of external attributes of the research object; The location of the research object on the assembly line; This represents the total length of the production line.

[0053] On the assembly line, the research subjects are in position At this location, its set of state variables is Represented by a state vector ; at feature points The set of state variables is The state vector is .

[0054] The research subjects are located Jump to feature point At that point, the state transitioned to... Point-characteristic states. This application proposes two state transition methods: direct transition and chain transition.

[0055] The method for achieving state transition includes direct transition; the method of direct transition is as follows: obtain a complete set of characteristic state variables, and pass the complete set of characteristic state variables to the system; based on the complete set of characteristic state variables, the research object directly transitions from the current state to the target state. The state transition matrix is ​​set as follows. ,but In the formula, There are infinitely many solutions, meaning that at any given moment, there are infinitely many ways to achieve the instantaneous state transition. We can take the diagonal matrix as a solution to the above equation, and let...

[0056] (4)

[0057] That will satisfy ,Right now

[0058] (5)

[0059] diagonal matrix This 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:

[0060] Algorithm 1: Direct Transition

[0061] Input: Feature points after the jump .

[0062] Output: The state of the research object after the jump.

[0063] Void DIR_Transition(p) / / p is the feature point after the transition.

[0064] {

[0065] TO_State.b1=p.a1; / * Pass the feature point state to the system through the TO_State state set * /

[0066] TO_State.b2=p.a2;

[0067] ...

[0068] TO_State.bn=p.an; / * Transfers the state variable at point p to system variables b1 to bn. These variables act on various airborne systems, allowing the simulation program to continue running on this basis. * /

[0069] }

[0070] 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.

[0071] When dealing with some complex systems, the difficulty of direct transitions increases dramatically. Firstly, because... In a pipelined system, the values ​​of each state variable are fixed. When an external disturbance, such as a fault, is added to the simulation 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 injected again, 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 may pose a threat to the safety and stability of the system. Therefore, this application proposes another method for state transitions in complex systems based on the pipelined model: chain transitions.

[0072] The method for achieving state transitions also includes chain transitions; the chain transition method involves: sorting the necessary behaviors between adjacent feature points; the research object returns to the initial state from the current state through a direct transition, and then, according to the sorted necessary behaviors, performs state transitions sequentially in a pipeline order until it reaches the target state. That is, the research object changes state from its current state to its initial state through a direct transition. Jump to the target feature point At that time, its state does not immediately transition to the characteristic state of the target point, but first jumps to the starting point of the pipeline. The state of the research object is first directly transitioned to the initial state by resetting, and the initial state set is represented as follows: Then, the simulation proceeded in the order of the production line. All processes in the stage simultaneously operate the assembly line at extremely high speeds to achieve rapid state evolution, after which it enters... Stages, then simulated sequentially. Stages of process... Through such a cycle, the position of the research object changes from... The point moves continuously to [location] in a short period of time. point, Wait until The state of the research object also changes from the initial state. Rapidly evolved to The characteristic state of a point .

[0073] During the state reset process, the system state transitions directly, and the state vector... ,in,

[0074] (6)

[0075] 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.

[0076] The process of each stage is sorted out, and the judgment and trigger of the execution conditions for each process are edited into a function, so that it can be completed automatically under program control. Sequential calls will achieve the desired result. the state from the point to each feature point, so as to realize chained transition. By injecting a fault into the execution condition of a certain process, based on the system function model, the fault disturbance will produce the corresponding fault effect. As the assembly line operates, it is possible to simulate that a fault occurs at a certain stage, and observe the effect of the fault effect at another stage. The algorithm is implemented as follows:

[0077] Algorithm 2: Chained Transition

[0078] Input: the serial number of the feature point after transition (equivalent to the feature point ).

[0079] Output: the system state after transition.

[0080] Void INDIR_Transition(int i) / *i is the serial number of the feature point after transition* /

[0081] {

[0082] int j=0; / / j is the serial number of the stage triggered by the currently completed process

[0083] While(j<i) / / sequentially complete all processes of stages before stage i

[0084] {

[0085] Action_Trigger(j); / *complete all processes of stage j, where j=0 corresponds to system initialization* /

[0086] Cur_p=j+1; / *after completing all processes of stage j, the current feature point is the feature point of the next stage* /

[0087] j++; / / prepare to complete the process trigger of the next stage

[0088] }

[0089] }

[0090] Void Aciton_Trigger(int ph) / / ph is the serial number of the stage after transition

[0091] {

[0092] int q=0; / / q is used to record the serial number of the current process in the current stage

[0093] int N=ph_n; / / total number of processes in the stage

[0094] While(q<N) / *sequentially complete all processes of the stage * /

[0095] {

[0096] If(Action_Condition(ph,q)==true) / *Judgement Are the prerequisites for the q-th process in stage satiated?

[0097] {

[0098] Action(ph, q); / / Complete this step.

[0099] If (False(ph, q) == true) / * If a fault exists in the current process * /

[0100] {

[0101] False_Trigger(ph,q); / * Implements this fault injection procedure * /

[0102] }

[0103] q++; / / Prepare to complete the next step

[0104] }

[0105] else

[0106] {

[0107] Set_Action_Condition(ph,q); / * Set The prerequisites for the q-th operation in stage q are met, and the operation triggering conditions are satisfied.

[0108] continue; / * Continue waiting * / The trigger condition for the q-th process in stage q is met.

[0109] }

[0110] }

[0111] }

[0112] 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, 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.

[0113] 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.

[0114] When a system needs to be injected with a fault disturbance, after implementing the fault disturbance injection procedure within a certain process, almost no additional work is required for the fault effect. This is because the fault disturbance is injected into the system functional model, and the fault disturbance effect will be generated within the functional model and radiate outwards. The rapid operation of the pipeline only accelerates the evolution of the system state; it does not change the system's operating logic, much less the state that the system should have at each stage.

[0115] For simple systems, direct transitions can be used alone; they are straightforward and easy to implement. For complex systems, cascading transitions ensure high system stability and offer more flexible fault injection. The two methods, direct and cascading transitions, can be used in combination depending on the characteristics of the system being modeled.

[0116] 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.

[0117] In summary, 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; this greatly reduces the modeling workload, the model is simple and stable, supports stable state transitions of the research object, and has the advantages of low research and application cost and high stability.

Claims

1. A state transition method for simulation of a deducible continuous system, used to predict the state of the system and realize state transitions based on the state predictions, characterized in that... include: Step 1: Create the pipeline model; The pipeline model includes: Behavioral layer: used to represent the operational behaviors of assembly line workers towards the research object, including necessary behaviors and intervention behaviors; Functional layer: 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; Location layer: used to represent the location of the research object; Step 2: Establish a mapping relationship between the position and state of the research object; predict the state of the research object based on the mapping relationship; perform transition control based on the state prediction to achieve state transition; The methods for implementing state transitions include direct transitions and chain transitions. The prerequisite for chain transitions is that all necessary actions within each stage are correctly ordered and each stage has a self-triggering function.

2. The state transition method for simulation of a deducible continuous system according to claim 1, characterized in that: The necessary actions include assembly line processes and fault disturbances.

3. The state transition method for simulation of a deducible continuous system according to claim 2, characterized in that: The interference behavior is used to induce a state transition in the research object.

4. The state transition method for simulation of a deducible continuous system according to claim 3, characterized in that: The location layer includes multiple stages divided in pipeline sequence, with each stage starting at a feature point.

5. The state transition method for simulation of a deducible continuous system according to claim 4, characterized in that: The state of the feature point is the feature state; The state of the starting point of the production line is the initial state.

6. The state transition method for simulation of a deducible continuous system according to claim 5, characterized in that: The mapping relationship is a unary function: ； in, A function of the set of state variables, representing the state of the object under study at its position. The state of being; For the research subjects in location The set of state variables that characterize its external attributes; To determine the number of external attributes of the research object; The location of the research object on the assembly line; This is expressed as the total length of the production line.

7. The state transition method for simulation of a deducible continuous system according to claim 6, characterized in that: The set of feature points is represented as follows: ； in, The number of stages.

8. The state transition method for simulation of a deducible continuous system according to claim 7, characterized in that: The stage is represented as follows: 。 9. The state transition method for simulation of a deducible continuous system according to claim 8, characterized in that: The methods for achieving state transition include direct transition; the method for direct transition is as follows: obtain a complete set of characteristic state variables, pass the complete set of characteristic state variables to the system; based on the complete set of characteristic state variables, the research object directly transitions from the current state to the target state.

10. The state transition method for simulation of a deducible continuous system according to claim 8, characterized in that: The method for achieving state transition also includes chain transition; the chain transition method is as follows: sorting the necessary behaviors between adjacent feature points; the research object returns from the current state to the initial state, and then according to the sorted necessary behaviors, performs state transitions in a pipeline order until the target state is reached.

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