CN111611679B - Modeling method of sensor scheduling behavior tree model - Google Patents
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Abstract
The invention provides a modeling method of a sensor scheduling behavior tree model, and aims to provide a modeling method with high reliability, reusability and expansibility. The invention is realized by the following technical scheme: based on the behavior tree, describing a scheduler of a sensor scheduling process by adopting the behavior tree, dividing the condition nodes of the behavior tree into external condition nodes and sensor condition nodes, and carrying out parameterization definition; the triggering condition interfaces of the external environment model and the sensor model are standardized; the behavior nodes are expanded into sensor behavior nodes, the behavior tree model describes a sensor scheduling process, the sensor scheduling behavior tree model is traversed at each time step of sensor system simulation, a scheduled main body is a specific behavior action of a sensor, invalid, interrupted and uninitiated sensor behavior states are added according to the working characteristics of the sensor, parameterized definition is carried out on the sensor behavior nodes, and modeling of the sensor scheduling behavior tree model is completed.
Description
Technical Field
The invention relates to a sensor scheduling behavior modeling method based on a behavior tree.
Background
With the complexity of sensor types and functions, the types and processes of sensor scheduling become more and more complex, and a behavior modeling method is required to describe the sensor scheduling behavior in a standardized manner. Structured text, discrete Petri nets, and finite state machines are all methods of behavior description. The structured text is described by the behavior in the form of structured language (such as script, C language, etc.), the researchers of the sensor scheduling algorithm belong to electronic professional practitioners, and describe in the form of computer language, which has poor intuitiveness and operability, and in addition, it is difficult to express the complex relationship between the sensor scheduling, such as the parallel behavior relationship of executing the sensor cooperation while interfering and tracking. The discrete Petri network is a mathematical representation of a discrete parallel system, can describe asynchronous and concurrent behaviors, has higher abstraction degree, can only be mastered by professional technicians, is not friendly to commanders who formulate a sensor scheduling strategy, and therefore affects the application and popularization of the sensor scheduling behavior modeling method.
Finite state machines are used to represent finite states and transitions, changes, etc. that occur between these states. The method can clearly describe different states of the life cycle of the sensor system, but has the defects of low modularization degree, poor behavior reusability, one-step control and the like, and is not beneficial to monitoring, debugging and iterative development of the sensor behavior in a simulation system.
The behavior tree BT (BehaviorTrees) is a new method that can be used for system behavior modeling, and is used in more and more fields because it has strict formal semantics, convenient graphic syntax, and good hierarchical properties. Behavior tree is a formalized graphical modeling language, mainly used for system and software engineering. The behavioral tree uses well-defined symbols to explicitly represent hundreds or even thousands of natural language requirements that are commonly used to express stakeholder requirements for large-scale software integrated systems. The behavior tree is used as a formalization method with strict formal semantics for protecting system modeling and reliability analysis and calculation, and has the following advantages: the modeling method based on the behavior tree ignores the system state hidden in the modeling process by focusing on the system behavior and the relation thereof, the modeling thought accords with the natural thinking of the system design realization, models built in the system analysis and design stage can be reused, and the modeling process and the generated models are simple.
There are two methods for constructing the software behavior tree. The first case is that a software behavior tree is obtained by designing, that is, a corresponding behavior tree is designed at the same time as the software is designed, in which case the main control behavior of the software is completely identical to the behavior tree obtained by the design, and this behavior tree is a complete behavior tree. The second case is by recording the running trace of the software and deriving the behavior tree from the behavior record of the software according to some algorithm rule. The behavior tree is structurally a subtree of the design behavior tree, and is mainly used in the situation that the complete behavior tree cannot be obtained, and the more the software is used, the more fully the software is operated, the more the behavior tree obtained through the behavior tree is close to the behavior tree obtained through the design. After the software behavior tree is established, judging whether the software is operated according to the behavior tree according to a monitoring result in the software operation process, if the node which is not in the behavior tree appears in the operation process, returning the node to the normal behavior tree operation through a self-adjusting strategy, expanding the software behavior tree and adding the self-adjusting method node of the behavior node into a strategy library.
Disclosure of Invention
Aiming at the defects of a structured text, a discrete Petri network and a finite state machine modeling method, the invention provides the sensor behavior scheduling method which has high reliability, reusability and expansibility, can facilitate the multiplexing and expansion of sensor model functions in simulation system engineering, and simultaneously has a more friendly and easily understood graphic interactive operation interface, so that a user can design the sensor behavior scheduling method based on a task strategy level.
The above object of the present invention can be achieved by a modeling method of a sensor scheduling behavior tree model, which has the following features: based on the behavior tree, describing a scheduler of a sensor scheduling flow by adopting the behavior tree, managing the behavior tree based on events by the scheduler, carrying out centralized management on all behaviors needing to be updated, adding the behavior state of the sensor, dividing the condition nodes of the behavior tree into external condition nodes and sensor condition nodes, describing the conditions generated by entities or environments outside the sensor system as the external condition nodes, and carrying out parameterization definition; the conditions generated by the sensor system are described as sensor condition nodes, and parameterized definition is carried out; the reusability and expansibility of the sensor system in the iterative development process are improved by using the trigger condition interface standardization of the external environment model and the sensor model; the behavior nodes are expanded into sensor behavior nodes, the behavior tree model describes a sensor scheduling process, the sensor scheduling behavior tree model is traversed at each time step of sensor system simulation, a scheduled main body is a specific behavior action of a sensor, invalid, interrupted and uninitiated sensor behavior states are added according to the working characteristics of the sensor, parameterized definition is carried out on the sensor behavior nodes, and modeling of the sensor scheduling behavior tree model is completed.
The invention has the following characteristics and beneficial effects:
the reliability is high. The invention describes a scheduler of a sensor scheduling flow by adopting a behavior tree based on the behavior tree, the scheduler manages the behavior tree based on events, performs centralized management on all behaviors needing to be updated, increases the behavior state of the sensor, describes the sensor scheduling process by adopting a behavior tree model, traverses the sensor scheduling behavior tree model at each time step of system simulation, and adds invalid, interrupted and uninitiated sensor behavior states according to the working characteristics of the sensor, thereby more truly reflecting various conditions in the sensor scheduling process, ensuring that the whole scheduling process is more reliable, visual and easy to be understood and edited by a user.
Multiplexing and expandability. The invention divides the condition nodes of the traditional behavior tree into the external condition nodes and the sensor condition nodes, and carries out parameterization definition, so that the trigger condition interfaces of the external environment model and the sensor model are standardized, the sensor execution action and the trigger condition are modularized, and the multiplexing and the expansion of the sensor model function in the simulation system engineering can be facilitated. The simulation system engineering has better expansibility and reusability during iterative development. The reusability and expansibility of the simulation system in the iterative development process are improved by improving the standardization degree of the simulation modeling interface of the sensor; the sensor behavior scheduling can be visually modeled in an intuitively friendly descriptive manner during the simulation process,
according to the invention, based on the traditional behavior tree, the sensor scheduling flow is described by adopting the behavior tree, the condition nodes are expanded into the external condition nodes and the sensor condition nodes, the behavior nodes are expanded into the sensor behavior nodes, and the sensor behavior state is increased. The behavior tree model describes the sensor scheduling process, parameterizes and defines the behavior nodes of the sensor, standardizes the behavior interfaces of the sensor model, and improves the reusability and expansibility in the iterative development process of the system. Enabling users to design sensor behavior scheduling based on task policy level.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is a schematic diagram of a sensor scheduling flow of the present invention.
FIG. 2 is a visual illustration of a sensor scheduling behavior tree model of the present invention.
Detailed Description
See fig. 1. According to the invention, a scheduler of a sensor scheduling flow is described by adopting a behavior tree based on the behavior tree, the scheduler manages the behavior tree based on events, performs centralized management on all behaviors needing to be updated, increases the behavior state of the sensor, divides the condition nodes of the behavior tree into external condition nodes and sensor condition nodes, and describes the conditions generated by entities or environments outside a sensor system as external condition nodes and performs parameterization definition; the conditions generated by the sensor system are described as sensor condition nodes, and parameterized definition is carried out; the reusability and expansibility of the sensor system in the iterative development process are improved by using the trigger condition interface standardization of the external environment model and the sensor model; the behavior nodes are expanded into sensor behavior nodes, the behavior tree model describes a sensor scheduling process, the sensor scheduling behavior tree model is traversed at each time step of sensor system simulation, a scheduled main body is a specific behavior action of a sensor, invalid, interrupted and uninitiated sensor behavior states are added according to the working characteristics of the sensor, parameterized definition is carried out on the sensor behavior nodes, and modeling of the sensor scheduling behavior tree model is completed.
The behavior tree node consists of a composite node, a condition node, a sensor behavior node and a decoration node, wherein the composite node is responsible for logic control, the external condition node and the sensor condition node are used for describing conditions for triggering the sensor behavior, the sensor behavior node describes the behavior action of the sensor, the decoration node is used for adding additional strategies in the sensor behavior, and the expression and strategy capability of the behavior tree are enhanced, including counting, timing, circulation and other descriptions. After any node in the behavior tree is executed, the execution result must be reported to its parent node, so that the parent node determines whether the execution of the child node is within the expected range, and the success indicates that the execution of the child node is within the expected behavior, and the failure indicates that unexpected behavior is encountered.
The composite node is composed of serial nodes, parallel nodes and selection nodes as logic control nodes, and when the composite node is executed, the serial nodes execute all the child nodes sequentially. If one child node fails to return after executing, stopping the execution of the subsequent node, returning the failure to the parent node of the node, otherwise, returning success to the parent node of the node, and returning the success to the parent node of the node. When executing the parallel node, the parallel node traverses all the sub-nodes, and if one sub-node succeeds, the success is returned, and if all the sub-nodes fail, the failure is returned. When executing the selected node, it iterates to execute its own child node, if one child node is encountered to return success, then the iteration is stopped, success is returned to the parent node of the node, otherwise, all nodes return failure, then failure is returned to its own parent node.
The condition node is composed of an external condition node and a sensor condition node, wherein the external condition node describes the conditions generated by entities or environments outside the sensor system and is composed of current parameters, trigger parameters and condition judgment symbol parameters, and the predefined conditions are used for describing the execution of the sensor behavior action. The current parameter refers to a certain parameter or state generated by an entity or environment outside the sensor; the triggering parameter represents a parameter and a state which need to be met when the external condition occurs; the condition judgment symbol describes the logic relation that the external current parameter and the triggering parameter meet, the sensor condition node describes the condition generated by the sensor itself, and the sensor identifier with uniqueness is formed by the sensor identifier, the sensor current parameter, the triggering parameter which indicates that the parameter or state needs to be met when a certain condition of the sensor occurs, and the condition judgment symbol, and the symbol of the sensor which identifies the action is generated; the current parameters of the sensor refer to generating certain parameters or states such as beam pointing direction and detecting success or failure of a target; the condition judgment symbol describes the logic relation that the current parameter and the triggering parameter of the sensor meet. The condition judgment symbols include, <, >, =, and. One conditional node may have multiple conditions, connected by AND operators AND OR OR connection symbols.
The sensor identifier, the sensor behavior and the sensor behavior state of the sensor in task scheduling execution form a sensor behavior node, and the sensor identifier has uniqueness and is an identification symbol for identifying the sensor generating behavior action, such as a communication sensor, a detection sensor and the like; sensor behavior represents some behavioral action produced by the sensor, such as identifying a target, communicating with an external target, etc.; sensor behavior states represent states when a sensor produces a certain behavioral action, including success, failure, running, invalid, not started, and interrupted. Successfully indicates that the action behavior of the sensor is executed and completed successfully; failure indicates that the sensor action behavior has been performed but not successfully completed; in operation, the sensor action behavior is in the process of being executed; based on the traditional behavior state, invalid, non-starting and interruption states are newly added, wherein the invalid represents that the sensor action behavior is at the starting moment of triggering execution, and the sensor fails to execute due to some reasons; failure to start indicates that the sensor action is not triggered in one execution; an interrupt indicates that the sensor action behavior is stopped by intervention during execution, but continued operation may resume.
The software behavior tree is a description form of individual behavior paths in software, and can be understood as a description of phenomena of traversing, selecting, counting in, going out and the like of a main body on the behavior tree. Each node in the behavior tree is an operation executed by the behavior tree software, and at each time step of system simulation, the behavior tree of the sensor scheduling is traversed in sequence, and the behavior state of the sensor is recorded. The behavior flow of the sensor schedule can be designed and monitored through a visual interface.
In an alternative embodiment, a sensor scheduling behavior tree model of some sort is exemplified:
serial node 1 is a serial node 1 of a certain sensor scheduling behavior tree as a root node, and serial node 1 includes a sensor condition node 1 described as judging whether a sensor a detection target is successful, a decoration node 1 described as "ONCE", representing a child node thereof executed only ONCE, and a selection node 1, three child nodes. Wherein, ornamental node 1 comprises: the sensor behavior node 1 described as the sensor B for target recognition, a child node and a decoration node are used for adding additional strategies when the sensor behavior is scheduled, and the expression and strategy capability of the description of counting, timing, circulation and the like of the behavior tree are enhanced. The sensor behavior node 1 only runs once under the action of the decoration node 1, and when the sensor behavior node 1 is executed, the sensor behavior state of the sensor B is recorded; the selection node 1 comprises a serial node 2 and a sensor behavior node 4 described as a sensor D detection target, two child nodes.
The first child node of the serial node 2 is an external condition node 1 for judging whether the current target is an important target, and the second child node of the serial node 2 is a parallel node 1, wherein the parallel node 1 comprises a sensor behavior node 2 described as external communication of a sensor C and a sensor behavior node 3 described as tracking target of a sensor A.
In the sensor scheduling process, the serial node 1 and the sensor condition node 1 are executed to judge whether the detection target of the sensor A is successful, otherwise, the failure state is immediately returned to the father node (serial node 1), the serial node 1 returns failure, and the scheduling process is ended. If yes, executing the decoration node 1 and the sensor behavior node 1, running only once under the action of the decoration node 1, describing the sensor behavior node 1 as a sensor B to perform target identification, judging whether the sensor behavior node 1 runs successfully or not, if not, returning to the father node (decoration node 1), returning to the father node (serial node 1) by the decoration node 1, returning to the failure by the serial node 1, and ending the scheduling flow. If yes, select node 1 is executed. The selection node 1 processes its child nodes in turn, and as long as one child node is running successfully, the selection node 1 returns this state to the parent node immediately, and the rest of the nodes are no longer executing.
And then sequentially executing the serial node 2 of the child node of the selected node 1, executing the external condition node 1 of the child node of the serial node 2, judging whether the current identification target is an important target, if so, executing the parallel node 1, and if the current identification target is not the important target, returning to the father node without executing the parallel node 1. Under the condition that the current identification target is an important target, the parallel node 1 entering the serial node 2 sequentially processes all the sub-nodes below the serial node until the return state of each node is received after traversing all the sub-nodes; executing the sensor behavior node 2, communicating the sensor C to the outside, and recording the sensor behavior state of the sensor C when the sensor behavior node 2 is executed; while executing the sensor behavior node 2, executing the sensor behavior node 3, the sensor a tracking the target; when the execution of the sensor behavior node 3 is completed, the sensor behavior state of the sensor a is recorded.
After the execution of the sensor behavior node 2 and the sensor behavior node 3 is finished, judging whether the sensor behavior node 2 and the execution sensor behavior node 2 fail at the same time, if so, returning to fail by the parallel node 1; if the sensor behavior node 2 and the sensor behavior node 3 do not run successfully at the same time, the success is returned to the parent node (parallel node 1), the parallel node 1 returns to the parent node, the serial node 2 returns to the selection node 1 of the parent node, the selection node 1 returns to the parent node, and the serial node 1 returns to the parent node, at this time, the whole scheduling process is executed. And returning the state to the father node immediately when receiving the failure returned by the child node, and continuing to traverse the next child node by the serial node when the traversed child node returns success.
If the sensor behavior node 2 and the sensor behavior node 3 are simultaneously failed in operation, returning to the parallel node 1 which fails to the parent node), returning to the serial node 2 which fails to the parent node by the parallel node 1, returning to the selection node 1 which fails to the parent node by the serial node 2, entering the second child node of the selection node 1, and detecting the target by the sensor behavior node 4 and the sensor D. When the execution of the sensor behavior node 4 is completed, the sensor behavior state of the sensor D is recorded. It is determined whether the sensor behavior node 4 is operating successfully. At this time, if the sensor behavior node 4 runs successfully, the sensor behavior node returns success to the selection node 1 of the parent node, the selection node 1 returns success to the serial node 1 of the parent node, and the serial node 1 returns success, at this time, the whole scheduling process is completed. If the operation of the sensor behavior node 4 fails, the failure is returned to the father node, the node 1 is selected, the serial node 1 which fails to the father node is returned to the selected node 1, the serial node 1 fails to return, and at the moment, the whole scheduling process is finished.
And under the condition that the current identification target is not an important target, returning to the serial node 2 of the father node, returning to the father node by the serial node 2, selecting the node 1, entering the second child node of the sensor behavior node 4 represented by the selected node 1, executing the sensor behavior node 4, detecting the target by the sensor D, and recording the sensor behavior state of the sensor D when the execution of the sensor behavior node 4 is finished. Judging whether the sensor behavior node 4 runs successfully or not, if the sensor behavior node 4 runs successfully, returning success to the selection node 1 of the father node, returning success to the serial node 1 of the father node by the selection node 1, and returning success to the serial node 1, wherein the whole scheduling process is finished. If the operation of the sensor behavior node 4 fails, returning failure to the selection node 1 of the father node, returning failure to the serial node 1 of the father node by the selection node 1, and returning failure to the serial node 1, wherein the whole scheduling process is finished.
See fig. 2. The visual mode shows the monitoring condition of the execution of the sensor scheduling behavior tree model. Wherein the parameters of the sensor behavior node 1 consist of a sensor identifier, a sensor behavior and a sensor behavior state. The sensor identifier is sensor B, the sensor behavior is target recognition, and the sensor behavior state is successful.
The parameters of the sensor condition node 1 are composed of a sensor identifier, a sensor current parameter, a triggering parameter and a condition judgment symbol, wherein the sensor identifier is a sensor A, the sensor current parameter is success or failure of a detection target, the triggering parameter is success of detection, and the condition judgment symbol is=.
The selection node 1 comprises two sub-nodes, in turn a serial node 2 and a sensor behavior node 4.
The serial node 2 comprises an external condition node 1 and a parallel node 1 in sequence, and two child nodes.
The parameters of the external condition node 1 are composed of current parameters, trigger parameters and condition judgment symbol parameters. The current parameter is the target type, the trigger parameter is the important target, and the condition judgment symbol is=.
The parallel node 1 comprises two sub-nodes, in turn a sensor behavior node 2 and a sensor behavior node 3.
The parameters of the sensor behavior node 2 consist of a sensor identifier, a sensor behavior and a sensor behavior state. Wherein the sensor identifier is sensor C, the sensor behavior is external communication, and the sensor behavior state is in operation.
The parameters of the sensor behavior node 3 consist of a sensor identifier, a sensor behavior and a sensor behavior state. Wherein the sensor identifier is sensor a, the sensor behavior is a tracking target, and the sensor behavior state is in operation.
The parameters of the sensor behavior node 4 consist of a sensor identifier, a sensor behavior and a sensor behavior state. Wherein the sensor identifier is sensor D, the sensor behavior is a detection target, and the sensor behavior state is not started.
In the visualization of the sensor scheduling behavior tree model, the sensor scheduling behavior tree model sequentially executes all the child nodes by taking the serial node 1 as a root node, and the visualization parameter of the decoration node 1 is described as on, which means that the child nodes thereof are executed only Once. If the serial node 1 returns failure after encountering execution of one child node, the execution of the following node is stopped, the node is taken as a root node to represent that the secondary sensor is failed in dispatching, otherwise, the node is taken as the root node to represent that the secondary sensor is successfully dispatched. Selecting node 1 to execute own child node iteratively, stopping iteration if one child node returns success, returning success to the father node of the node, otherwise, returning failure to all nodes, and returning failure to own father node.
The serial node 2 sequentially executes all the child nodes, if one child node fails to return after executing, the execution of the subsequent node is stopped, the node returns failure to the father node, otherwise, the node returns success to the father node, and the node returns success to the father node; the parallel node 1 traverses all the child nodes, wherein one child node returns success if successful, and all the child nodes return failure.
While the foregoing is directed to the preferred embodiment of the present invention, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.
Claims (7)
1. A modeling method of a sensor scheduling behavior tree model has the following characteristics: based on the behavior tree, describing a scheduler of a sensor scheduling flow by adopting the behavior tree, managing the behavior tree based on events by the scheduler, carrying out centralized management on all behaviors needing to be updated, adding the behavior state of the sensor, dividing the condition nodes of the behavior tree into external condition nodes and sensor condition nodes, describing the conditions generated by entities or environments outside the sensor system as the external condition nodes, and carrying out parameterization definition; the conditions generated by the sensor system are described as sensor condition nodes, and parameterized definition is carried out; the reusability and expansibility of the sensor system in the iterative development process are improved by using the trigger condition interface standardization of the external environment model and the sensor model; the behavior nodes are expanded into sensor behavior nodes, a behavior tree model describes a sensor scheduling process, a sensor scheduling behavior tree model is traversed at each time step of sensor system simulation, a scheduled main body is a specific behavior action of a sensor, invalid, interrupted and uninitiated sensor behavior states are added according to the working characteristics of the sensor, parameterized definition is carried out on the sensor behavior nodes, and modeling of the sensor scheduling behavior tree model is completed;
the behavior tree node consists of a composite node, a condition node, a sensor behavior node and a decoration node, wherein the composite node is responsible for logic control, the external condition node and the sensor condition node are used for describing conditions for triggering the sensor behavior, the sensor behavior node describes the behavior action of the sensor, the decoration node is used for adding additional strategies in the sensor behavior, and the expression and strategy capability of the behavior tree are enhanced, including counting, timing and cycle description;
after any node in the behavior tree is executed, reporting an execution result to a father node of the behavior tree, so that the father node judges whether the execution of the child node is in an expected range, successfully indicates that the execution of the child node is in an expected behavior, and failure indicates that unexpected behavior is encountered;
the composite node is composed of a serial node, a parallel node and a selection node as logic control nodes, when the composite node is executed, the serial node sequentially executes all child nodes, if one child node fails to return after executing, the execution of the subsequent node is stopped, the composite node returns failure to a father node of the composite node, otherwise, the composite node returns success to the father node of the composite node; executing parallel nodes; traversing all the child nodes by the parallel nodes, wherein if one child node succeeds, the success is returned, and if all the child nodes fail, the failure is returned; when executing the selected node, it iterates to execute its own child node, if one child node is encountered to return success, the iteration is stopped, success is returned to the parent node of the node, otherwise, all nodes return failure, and failure is returned to its own parent node.
2. The modeling method of a sensor scheduling behavior tree model according to claim 1, wherein: the condition node is composed of an external condition node and a sensor condition node, wherein the external condition node describes a predefined condition which is generated by an entity or an environment outside a sensor system and is composed of a current parameter outside the sensor, a trigger parameter and a condition judgment symbol parameter, and the current parameter outside the sensor refers to a certain parameter and a state generated by the entity or the environment outside the sensor; the triggering parameter represents a parameter and a state which need to be met when the external condition occurs; the condition judgment symbol describes the logic relation that the current parameter and the triggering parameter of the sensor meet.
3. The modeling method of a sensor scheduling behavior tree model according to claim 1, wherein: the sensor condition node describes the conditions generated by the sensor, and consists of a sensor identifier, a current parameter of the sensor, a triggering parameter which indicates that the parameter or state of the sensor needs to be met when certain conditions occur, and four parameters of a condition judgment symbol.
4. The modeling method of a sensor scheduling behavior tree model according to claim 1, wherein: the sensor in task scheduling execution forms a sensor behavior node by three parameters of a sensor identifier, sensor behaviors and sensor behavior states, and the sensor identifier has uniqueness and is an identification symbol for identifying the sensor generating the behavior action.
5. The modeling method of a sensor scheduling behavior tree model according to claim 4, wherein: sensor behavior represents a certain behavioral action produced by the sensor, behavior states represent states of the sensor when producing a certain behavioral action, including success, failure, running, invalidity, non-initiation, and interruption; successfully indicates that the action behavior of the sensor is executed and completed successfully; failure indicates that the sensor action behavior has been performed; in operation, the sensor action behavior is in the process of being executed; invalidation indicates that the sensor action behavior is at the beginning of the trigger execution; failure to start indicates that the sensor action is not triggered in one execution; an interrupt indicates that the sensor action behavior is stopped by intervention during execution.
6. The modeling method of a sensor scheduling behavior tree model according to claim 1, wherein: each node in the behavior tree is an operation executed by the behavior tree software, and the sensor scheduling system is designed and monitored through a visual interface, so that the sensor scheduling behavior tree is traversed in sequence in the simulated behavior flow of each time step, and the behavior state of the sensor is recorded.
7. The modeling method of a sensor scheduling behavior tree model according to claim 1, wherein: in the sensor scheduling process, executing a serial node 1 and a sensor condition node 1, judging whether the detection of a target by a sensor A is successful, if not, immediately returning a failure state to the serial node 1 serving as a father node, returning the failure of the serial node 1, ending the scheduling process, if yes, executing a decoration node 1 and a sensor behavior node 1, only running once under the action of the decoration node 1, describing the sensor behavior node 1 as a sensor B for target identification, judging whether the operation of the sensor behavior node 1 is successful, if not, returning the decoration node 1 which fails to the father node, returning the failure of the decoration node 1 to the serial node 1 serving as the father node, and returning the failure of the serial node 1, ending the scheduling process; and then sequentially executing the serial node 2 of the child node of the selected node 1, executing the external condition node 1 of the child node of the serial node 2, judging whether the current identification target is an important target, if so, executing the parallel node 1, and if the current identification target is not the important target, returning to the father node without executing the parallel node 1.
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