Method for deducing state transition in time-varying system simulation

Abstract

The invention discloses a state transition method in deductable time-varying system simulation, and belongs to the technical field of deductable continuous system simulation. Comprising the following steps: step 1, constructing an assembly line model, and establishing a mapping relation between the position and state of a research object through the assembly line model; 2, state prediction of a research object is achieved according to the mapping relation of the pipeline model; and realizing state transition according to state prediction. The method for deducing state transition in time-varying system simulation has the advantages of being low in research and application cost and high in stability.

Description

Technical Field

[0001] The present application relates to the technical field of deducible continuous system simulation, and in particular to a state transition method in deducible time-varying system simulation. Background Art

[0002] Simulation of continuous systems sometimes requires the system to repeatedly transition between multiple states. For example, in civil aviation maintenance simulators, in order to compare the state and effect of the airborne system before and after the system fails, the simulator state needs to repeatedly transition before and after the fault is set, so that trainees can repeatedly compare the difference in system state under normal and fault conditions, and fully understand the fault to achieve the training effect. If the fault is triggered during the aircraft's cruising phase, it is necessary to quickly make the aircraft reach the cruising phase, set the fault observation effect, and then quickly land and enter the maintenance state to save virtual maintenance training time and improve maintenance training efficiency.

[0003] The state of a continuous system changes continuously over time. To achieve state transition, the system needs to know the state of the transition target point, that is, state prediction. For the state prediction of such systems, there are many algorithms such as data fitting and state estimation to solve this problem. However, such algorithms are often relatively complex and require a large amount of historical data. When the simulation object is complex enough, such algorithms will appear to be unable to cope with it. Civil aviation simulators simulate the composition and system principles of civil aviation passenger aircraft and demonstrate the operation effects. The system is extremely complex. Others, such as engineering machinery simulators, are equally complex. Such systems have many state variables. Using the current data fitting or state estimation algorithms, each variable requires a large amount of historical data for prediction and estimation, and a considerable part of the historical data cannot be obtained at all. Therefore, these methods are currently difficult to play a role in the transition of continuous system states.

[0004] Therefore, there is a need for a method that has low research and application cost, high stability and can deduce state transition in time-varying system simulation. Summary of the invention

[0005] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] In order to solve the technical problems mentioned in the above background technology section, some embodiments of the present application provide a method for deducing state transition in time-varying system simulation, comprising the following steps:

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

[0008] Step 2: Realize the state prediction of the research object according to the mapping relationship of the pipeline model, and realize the state transition according to the state prediction.

[0009] Furthermore, the process of constructing the pipeline model is: dividing the position of the pipeline into multiple stages; and establishing a unary function of the position and state on the pipeline.

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

[0011] Furthermore, the stage where the starting point of the pipeline is located is the initial stage; the state of the starting point of the pipeline 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 state variable set, which is expressed as the state of the research object at position x; s x ={s1(x),s2(x),s3(x),..,.sN(x)} is the set of state variables that characterize 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 assembly line; L represents the total length of the assembly line.

[0015] Furthermore, the pipeline behavior includes necessary behavior and interfering behavior; the necessary behavior includes pipeline procedures and fault disturbances; and the interfering behavior is used to cause state transition of the research object.

[0016] Furthermore, in step 2, the process of achieving state transition is: sorting the necessary behaviors between adjacent feature points; obtaining a complete set of initial state variables in 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 chain transitions in sequence from the starting point of the pipeline according to the sorted necessary behaviors until it transitions to the target state.

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

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

[0019] Here, m is the number of stages.

[0020] Further, the stages are expressed as:

[0021] ph i , (1≤i≤m). (3)

[0022] The beneficial effect of the present application is that it provides a state transition method in time-varying system simulation that has low research and application cost and high stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The illustrative embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application.

[0024] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.

[0025] In the attached picture:

[0026] Figure 1 is a schematic diagram of an assembly line model according to an embodiment of the present application;

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

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

[0029] Figure 4 It is a schematic diagram indicating the pH0 stage. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0031] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

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

[0033] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0034] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0035] Embodiment, a method for deriving state transition in time-varying system simulation, referring to Figure 1 , including the following steps:

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

[0037] The process of constructing the pipeline model is: divide the position of the pipeline into multiple stages, divide the stages into known stages and complex stages according to the pipeline behavior; establish a unary function of the position and state on the pipeline. The state transition of the research object in the known stage adopts the direct transition method, and the state transition of the research object in the complex stage adopts the chain transition method. At the same time, under normal circumstances, the system contains both known stages and complex stages; in some simple systems, all of its stages can be known stages, then the state transition of the simple system can only adopt direct transition; in some complex systems, all of its stages can be complex stages, then the state transition of the complex system can only adopt chain transition.

[0038] The pipeline model includes:

[0039] Behavior layer: used to represent the operational behavior of the assembly line workers on the research object. It is the driving event of the assembly line model, including necessary behaviors and interference behaviors; the necessary behaviors include assembly line processes and fault disturbances; necessary behaviors include the research object experiencing each assembly line process and fault disturbance on the assembly line. The research object moves along the assembly line from the starting point of the assembly line, undergoing one assembly line process after another, and the state of the research object changes continuously until the final state. In this process, fault disturbances also cause continuous changes in the state of the research object. The interference behavior is used to cause the state transition of the research object; the interference behavior refers to the behavior of the assembly line worker picking up the research object and changing its position, which makes the research object unable to accept the normal assembly line process processing, causing the state transition of the research object.

[0040] Functional layer: represents the functional model of the research object, which is used to express the relationship between the state and behavior of the research object.

[0041] State layer: used to represent the state of the research object, which is the output of the research object function model after accepting the behavior drive; this application classifies the state attributes of the research object into internal attributes and external attributes. Internal attributes refer to attributes that are not related to the research, while external attributes are the opposite. Based on this, the research object can be regarded as a black box, and the internal attributes are placed in the black box and no longer concerned. The external attributes are manifested as the exterior of the black box and become the main research content of this application. Taking civil aircraft as an example, a flight of a civil aircraft can be divided into 10 flight phases. For training purposes, civil aviation simulators are expected to repeatedly transition between 10 flight phases. According to the definition of flight phases, to achieve transitions, only the state quantities that can characterize the flight phases, such as flight altitude, engine start status, and ground speed, are needed, and other state quantities that are not related to this are not concerned, such as navigation station frequency, instrument display page, and cabin air conditioning temperature. These state quantities that are not related to the research content are attributed to internal attributes, and others are attributed to external attributes. As a result, the research object is characterized as a black box.

[0042] Position layer: used to indicate the position of the research object on the assembly line; the position layer includes multiple stages divided according to the assembly line sequence, the starting point of the stage is the feature point; the state of the feature point is the feature state; through the reasonable division of the stages on the assembly line, 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 randomly selected on the assembly line, and the state transition of the research object is from the current state to each feature state; the stage where the assembly line starting point is located is the initial stage; the state of the assembly line starting point is the initial state.

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

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

[0045] Here, m is the number of stages.

[0046] like Figure 3 As shown, the stages are represented as follows:

[0047] ph i , (1≤i≤m); (3)

[0048] On the assembly line, feature point p i to p i+1 The part between (1≤i<m) is called the stage, and the feature point p m The part between the end of the pipeline is called 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 state set, that is, the change of the position of the research object means the change of its state; the state prediction of the research object is realized according to the mapping relationship; transition control is performed according to the state prediction to realize 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 state variable set, which is expressed as the state of the research object at position x; s x ={s1(x),s2(x),s3(x),..,.sN(x)} is the set of state variables that characterize 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 assembly line; L represents the total length of the assembly line.

[0053] On the assembly line, the object is at position x, and its state variable set is s x ={s1(x),s2(x),s3(x),...,sN(x)}, expressed as state vector s x =(s1(x),s2(x),s3(x),...,sN(x)) T ; At feature point p i The state variable set is The state vector is

[0054] Step 2: Realize the state prediction of the research object according to the mapping relationship of the pipeline model; realize the state transition according to the state prediction.

[0055] In step 2, the process of achieving state transition is: sorting the necessary behaviors between adjacent feature points; obtaining a complete set of initial state variables in 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 chain transitions in sequence from the starting point of the pipeline according to the sorted necessary behaviors until it transitions to the target state.

[0056] The specific method of direct transition is as follows: obtain a complete set of characteristic state variables in a 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 according to the complete set of characteristic state variables. Assuming the state transfer matrix is ​​B, then In the formula, B has infinite solutions, that is, at any time, there are infinite ways to achieve instantaneous state transition. The diagonal matrix can be taken as the solution of the above formula, let

[0057]

[0058] Can 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 actual meaning is "state replacement", which can be achieved by directly replacing the corresponding state before the jump with the characteristic state of the characteristic point where the research object is located after the jump. The algorithm is implemented as follows:

[0061] Algorithm 1: Direct Transition

[0062] Input: Feature point p after 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 when implementing the program, it is ensured that the system can continue to run on this basis after obtaining the new state variables.

[0066] When facing some complex systems, the difficulty of direct transition will increase sharply. First, due to S x The values ​​of each state variable in the simulation are fixed. When an external disturbance is added to the simulation system, such as injecting a fault, a direct transition will cause the fault to disappear. If the fault is to be retained after the transition, the only way is to transition first and then inject the fault again, which may bring instability to the system. Secondly, it is difficult to obtain a complete set of state variables in practical applications, especially as the number of variables may change as the system runs, which further increases the difficulty of obtaining a complete set of state variables. Even ignoring one variable may bring hidden dangers to the safety and stability of the system.

[0067] The specific method of chain transition is as follows: sort the necessary behaviors between adjacent feature points; obtain a complete set of initial state variables in the initial stage, and transfer 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 sequentially performs chain transitions from the starting point of the pipeline according to the sorted necessary behaviors until it transitions to the target state. That is, the research object jumps from position x to the target feature point p i, its state does not immediately jump to the characteristic state of the target point, but first uses the direct jump method to jump to the starting point p1 of the pipeline. The state of the research object first jumps directly to the initial state by resetting. The initial state set is expressed as After that, all the processes of the ph1 stage are simulated in sequence according to the assembly line, and the assembly line is operated at a high speed to achieve rapid state evolution. Then, the ph2 stage is entered, and the processes of the ph2 stage are simulated in sequence... After such a cycle, the position of the research object moves continuously from point p1 to point p2, point p3, etc. in a short time until p i point, the state of the research object also changes from the initial state Rapid evolution to p i Feature state of a point

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

[0069]

[0070] When carrying out production activities, workers have to observe the state of materials beside the assembly line, and can only operate and complete a certain process under a specific state. Similarly, in this model, to achieve chain transition, we must first sort the processes between every two feature points, analyze the state evolution process of the process research object, grasp the triggering conditions of each process, and simulate the process in a timely manner, so as to ensure that the research object can jump from the current arbitrary point to the target feature point in a very short time. This processing method does not need to consider how to obtain a complete set of feature state variables, so there is no omission of variables, ensuring the high stability of the system.

[0071] Sort each process in each stage, edit the judgment and trigger of each process execution condition into a function, so that it can be automatically completed under program control, and the state from point p1 to each feature point can be realized by sequential call, realizing chain transition. Inject a fault into the execution condition of a process. According to the system function model, the fault disturbance will produce the expected fault effect. As the assembly line runs, it is possible to simulate a fault at a certain stage and check the effect of the fault effect at another stage. The algorithm is implemented as follows:

[0072] Algorithm 2: Chain Transition

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

[0074] Output: System status after the jump.

[0075]

[0076]

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

[0078] The key to chain transitions is to be able to correctly sort all the necessary behaviors in each stage, master the prerequisites for each process, and be able to write self-triggering functions for them. This requires developers to have a deep understanding of the functional principles of the system and a thorough understanding of the necessary behaviors in each stage. It also places high demands on development capabilities and has a high threshold. However, if developers have this ability, the state transitions achieved by chain transitions will be recommended due to their higher stability. Note that the "necessary behaviors" here refer to behaviors that only affect the attributes of the research object of interest.

[0079] When the system needs to inject faults, after implementing the fault injection program in a certain process, there is almost no need to do any additional work for the fault effect. Because the fault is injected into the system functional model, the fault effect will be generated in the functional model and radiate outward. The fast operation of the assembly line only speeds up the evolution of the system state, and will not change the system operation logic, let alone the state that the system should have at each stage.

[0080] For simple systems, most of the stages are known stages, and the direct transition method can be used alone. The direct transition method is simple and easy to implement. For complex systems, there are many complex stages, and the chain transition method can ensure the high stability of the system and make fault injection more flexible. Since complex systems often contain certain known stages, the direct transition and chain transition methods can be used in combination according to the characteristics of the modeling object.

[0081] Furthermore, the method of the present application is applied to a maintenance trainer to achieve the jump of ten flight phases in flight simulation. Its pipeline model includes:

[0082] Behavior layer: includes necessary behaviors and interference behaviors. Necessary behaviors refer to all operations that affect the aircraft and fault injection from the instructor aircraft to enable the aircraft to complete the route flight; interference behaviors refer to the operations of the student aircraft to control the flight phase jump.

[0083] Functional layer (Function): that 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 layer: indicates the changes in the state variables of each onboard system after the aircraft accepts the pilot's operation.

[0085] Location layer: expresses the "position" of the aircraft in a complete flight route. The location layer is divided into ten stages: ph1, ph2, ph3, ..., ph 10 , corresponding to ten flight phases. After the aircraft receives the pilot's operation, the functional model outputs continuous and nonlinear states of each onboard system, which are ultimately mapped to the position of the aircraft on the position layer.

[0086] When the aircraft cockpit simulation module is started, initialization is completed first, and as the system is powered on and self-checked, some variables are preset, and then the various airborne systems are operated. According to the previous analysis, the "flight phase jump" is suitable for the chain jump method, but if each jump starts from the defined ph1 stage, it means that the system needs to be powered off and then powered on again. The trainees will see the process of all display screens and panel indicator lights turning off and then lighting up again, and wait for 40 seconds for the system to self-check again, which will bring a poor application experience. Considering that there will be no interfering behavior input during the initialization, power-on and self-check process of the maintenance trainer, and most systems will have such an initialization process, there will be no interfering behavior input referred to in this application, so the chain jump method is further optimized.

[0087] During the system initialization process without interfering behavior input, the ph0 stage is added before the ph1 stage, such as Figure 4 As shown, in the ph0 stage, the system initialization, power-on and self-test are completed, and the ph1 stage starts with the variable preset after the system self-test to complete the system functions of flight stage 1. i In chain transition, there is no need to jump to the ph0 stage, you can directly jump to the ph1 stage and start. This can not only achieve the task goal, but also bring a better application experience.

[0088] Furthermore, the method of the present application is verified by simulation. In the simulation, the simulated aircraft is tested to jump directly from flight stage 1 to flight stage 6, and from flight stage 6 to flight stage 10, and the test results shown in Table 1 are obtained.

[0089] Table 1 Summary of test results

[0090]

[0091] As can be seen from Table 1, when the trainee sets the jump command of the flight phase, the aircraft can quickly jump from the current flight phase to the target flight phase and continue to fly as planned. In the case of no oscillation of the state variables, the entire process takes less than 200ms. If the state variables oscillate, the state is basically stable after 3-5 seconds, and the state transition after the flight phase jump is achieved. The test results show that all parameters of the aircraft can complete the state transition within 200ms, and the aircraft as a whole can be stabilized within 6 seconds, meeting the transition requirements. Compared with flight simulation without stage jump, the efficiency of flight simulation is greatly improved and the needs of classroom experimental training are met.

[0092] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

[0093] To summarize, the method of the present 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; the modeling workload is greatly reduced, the model is simple and stable, and supports the stable transition of the state of the research object, with the advantages of low research and application cost and high stability.

Claims

1. A method for deriving state transition in time-varying system simulation, characterized in that: The following steps are involved: Step 1: Build a pipeline model and establish a mapping relationship between the position and state of the research object through the pipeline model; Step 2: Realize the state prediction of the research object according to the mapping relationship of the pipeline model, and realize the state transition according to the state prediction.

2. The state transition method in the simulation of a time-varying system according to claim 1, characterized in that: The process of constructing the pipeline model is: dividing the position of the pipeline into multiple stages; and establishing a unary function of the position and state on the pipeline.

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

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

5. The state transition method in the simulation of a time-varying system according to claim 4, characterized in that: The unary function is: S x =h(s x )=f(x)(0≤x≤L); (1) Among them, S x =h(s x ) is a function of the state variable set, which is expressed as the state of the research object at position x; s x ={s1(x),s2(x),s3(x),...,sN(x)} is the set of state variables that characterize 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 assembly line; L represents the total length of the assembly line.

6. The state transition method in the simulation of a time-varying system according to claim 5, characterized in that: The pipeline behavior includes necessary behavior and interfering behavior; the necessary behavior includes pipeline procedures and fault disturbances; the interfering behavior is used to cause state transition of the research object.

7. The state transition method in the simulation of a time-varying system according to claim 6, characterized in that: In step 2, the process of achieving state transition is: sorting the necessary behaviors between adjacent feature points; obtaining a complete set of initial state variables in the initial stage, and transferring 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 chain transitions from the starting point of the assembly line according to the sorted necessary behaviors until it reaches the target state.

8. The state transition method in the simulation of a time-varying system according to claim 7, characterized in that: The set of feature points is expressed as: P={p1,p2,...p m }; (2) Here, m is the number of stages.

9. The state transition method in the simulation of a time-varying system according to claim 8, characterized in that: The stages are represented as: ph i ,(1≤i≤m)。 (3)

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