Pipeline model for deducable continuous system simulation

Abstract

The invention discloses a pipeline model for deductable continuous system simulation, and belongs to the technical field of deductable continuous system simulation. The behavior layer is used for representing operation behaviors, including necessary behaviors and interference behaviors, of assembly line workers on a research object; the functional layer is used for representing the relationship between the state and 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 relation; state prediction of the research object is realized according to the mapping relation; and performing transition control according to the state prediction to realize state transition. The pipeline model has the advantages of being low in research and application cost and high in stability and used for deducable continuous system simulation.

Description

Technical Field

[0001] The present application relates to the technical field of deducible continuous system simulation, and in particular, to a pipeline model for deducible continuous 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 pipeline model that has low research and application cost and high stability and can be used for simulating continuous systems. 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 pipeline model for simulating a continuous system, which is used to predict the state of the system and realize state transition according to the state prediction, including:

[0007] Behavior layer: used to represent the operational behaviors of assembly line workers on the research object, including necessary behaviors and interfering 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 form a mapping relationship; the state prediction of the research object is realized according to the mapping relationship; and transition control is performed according to the state prediction to realize state transition.

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

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

[0014] Furthermore, the position layer includes a plurality of stages divided in pipeline order, and the starting point of each stage is a feature point.

[0015] Furthermore, the state of the characteristic point is a characteristic state; and the state of the starting point of the pipeline is an initial state.

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

[0017] S x =h(s x )=f(x)(0≤x≤L); (1)

[0018] 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.

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

[0020] P = {p 1 ,p 2 ,...p m}; (2)

[0021] Here, m is the number of stages.

[0022] Further, the stages are expressed as:

[0023] ph i, (1≤i≤m). (3)

[0024] Furthermore, the method for realizing state transition includes direct transition; the method for direct transition is: obtaining a complete set of characteristic state variables, and transferring the complete set of characteristic state variables to the system; according to the complete set of characteristic state variables, the research object directly transitions from the current state to the target state.

[0025] Furthermore, the method for realizing state transition also includes chain transition; the method of chain transition is: 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, the state transition is performed in sequence in the pipeline order until it transitions to the target state.

[0026] The beneficial effect of the present application is that it provides a pipeline model with low research and application cost and high stability for simulating continuous systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] 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.

[0028] 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.

[0029] In the attached picture:

[0030] Figure 1 is a schematic diagram of an assembly line model according to an embodiment of the present application;

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

[0032] Figure 3 It is a schematic diagram of the upper stage of the pipeline model. DETAILED DESCRIPTION

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0038] Embodiment, a pipeline model for simulating a continuous system, for predicting the state of the system and performing transition control according to the state prediction, referring to Figure 1 ,include:

[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 irrelevant to this study, 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 irrelevant to this are not concerned, such as navigation station frequency, instrument display page, and cabin air conditioning temperature. These state quantities that are irrelevant 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 pipeline; the position layer includes multiple stages divided according to the pipeline 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 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 randomly 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 starting point of the pipeline is the initial state;

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

[0044] P = {p 1 ,p 2 ,...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 mapping relationship is a unary function:

[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] The research object jumps from position x to feature point p i At, the state transitions to p i This application proposes two state transition modes: direct transition and chain transition.

[0055] The method of achieving state transition includes direct transition; the method of direct transition is: obtaining a complete set of characteristic state variables, transferring the complete set of characteristic state variables to the system; according to the complete set of characteristic state variables, the research object directly transitions from the current state to the target state. Assuming the state transfer matrix is ​​A, then In the formula, A 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

[0056]

[0057] Can satisfy Right now

[0058]

[0059] The diagonal matrix A 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:

[0060] Algorithm 1: Direct Transition

[0061] Input: Feature point p after jump i .

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

[0063]

[0064] 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.

[0065] 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 if one variable is ignored, it may bring hidden dangers to the safety and stability of the system. Therefore, this application proposes another method suitable for state transitions in complex systems based on the pipeline model, chain transitions.

[0066] The method of realizing state transition also includes chain transition; the chain transition method is: sorting the necessary behaviors between adjacent feature points; the research object returns to the initial state from the current state by direct transition, and then according to the sorted necessary behaviors, the state transition is performed in sequence according to the pipeline order 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 jumps to the starting point p of the pipeline 1 , the state of the research object is first directly transitioned to the initial state by resetting, and the initial state set is expressed as Then simulate ph in pipeline order 1All the processes in the stage, while making the assembly line run at high speed to achieve rapid state evolution, and then enter the ph 2 stage, and then simulate ph 2 Stage process... After such a cycle, the position of the research object changes from p 1 The point moves continuously to p in a short time 2 Point, p 3 Wait 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

[0067] During the state reset process, the system state directly transitions, and the state vector in,

[0068]

[0069] 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.

[0070] Sequence each process in each stage, edit the judgment and trigger of each process execution condition into a function, so that it can be completed automatically under program control, and the sequential call can be realized by p 1 The state of each feature point is realized by injecting a fault into the execution condition of a certain 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:

[0071] Algorithm 2: Chain Transition

[0072] Input: The sequence number of the feature point after the jump (equivalent to the feature point p i ).

[0073] Output: System status after the jump.

[0074]

[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, 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 fault disturbance, after implementing the fault disturbance injection procedure in a certain process, there is almost no need to do any additional work for the fault effect. Because the fault disturbance is injected into the system functional model, the fault disturbance 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, the direct transition method can be used alone. The direct transition method is simple and easy to implement. For complex systems, the chained transition method can ensure high system stability and more flexible fault injection. The direct transition and chained transition methods can be used in combination according to the characteristics of the modeling object.

[0081] 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.

[0082] To summarize, 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; 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 pipeline model for simulating a continuous system, used for predicting the state of the system and realizing state transition according to the state prediction, characterized in that: include: Behavior layer: used to represent the operational behaviors of assembly line workers on the research object, including necessary behaviors and interfering 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; The position and state of the research object form a mapping relationship; the state prediction of the research object is realized according to the mapping relationship; and transition control is performed according to the state prediction to realize state transition.

2. The pipeline model for simulating a continuous system according to claim 1, characterized in that: The necessary behaviors include assembly line procedures and fault disturbances.

3. The pipeline model for simulating a continuous system according to claim 2, characterized in that: The interference behavior is used to cause a state transition of the research object.

4. The pipeline model for simulating a continuous system according to claim 3, characterized in that: The position layer includes a plurality of stages divided in pipeline order, and the starting point of each stage is a feature point.

5. The pipeline model for simulating a continuous system according to claim 4, characterized in that: The state of the feature point is a feature state; The state at the starting point of the pipeline is the initial state.

6. The pipeline model for simulating a continuous system according to claim 5, characterized in that: The mapping relationship is a unary function: 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.

7. The pipeline model for simulating a continuous system according to claim 6, characterized in that: The set of feature points is expressed as: P={p1,p2,...p m }; (2) Here, m is the number of stages.

8. The pipeline model for simulating a deducible continuous system according to claim 7, characterized in that: The stages are represented as: ph i ,(1≤i≤m)。 (3) 9. The pipeline model for deducible continuous system simulation according to claim 8, characterized in that: The method for realizing state transition includes direct transition; the method for direct transition is: obtaining a complete set of characteristic state variables, and transferring the complete set of characteristic state variables to the system; according to the complete set of characteristic state variables, the research object directly transitions from the current state to the target state.

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

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