Simulation system and method for low-altitude flight, electronic device and storage medium

Abstract

This invention provides a simulation system, method, electronic device, and storage medium for low-altitude flight, belonging to the field of computer simulation technology. The system includes a task flow engine, a behavior rule engine, and a physics calculation engine. The task flow engine is used to acquire the task execution logic sequence of the target simulation task to determine the task instructions for each simulation step. The behavior rule engine is used to determine decision results based on the task instructions and / or the current physical state data of each simulation entity, as well as target rules, wherein the decision results include task adjustment requests and / or control instructions. The physics calculation engine is used to determine new physical state data for each simulation entity based on the control instructions when the decision results include control instructions. The task flow engine is also used to determine another task instruction based on the task adjustment request when the decision results include a task adjustment request. Using this invention can improve the fidelity of simulation results.

Description

Technical Field

[0001] This invention relates to the field of computer simulation technology, and in particular to a simulation system, method, electronic device, and storage medium for low-altitude flight. Background Technology

[0002] With the rapid development of the low-altitude economy, the operating scenarios of aircraft such as drones and eVTOLs are becoming increasingly complex, placing higher demands on the realism and dynamic response capabilities of low-altitude flight simulation systems.

[0003] Current mainstream low-altitude flight simulation systems generally suffer from a lack of simulation dimension. Most systems focus only on simulation of a single domain, such as either performing only aircraft flight mechanics calculations or only conducting discrete event-driven task logic simulations. Such systems have rigid architectures and limited functions, and can only complete simple logical judgments based on preset conditions. This results in simulation results that deviate significantly from real-world operating scenarios, making it difficult to meet the simulation requirements of today's complex low-altitude airspace environments.

[0004] Furthermore, in some existing layered simulation architectures, information flow is mostly unidirectional or has weak feedback, making it difficult for simulation output results to have a reverse effect on the entire simulation process. This makes it difficult for the simulation system to realistically simulate the dynamic adjustment process of the coupling between mission and execution in low-altitude flight, thus limiting the simulation fidelity. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a simulation system, method, electronic device and storage medium for low-altitude flight, which can effectively improve the response capability of low-altitude flight simulation to complex and sudden scenarios and the fidelity of simulation results.

[0006] According to one aspect of the present invention, a simulation system for low-altitude flight is provided, the system comprising a mission flow engine, a behavior rule engine, and a physics calculation engine; The task flow engine is used to obtain the task execution logic sequence of the target simulation task, which is used to simulate the flight process of at least one simulation entity in the target low-altitude flight domain; and to determine the task instructions for each simulation step based on the task execution logic sequence. The behavior rule engine is used to determine a decision result based on the task instructions and / or the current physical state data of each of the simulation entities, as well as the target rules corresponding to the current target facts matched in the rule base, wherein the decision result includes task adjustment requests and / or control instructions; The physics calculation engine is used to determine new physical state data for each of the simulation entities based on the control instructions when the decision result includes the control instructions; The task flow engine is also used to determine another task instruction based on the task adjustment request when the decision result includes the task adjustment request.

[0007] Optionally, the system may further include an operation control module, which is used for: Control the simulation process of the task flow engine, behavior rule engine, and physics calculation engine to ensure timing consistency and state synchronization among the engines.

[0008] According to another aspect of the present invention, a simulation method for low-altitude flight is provided, the method being applied to a simulation system for low-altitude flight, the system comprising a task flow engine, a behavior rule engine, and a physics calculation engine; The method includes: The task flow engine obtains the task execution logic sequence of the target simulation task, which is used to simulate the flight process of at least one simulation entity in the target low-altitude flight domain; and determines the task instructions for each simulation step based on the task execution logic sequence. The behavior rule engine determines the decision result based on the task instructions and / or the current physical state data of each simulation entity, as well as the target rules corresponding to the current target facts matched in the rule base. The decision result includes task adjustment requests and / or control instructions. When the decision result includes the control command, the physics calculation engine determines new physical state data for each of the simulation entities based on the control command. The task flow engine also determines another task instruction based on the task adjustment request when the decision result includes the task adjustment request.

[0009] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising the above-described simulation system for low-altitude flight.

[0010] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the above-described simulation method for low-altitude flight.

[0011] In this invention, a simulation system for low-altitude flight includes a hierarchical task flow engine, behavior rule engine, and physics calculation engine. The task flow engine issues task instructions based on the task execution logic sequence of the target simulation task, drives the behavior rule engine to determine control instructions, and then drives the physics calculation engine to determine new physical state data for each simulation entity, realizing the hierarchical transmission from the macroscopic task objective to the microscopic flight execution. The physical state data of the simulation entities output by the physics calculation engine can be fed back to the behavior rule engine to generate task adjustment requests, which in turn correct the task execution logic of the upper-level task flow engine and determine another task instruction, thereby realizing the dynamic adjustment of the bottom-up physical execution state to the top-level task scheduling.

[0012] This hierarchical modeling and two-way closed-loop collaborative mechanism breaks through the limitations of traditional simulation's one-way execution and single dimension, achieving dynamic adaptation of strategic, tactical, and physical execution, and effectively improving the response capability and simulation result fidelity of low-altitude flight simulation to complex and sudden scenarios. Attached Figure Description

[0013] Further details, features, and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 A schematic diagram of a simulation system for low-altitude flight provided according to an exemplary embodiment of the present invention is shown; Figure 2 A schematic diagram of a task flow engine architecture provided according to an exemplary embodiment of the present invention is shown; Figure 3 A schematic diagram of a behavior rule engine architecture provided according to an exemplary embodiment of the present invention is shown; Figure 4 A schematic diagram of a physical computing engine architecture provided according to an exemplary embodiment of the present invention is shown; Figure 5 This diagram illustrates a low-altitude flight simulation service process according to an exemplary embodiment of the present invention. Figure 6 A flowchart of a simulation method for low-altitude flight according to an exemplary embodiment of the present invention is shown; Figure 7 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present invention is shown. Detailed Implementation

[0014] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0015] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0016] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0019] To address the shortcomings of existing low-altitude flight simulation systems, such as limited dimensions, rigid rules, lack of feedback loops, and insufficient dynamic adaptability, this invention provides a simulation system for low-altitude flight, such as... Figure 1 The diagram shows a simulation system for low-altitude flight, comprising a mission flow engine, a behavior rule engine, and a physics calculation engine. Based on a layered decoupling and collaborative simulation design concept, the low-altitude operation system is decomposed into three modeling dimensions: strategic, tactical, and physical. Layered simulation is achieved through the division of labor among the mission flow engine, behavior rule engine, and physics calculation engine. Standardized data interaction, state synchronization, and cross-engine communication are realized through a data bus and event distribution mechanism. Following a collaborative logic of top-down driving and bottom-up feedback, a full-link closed-loop simulation architecture is constructed to achieve high-fidelity and extrapolable simulation of complex low-altitude dynamic scenarios.

[0020] First, we will introduce the basic architecture of each engine.

[0021] 1. Task Flow Engine like Figure 2 The diagram shown illustrates the architecture of a task flow engine, which may include a task parser, a timing scheduler, and a state manager.

[0022] As the strategic layer, the task flow engine is responsible for parsing and executing macro-level task flows, supporting complex logic such as task parallelism, conditional branching, and loop control, and managing the macro-level rhythm of simulation operation.

[0023] The task parser is responsible for parsing user input or predefined simulation task scripts, breaking them down into a sequence of executable subtasks. This enables structured description and semantic understanding of tasks, providing a foundation for subsequent scheduling and execution.

[0024] The timing scheduler controls the execution order and timing of tasks based on task dependencies and the simulation clock. It supports complex logic such as parallelism, branching, and looping to ensure the timing rationality and efficiency of task execution.

[0025] The status manager tracks and manages task execution status changes in real time, including task start, progress, pause, completion, or exception. It provides visualization and query interfaces for task status and supports dynamic adjustment and recovery of task flow.

[0026] 2. Behavior Rule Engine like Figure 3 The diagram shown illustrates the architecture of a behavior rule engine, which may include a rule definition layer, a rule base, a fact base, an inference engine, and a decision-maker.

[0027] As a tactical layer, the behavior rule engine drives the intelligent decision-making and autonomous behavior of simulated entities based on preset rules and real-time situation, and supports hot loading and dynamic updating of rules.

[0028] The rule definition layer provides an environment for defining and editing rules, including a rule editor, syntax checker, template library, and rule language definition tools. It supports multiple rule expression methods (natural language templates, domain-specific languages, decision tables, and decision trees); provides editing assistance functions such as syntax highlighting, auto-completion, and real-time validation; and supports rule templates, lowering the barrier to rule writing.

[0029] The rule base stores pre-defined behavioral rules and decision-making logic, including flight rules, obstacle avoidance strategies, and emergency responses. It provides the inference engine with an executable set of rules, supporting dynamic loading and updating of rules.

[0030] The fact base stores real-time situational data from the current simulation, including entity states, environmental parameters, and task progress. It provides contextual information for rule-based reasoning, reflecting the current operational state of the system.

[0031] The inference engine performs logical reasoning based on a rule base and a fact base to determine the behavioral rules that should be triggered under the current conditions. This automates intelligent decision-making and supports multi-rule collaborative reasoning under complex conditions.

[0032] The decision-maker generates specific control or behavioral instructions based on the reasoning results and sends them to the physical computing engine for execution. This transforms abstract rules into concrete operations, achieving a closed loop from decision-making to execution.

[0033] 3. Physics Calculation Engine like Figure 4 The diagram shows the architecture of the physics computing engine, which includes a dynamics solver, a sensor simulator, and an environmental effects simulator.

[0034] The physics computing engine, as the physics layer, is responsible for solving the six-degree-of-freedom dynamics of the aircraft, simulating the physical characteristics of sensors, and calculating the physical effects of the environment, ensuring the physical authenticity of the simulation results.

[0035] The dynamics solver performs numerical solutions to the six-degree-of-freedom dynamic equations of the aircraft, simulating its physical states such as flight attitude, position, and velocity. It provides high-precision physical motion simulation, ensuring that the simulation results are consistent with real physical behavior.

[0036] The sensor simulator simulates the physical characteristics and output data of various sensors (such as navigation and positioning, inertial measurement units, cameras, and radar). It provides simulated perceptual input for the behavior rule engine and supports the simulation of sensor noise, latency, and faults.

[0037] Environmental effects simulators calculate the physical interactions between aircraft and the simulated environment (such as wind fields and temperature). This realizes the realistic impact of environmental factors on aircraft behavior and improves the environmental fidelity of the simulation.

[0038] The following will refer to Figure 1 The simulation system shown and Figure 5 The diagram shown illustrates the low-altitude flight simulation workflow, and introduces the low-altitude flight simulation workflow based on this simulation system.

[0039] During low-altitude flight simulation: The task flow engine is used to obtain the task execution logic sequence of the target simulation task, which is used to simulate the flight process of at least one simulation entity in the target low-altitude flight domain; based on the task execution logic sequence, the task instructions for each simulation step are determined. The behavior rule engine is used to determine the decision result based on the task instructions and / or the current physical state data of each simulation entity, as well as the target rules that are matched with the current target facts in the rule base. The target rules are matched with the current simulation situation, and the decision result includes task adjustment requests and / or control instructions. The physics calculation engine is used to determine new physical state data for each simulation entity based on the control commands when the decision results include control commands. The task flow engine is also used to determine another task instruction based on the task adjustment request when the decision result includes the task adjustment request.

[0040] In one possible implementation, the task flow engine first acquires and parses the target simulation task to form a task execution logic sequence consisting of multiple sub-tasks. This sequence is used to characterize the task execution route of at least one simulation entity in the target low-altitude flight domain (such as from takeoff, cruise to landing).

[0041] During the simulation process, the task flow engine traverses and executes the task execution logic sequence step by step according to a unified simulation step rhythm. Based on the task stage, execution conditions and preset logic corresponding to the current simulation step, it automatically generates and outputs the task instructions required for the current step to drive subsequent simulation stages to carry out decision-making and physical execution.

[0042] Optionally, when generating the task execution logic sequence, the task flow engine is used for: Obtain the low-altitude flight plan of at least one simulated entity submitted by the user in the target low-altitude flight domain; Based on the low-altitude flight plan, generate the task execution logic sequence of the target simulation task; The task execution logic sequence is represented by a directed acyclic graph, which includes multiple task execution sub-logic, each corresponding to a simulated step flight process.

[0043] In one possible implementation, the user first completes the scenario configuration and flight parameter settings, and inputs a low-altitude flight plan. The simulation system performs a unified legality check on the user-configured scenario parameters, flight parameters, and low-altitude flight plan; if the check fails, the system prompts the user to correct and reconfigure the parameters, iterating the check until all parameters are legal and compliant, and then initializes the simulation environment and the low-altitude flight plan.

[0044] The mission flow engine receives and parses the low-altitude flight plan input by the user. The flight plan contains constraint information for the entire low-altitude flight process, such as the take-off point, landing point, waypoints, flight sequence, mission phase division, no-fly zone avoidance constraints, mission priority, etc. of the simulated entity.

[0045] The mission flow engine structurally decomposes the low-altitude flight plan, dividing the complete flight process into multiple continuous and ordered sub-mission units, such as takeoff, climb, cruise, maneuvering, hovering, descent, and landing. Each sub-mission unit is modeled and represented based on the logical structure of a directed acyclic graph (DAG). Directed edges represent the execution order, triggering conditions, and transition relationships between sub-missions, and the acyclic property ensures that the mission flow will not experience circular deadlocks.

[0046] Each subtask unit generates a corresponding task execution sub-logic, and all task execution sub-logics are combined according to flight timing and constraint relationships to form a complete task execution logic sequence. Simultaneously, according to the unified simulation step duration of the simulation system, each task execution sub-logic is divided into time slices, ensuring that each task execution sub-logic strictly corresponds to the flight behavior and execution content within a single simulation step. This achieves the transformation of the macro-level flight plan into a simulation task logic that can be scheduled stepwise and extrapolated sequentially, providing a structured basis for issuing corresponding task instructions in each subsequent simulation step.

[0047] Optionally, when determining task instructions, the task flow engine is used for: At the end of the current simulation step, in accordance with the normal execution order indicated by the directed acyclic graph, the first task execution sub-logic corresponding to the next simulation step is determined in the task execution logic sequence, so as to determine the task instruction corresponding to the first task execution sub-logic. In response to a task adjustment request, deviating from the normal execution order, a second task execution sub-logic that matches the task adjustment request is determined in the task execution logic sequence, so as to determine the task instruction corresponding to the second task execution sub-logic.

[0048] In one possible implementation, the task flow engine distinguishes between two scenarios when generating task instructions: normal process progression and dynamic task adjustment.

[0049] In the first scenario of normal process progression, the simulation system continuously advances according to a preset simulation step cycle. After the current simulation step is completed and the physical state and simulation situation of the current simulated entity are updated, the task flow engine reads the predefined normal execution sequence link in the directed acyclic graph. Starting from the currently executing task execution sub-logic node, it traverses forward along the directed edges to match and determine the first task execution sub-logic corresponding to the next simulation step. The task flow engine parses the flight behavior and execution content (such as flight phase, route constraints, behavioral requirements, etc.) within a single simulation step corresponding to the first task execution sub-logic, and generates the corresponding simulation step's task instructions. This achieves top-down driving from the macro-task level to the micro-execution level, driving the low-altitude flight simulation to proceed in an orderly manner according to the predetermined flight plan.

[0050] In the second scenario of dynamic task adjustment, when the behavior rule engine generates a task adjustment request due to dynamic scenarios (such as airspace conflicts, aircraft malfunctions, air traffic control instructions, etc.) and pushes it to the task flow engine via the event dispatcher, the task flow engine triggers abnormal scheduling logic. At this time, it can deviate from the normal execution sequence of the directed acyclic graph and, based on the adjustment type, target stage, or constraints carried in the task adjustment request, match and determine a second task execution sub-logic that is suitable for the current emergency or change requirements among the various task execution sub-logics in the task execution logic sequence. Furthermore, the task flow engine can generate adjusted task instructions based on this second task execution sub-logic, realizing jumps, interruptions, returns, or maneuver changes in low-altitude flight simulation, that is, realizing bottom-up feedback correction from the micro-execution level to the macro-task level, and completing adaptive task scheduling in dynamic scenarios.

[0051] As a concrete example, taking the "takeoff → climb → cruise → landing" mission as an example, the directed acyclic graph contains nodes A (takeoff), B (climb), C (cruise), and D (landing), with directed edges A→B→C→D. During normal execution, the mission flow engine sequentially issues mission instructions for each stage. During the climb phase, when the behavior rule engine generates a mission adjustment request to "request early landing at an alternate landing point" due to detecting an airspace conflict, the mission flow engine, based on this request, directly jumps the current execution node from B to D and issues the mission instruction to "land at the alternate landing point," achieving dynamic adjustment at runtime.

[0052] Furthermore, after receiving the task instructions from the task flow engine, the behavior rule engine obtains the physical state data of each simulation entity in real time, and combines it with the simulation situation under the current low-altitude flight scenario to match and call the corresponding target rules.

[0053] During the simulation process, the behavior rule engine follows a unified simulation pace, using the current mission instructions as macro-constraints and real-time physical state data as the basis for execution. It calls the matched target rules to conduct logical reasoning, comprehensively judges the flight operation risks and execution feasibility, and generates decision results that include mission adjustment requests and / or control instructions. It can both feed back mission change requirements to the upper-level mission process engine and issue control instructions to the lower-level physical calculation engine, realizing the connection and control of macro-level missions and physical execution at the tactical level.

[0054] Optionally, when matching target rules to determine decision outcomes, the behavior rule engine is used for: In response to the update of the target fact, the target rule corresponding to the current target fact is matched in the rule base. The target fact includes any one or more corresponding fact objects from the task instruction, the current physical state data of each simulation entity, and the current simulation scene data. Based on the current objective facts and objective rules, determine the decision outcome.

[0055] In one possible implementation, the working memory of the behavior rule engine can continuously maintain and update a fact base. This fact base stores multiple fact objects, which are various events that trigger rules. These include task instructions triggered by the task flow engine, physical state data of each simulated entity fed back by the physics calculation engine, and fact objects corresponding to simulation scene data (such as meteorological data, radar trajectories, and airspace states). These fact objects can serve as real-time input for rule matching, providing dynamic and effective factual support for rule reasoning.

[0056] In response to fact updates in the fact base, the behavioral rule engine can perform rule matching. During rule matching, the inference engine of the behavioral rule engine can perform pattern matching based on the RETE algorithm (Network Pattern Matching Algorithm), using the current target fact as input to quickly filter target rules that match the target fact.

[0057] Whenever a target rule is selected that matches the target fact, the behavior rule engine can execute the matched target rule based on a decision context that includes task instructions, the physical state data of each simulated entity, and simulation scene data. It then performs decision reasoning by comprehensively considering task intent, the physical state of the simulated entities, and scene constraints to generate the corresponding decision result. Because the decision-making process is based on task instructions and the physical state data of the simulated entities, while also incorporating factual information from the entire scene, such as the environment, airspace, and the operating status of the simulation system, the decision logic can fully align with the operational logic and constraints of real low-altitude flight, thereby effectively improving the scene adaptability and simulation accuracy of the decision results.

[0058] When the behavior rule engine generates a decision result that requires task adjustment, this decision result will be fed back to the task flow engine as a task adjustment request. Upon receiving the request, the task flow engine will update the task objective according to the adjustment logic and generate new task instructions, triggering the update of the objective facts. After the new objective facts are injected into the fact base, a new round of decision processing will be triggered, realizing closed-loop iteration of the simulation task and dynamic adaptation of rules.

[0059] When the behavior rule engine generates decision results representing the physical control of the simulated entity, these decision results are sent to the physics calculation engine as control commands. The physics calculation engine receives the control commands issued by the behavior rule engine, which are used to constrain the actions and behaviors of the simulated entity during the low-altitude flight simulation process.

[0060] During the simulation process, the physics calculation engine follows a unified simulation step rhythm, using control commands as the basis for execution, to map and update the flight physics state corresponding to each simulation entity, determine the new physics state data of each simulation entity under the current simulation step, and feed the updated physics state data back up to provide state support for the upper-level behavior rule engine to carry out subsequent decisions.

[0061] Optionally, when determining new physical state data, the physics calculation engine is used for: Based on control commands and current simulation scene data, the six degrees of freedom attitude, flight position and sensor data of each simulation entity are calculated to generate new physical state data for each simulation entity.

[0062] In one possible implementation, the physics computing engine acquires control commands issued by the behavior rule engine and current simulation scene data (such as meteorological data, radar trajectories, airspace status, etc.) in real time.

[0063] During the simulation process, the physics calculation engine follows a unified simulation stepping rhythm, combines the flight behavior constraints corresponding to the control commands, and relies on the six-degree-of-freedom dynamic model to calculate the six-degree-of-freedom attitude, flight position, and sensor data (such as GPS (Global Positioning System) data, IMU (Inertial Measurement Unit) data, etc.) of each simulated entity, quantifies and updates the motion state and sensor output information of the simulated entity, and finally generates new physical state data for each simulated entity under the current simulation step.

[0064] This invention achieves refined simulation at each level of the domain through hierarchical modeling: the task flow engine focuses on process rationality, the behavior rule engine focuses on decision intelligence, and the physics calculation engine focuses on dynamic realism. Furthermore, new models, algorithms, and rules can be integrated into the corresponding engines as plug-ins, supporting continuous system evolution and capability expansion, and solving scalability issues through a modular architecture.

[0065] Based on this, a closed-loop collaborative simulation mechanism with top-down drive and bottom-up feedback is constructed to achieve high-fidelity dynamic simulation of the entire low-altitude flight process. The top-down drive refers to the task flow engine issuing task commands based on the reasonable process planning of the task layer, driving the behavior rule engine to conduct tactical intelligent decision-making and the physics calculation engine to complete the execution of real flight maneuvers, thus realizing the hierarchical transmission of macro-level task objectives to micro-level flight execution. The bottom-up feedback refers to the physics calculation engine outputting the physical state data of the simulated entities at the physical layer, which, combined with tactical decision-making requirements, generates task adjustment requests by the behavior rule engine, thus correcting the task execution logic of the upper-level task flow engine and achieving dynamic adjustment of the lower-level physical execution state to the upper-level task scheduling.

[0066] This hierarchical modeling and two-way closed-loop collaborative mechanism breaks through the limitations of traditional simulation's one-way execution and single dimension, achieving dynamic adaptation of strategic, tactical, and physical execution, and effectively improving the response capability and simulation result fidelity of low-altitude flight simulation to complex and sudden scenarios.

[0067] Optionally, in order to achieve loose coupling and standardized communication between engines, the simulation system may also include an event dispatcher, which is used to distribute simulation events subscribed to by each engine to the corresponding engine.

[0068] Among them, the simulation events subscribed to by the task flow engine include task adjustment request events triggered by the behavior rule engine; The simulation events subscribed to by the behavior rule engine include simulation scene data update events, task instruction events triggered by the task flow engine, and physical state data update events triggered by the physics calculation engine, so that the behavior rule engine can update the corresponding target facts based on the simulation events it obtains. The simulation events subscribed to by the physics calculation engine include control instruction events triggered by the behavior rules engine.

[0069] In one possible implementation, to achieve loosely coupled communication between engines, the simulation system builds a communication system based on a unified message bus and standardized interface specifications. Data interaction and service calls are completed through event dispatchers, data adapters, and service gateways, ensuring that each engine evolves independently while maintaining the overall consistency of the system.

[0070] Each engine generates corresponding simulation events in real time based on the running status of the simulation steps. Specifically, the task flow engine triggers a task instruction event when it issues a task instruction; the behavior rule engine triggers a task adjustment request event and a control instruction event when it generates a task adjustment request or control instruction; the physics calculation engine triggers a physics state data update event after updating the physics state data; and changes in the external scene synchronously trigger simulation scene data update events.

[0071] The event dispatcher continuously listens to the message bus, capturing all simulation events published by various engines and external systems in real time. Based on preset engine subscription relationships, it accurately distributes events through standardized interfaces. Specifically, it can dispatch task adjustment request events triggered by the behavior rule engine to the task flow engine, enabling the task flow engine to receive and respond to lower-level task change requests in real time; it can dispatch simulation scene data update events, task instruction events triggered by the task flow engine, and physical state data update events triggered by the physics calculation engine to the behavior rule engine, enabling the behavior rule engine to update the corresponding target facts in real time and provide real-time and accurate basis for dynamic decision-making; and it can dispatch control instruction events triggered by the behavior rule engine to the physics calculation engine, enabling the physics calculation engine to receive control instructions and execute physical state updates in a timely manner.

[0072] Through this subscription-distribution communication mechanism, each engine receives the event data it needs, realizing loosely coupled and asynchronous collaborative interaction between engines. At the same time, it is compatible with external system data access, ensuring the overall simulation operation is stable and scalable.

[0073] Optional, such as Figure 5 As shown, the simulation system may also include a spatial mesh engine, which is used for: During the low-altitude flight simulation, spatial conflict detection is performed on each simulation entity, the conflict detection result of each simulation entity is determined, and the simulation entities with spatial conflicts are designated as entities to be resolved. The conflict detection results of the entity to be resolved are sent to the behavior rule engine, so that the behavior rule engine can generate corresponding control instructions based on the conflict detection results of the entity to be resolved, so as to realize the spatial conflict resolution.

[0074] In one possible implementation, the airspace grid computing engine is built on the BeiDou grid coding system and incorporates a grid indexer, a spatial relationship calculator, and a conflict detector. This provides a unified spatial computing benchmark for spatially related rules such as conflict identification and resolution rules and geofencing access permission rules within the entire system and the behavior rule engine. Within each simulation step of the low-altitude flight simulation, the airspace grid engine receives updated physical state data from each simulation entity in real time. It uses the grid indexer to perform airspace spatial index matching, calculates the spatial positional relationships of each simulation entity using the spatial relationship calculator, and performs full-domain spatial conflict detection on each simulation entity to determine whether there are spatial conflicts such as route overlap or distance exceeding limits. The engine obtains the conflict detection results for each simulation entity and marks simulation entities with spatial conflicts as entities to be resolved.

[0075] After completing conflict detection, the spatial mesh engine pushes the conflict detection results, including the conflict location, conflict type, and spatial constraints of the entity to be resolved, to the behavior rule engine via the event dispatcher. Then, the behavior rule engine updates the corresponding target facts based on the conflict detection results, matches the conflict resolution target rules, performs spatial logic deduction based on the unified spatial benchmark provided by the spatial mesh engine, completes conflict resolution decisions and / or path replanning, generates control instructions for conflict avoidance, and sends them to the physics calculation engine for execution, thus achieving spatial conflict resolution of the simulated entity.

[0076] It should be noted that, in addition to the BeiDou grid coding system, the spatial grid computing engine can also be built based on GeoSOT (Earth Space Grid) or other types of global discrete grid systems. This embodiment does not limit the specific grid coding system.

[0077] Further optional, the spatial grid engine is also used for: The low-altitude flight airspace is divided into grids and modeled to establish airspace grid cells for the low-altitude flight airspace. The spatial computation of the task flow engine, behavior rule engine, and physics calculation engine is uniformly mapped to the airspace grid cell, so that the task flow engine, behavior rule engine, and physics calculation engine can perform low-altitude flight simulation based on the same grid coordinate system.

[0078] In one possible implementation, the airspace grid computing engine performs global grid subdivision modeling of the target low-altitude flight airspace, discretizing the continuous three-dimensional low-altitude space into standardized and structured airspace grid units, and constructing a unified grid space coordinate system.

[0079] To address the problems in traditional low-altitude simulations where different models (environment, entities, and airspace) use their own independent coordinate systems and lack a unified time reference, leading to difficulties in data fusion and unreliable spatial calculation results such as conflict detection, the airspace grid engine uses standardized interfaces to uniformly map various spatial data such as spatial location, flight path planning, and situation calculation from the task flow engine, behavior rule engine, and physics calculation engine to the grid coordinate system corresponding to the aforementioned airspace grid cells. At the same time, relying on the operation control module, it ensures that the entire system moves synchronously under a unified simulation clock, completing the spatiotemporal reference alignment.

[0080] Therefore, the task flow engine, behavior rule engine, and physical calculation engine can all carry out low-altitude flight simulation operations such as route planning, conflict identification, and state calculation based on the same set of airspace grid cells and grid coordinate system. This provides a unified benchmark for all space-related calculations in the entire system, avoids calculation errors caused by coordinate transformation and accuracy differences between multiple engines, ensures consistency and repeatability of all spatial calculation processes in the entire system, effectively solves the problems of spatiotemporal inconsistency and calculation conflicts, and improves the reliability of low-altitude flight simulation spatial calculation results.

[0081] Optionally, the simulation system may also include a runtime control module, which is used for: Control the simulation process of the task flow engine, behavior rule engine, and physics calculation engine to ensure timing consistency and state synchronization among the engines.

[0082] In one possible implementation, the runtime control module establishes a unified simulation clock and step scheduling mechanism, serving as the central hub for the simulation timing of the entire system, and coordinating and managing the overall simulation process of the task flow engine, behavior rule engine, and physical calculation engine.

[0083] During low-altitude flight simulation, the operation control module can synchronously issue timing scheduling commands to each engine according to a preset unified simulation step rhythm, strictly control the start, pause, propulsion and termination sequence of each engine, and ensure that the task flow issuance, behavior rule decision-making and physical state calculation are executed in an orderly manner within the same simulation time sequence; at the same time, it collects the operating status and data output of each engine in real time, verifies and synchronizes the status information of each engine, and corrects anomalies such as timing deviation and status lag in a timely manner.

[0084] Through the aforementioned timing control and state synchronization methods, the operation control module ensures that the task flow engine, behavior rule engine, and physical calculation engine are strictly aligned in simulation timing and synchronized in real time, avoiding timing errors and data asynchrony caused by the independent operation of each engine, and providing a stable and reliable timing operation foundation for multi-engine closed-loop collaborative simulation.

[0085] In addition, refer to Figure 5 The diagram illustrates the low-altitude flight simulation workflow. After the airspace grid computing engine completes spatial conflict detection, if it determines that there are no spatial conflicts in the current simulation, it proceeds to the full-domain anomaly detection stage. This anomaly detection is carried out across the entire simulation chain, covering various simulation anomalies such as task flow anomalies, behavioral decision-making anomalies, physical operation anomalies, airspace operation anomalies, and environmental parameter anomalies.

[0086] If an anomaly detection determines that a simulation anomaly exists, the anomaly handling procedure is initiated to record, handle, and respond to the anomaly event, and the anomaly status notification is fed back to the upper layer to intervene in the main simulation process loop to complete the anomaly intervention; if no simulation anomaly is detected, simulation data recording and airspace situation visualization are executed directly.

[0087] The system then determines whether the current simulation has met the preset termination conditions: if the termination conditions have not been met, it enters the next time step cycle and continues to carry out the full-process simulation; if the termination conditions have been met, it summarizes the full-cycle simulation operation data, conflict handling records, anomaly handling information, etc., and generates the final low-altitude flight simulation evaluation report to complete this simulation task.

[0088] This embodiment can achieve the following beneficial effects: This invention provides a simulation system for low-altitude flight, comprising a hierarchical task flow engine, behavior rule engine, and physics calculation engine. The task flow engine issues task instructions based on the task execution logic sequence of the target simulation task, driving the behavior rule engine to determine control instructions, which in turn drives the physics calculation engine to determine new physical state data for each simulation entity, achieving a top-down, hierarchical transmission from the macroscopic task objective to the microscopic flight execution. The physical state data of the simulation entities output by the physics calculation engine can be fed back to the behavior rule engine to generate task adjustment requests, which in turn correct the task execution logic of the upper-level task flow engine, determining another task instruction. This enables dynamic adjustment of the bottom-up physical execution state to the top-level task scheduling.

[0089] This hierarchical modeling and two-way closed-loop collaborative mechanism breaks through the limitations of traditional simulation's one-way execution and single dimension, achieving dynamic adaptation of strategic, tactical, and physical execution, and effectively improving the response capability and simulation result fidelity of low-altitude flight simulation to complex and sudden scenarios.

[0090] Based on the same inventive concept, this embodiment of the invention provides a simulation method for low-altitude flight. This method is applied to the aforementioned simulation system for low-altitude flight, which includes a task flow engine, a behavior rule engine, and a physics calculation engine. The implementation method is the same as described above, and will not be repeated in this embodiment.

[0091] Reference Figure 6 The flowchart of a simulation method for low-altitude flight is shown, which includes the following steps 601-604: Step 601: The task flow engine obtains the task execution logic sequence of the target simulation task, which is used to simulate the flight process of at least one simulation entity in the target low-altitude flight domain; based on the task execution logic sequence, it determines the task instructions for each simulation step. Step 602, the behavior rule engine determines the decision result based on the task instruction and / or the current physical state data of each simulation entity, and the target rule corresponding to the current target fact matched in the rule base, wherein the decision result includes task adjustment request and / or control instruction; Step 603: When the decision result includes the control command, the physics calculation engine determines new physical state data for each simulation entity based on the control command. Step 604: When the decision result includes the task adjustment request, the task flow engine further determines another task instruction based on the task adjustment request.

[0092] Optionally, obtaining the task execution logic sequence of the target simulation task includes: Obtain the low-altitude flight plan of at least one simulated entity submitted by the user in the target low-altitude flight domain; Based on the low-altitude flight plan, generate the task execution logic sequence for the target simulation task; The task execution logic sequence is represented by a directed acyclic graph, which includes multiple task execution sub-logic, each of which corresponds to a simulated step flight process.

[0093] Optionally, determining the task instruction for each simulation step based on the task execution logic sequence includes: at the end of the current simulation step, determining the first task execution sub-logic corresponding to the next simulation step in the task execution logic sequence according to the normal execution order indicated by the directed acyclic graph, so as to determine the task instruction corresponding to the first task execution sub-logic; The step of determining another task instruction based on the task adjustment request includes: in response to the task adjustment request, deviating from the normal execution order, determining a second task execution sub-logic that matches the task adjustment request in the task execution logic sequence, so as to determine the task instruction corresponding to the second task execution sub-logic.

[0094] Optionally, determining the decision result based on the task instructions and / or the current physical state data of each of the simulation entities, and the target rules corresponding to the current target facts matched in the rule base, includes: In response to the update of the target fact, a target rule corresponding to the current target fact is obtained by matching in the rule base. The target fact includes any one or more corresponding fact objects from the task instruction, the current physical state data of each simulation entity, and the current simulation scene data. Based on the current target facts and the target rules, the decision outcome is determined.

[0095] Optionally, determining new physical state data for each simulation entity based on the control commands includes: Based on the control commands and the current simulation scene data, the six degrees of freedom attitude, flight position and sensor data of each simulation entity are calculated to generate new physical state data for each simulation entity.

[0096] Optionally, the system may also include an event dispatcher; The method further includes: The event dispatcher distributes the simulation events subscribed to by each engine to the corresponding engine; The simulation events subscribed to by the task flow engine include the task adjustment request event triggered by the behavior rule engine. The simulation events subscribed to by the behavior rule engine include simulation scene data update events, task instruction events triggered by the task flow engine, and physical state data update events triggered by the physical calculation engine, so that the behavior rule engine updates the corresponding target facts based on the simulation events it obtains. The simulation events subscribed to by the physics computing engine include control instruction events triggered by the behavior rule engine.

[0097] Optionally, the system further includes a spatial grid engine, and the method further includes: During low-altitude flight simulation, the airspace grid engine performs spatial conflict detection on each simulated entity, determines the conflict detection result of each simulated entity, and identifies simulated entities with spatial conflicts as entities to be resolved. The conflict detection results of the entities to be resolved are sent to the behavior rule engine, so that the behavior rule engine generates corresponding control commands based on the conflict detection results of the entities to be resolved, thereby achieving spatial conflict resolution.

[0098] Optionally, the airspace mesh engine further performs mesh-based subdivision modeling of the low-altitude flight airspace, establishing airspace mesh cells for the low-altitude flight airspace; and uniformly maps the spatial calculations of the task flow engine, the behavior rule engine, and the physical calculation engine to the airspace mesh cells, so that the task flow engine, the behavior rule engine, and the physical calculation engine can perform low-altitude flight simulation based on the same mesh coordinate system.

[0099] This embodiment can achieve the following beneficial effects: This invention provides a simulation method for low-altitude flight, applied to a simulation system for low-altitude flight. The system includes a hierarchical task flow engine, behavior rule engine, and physics calculation engine. The task flow engine issues task instructions based on the task execution logic sequence of the target simulation task, driving the behavior rule engine to determine control instructions, which in turn drives the physics calculation engine to determine new physical state data for each simulation entity. This achieves a top-down, hierarchical transmission of the macroscopic task objective to the microscopic flight execution. The physical state data of the simulation entities output by the physics calculation engine can be fed back to the behavior rule engine to generate task adjustment requests, which in turn correct the task execution logic of the upper-level task flow engine, determining another task instruction. This enables dynamic adjustment of the bottom-up physical execution state to the top-level task scheduling.

[0100] This hierarchical modeling and two-way closed-loop collaborative mechanism breaks through the limitations of traditional simulation's one-way execution and single dimension, achieving dynamic adaptation of strategic, tactical, and physical execution, and effectively improving the response capability and simulation result fidelity of low-altitude flight simulation to complex and sudden scenarios.

[0101] An exemplary embodiment of the present invention also provides an electronic device equipped with a simulation system for low-altitude flight provided in the embodiments of the present invention. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform a method according to an embodiment of the present invention.

[0102] An exemplary embodiment of the present invention also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0103] An exemplary embodiment of the present invention also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a method according to an embodiment of the present invention.

[0104] refer to Figure 7 The present invention will now be described in the form of a structural block diagram of an electronic device 700 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0105] like Figure 7 As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the electronic device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0106] Multiple components in electronic device 700 are connected to I / O interface 705, including: input unit 706, output unit 707, storage unit 708, and communication unit 709. Input unit 706 can be any type of device capable of inputting information to electronic device 700. Input unit 706 can receive input digital or text information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 707 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 708 may include, but is not limited to, disk and optical disk. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth devices, Wi-Fi devices, WiMax devices, cellular communication devices, and / or the like.

[0107] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above. For example, in some embodiments, the above-described simulation method for low-altitude flight can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709. In some embodiments, the computing unit 701 can be configured to perform the above-described simulation method for low-altitude flight by any other suitable means (e.g., by means of firmware).

[0108] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0109] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0110] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0111] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0112] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0113] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

Claims

1. A simulation system for low-altitude flight, characterized in that, The system includes a task flow engine, a behavior rule engine, and a physical calculation engine; The task flow engine is used to obtain the task execution logic sequence of the target simulation task, which is used to simulate the flight process of at least one simulation entity in the target low-altitude flight domain. Based on the task execution logic sequence, the task instructions for each simulation step are determined; The behavior rule engine is used to determine a decision result based on the task instructions and / or the current physical state data of each of the simulation entities, as well as the target rules corresponding to the current target facts matched in the rule base, wherein the decision result includes task adjustment requests and / or control instructions; The physics calculation engine is used to determine new physical state data for each of the simulation entities based on the control instructions when the decision result includes the control instructions; The task flow engine is also used to determine another task instruction based on the task adjustment request when the decision result includes the task adjustment request.

2. The system according to claim 1, characterized in that, The task flow engine is used for: Obtain the low-altitude flight plan of at least one simulated entity submitted by the user in the target low-altitude flight domain; Based on the low-altitude flight plan, generate the task execution logic sequence for the target simulation task; The task execution logic sequence is represented by a directed acyclic graph, which includes multiple task execution sub-logic, each of which corresponds to a simulated step flight process.

3. The system according to claim 2, characterized in that, The task flow engine is used for: At the end of the current simulation step, according to the normal execution order indicated by the directed acyclic graph, the first task execution sub-logic corresponding to the next simulation step is determined in the task execution logic sequence, so as to determine the task instruction corresponding to the first task execution sub-logic; In response to the task adjustment request, deviating from the normal execution order, a second task execution sub-logic that matches the task adjustment request is determined in the task execution logic sequence, so as to determine the task instruction corresponding to the second task execution sub-logic.

4. The system according to claim 1, characterized in that, The behavior rule engine is used for: In response to the update of the target fact, a target rule corresponding to the current target fact is obtained by matching in the rule base. The target fact includes any one or more corresponding fact objects from the task instruction, the current physical state data of each simulation entity, and the current simulation scene data. Based on the current target facts and the target rules, the decision outcome is determined.

5. The system according to claim 4, characterized in that, The physical calculation engine is used for: Based on the control commands and the current simulation scene data, the six degrees of freedom attitude, flight position and sensor data of each simulation entity are calculated to generate new physical state data for each simulation entity.

6. The system according to claim 4, characterized in that, The system also includes an event dispatcher; The event dispatcher is used to distribute the simulation events subscribed to by each engine to the corresponding engine; The simulation events subscribed to by the task flow engine include the task adjustment request event triggered by the behavior rule engine. The simulation events subscribed to by the behavior rule engine include simulation scene data update events, task instruction events triggered by the task flow engine, and physical state data update events triggered by the physical calculation engine, so that the behavior rule engine updates the corresponding target facts based on the simulation events it obtains. The simulation events subscribed to by the physics computing engine include control instruction events triggered by the behavior rule engine.

7. The system according to claim 1, characterized in that, The system also includes a spatial grid engine, which is used for: During the low-altitude flight simulation, spatial conflict detection is performed on each of the simulation entities, the conflict detection result of each simulation entity is determined, and the simulation entities with spatial conflicts are designated as entities to be resolved. The conflict detection results of the entity to be resolved are sent to the behavior rule engine, so that the behavior rule engine can generate corresponding control instructions based on the conflict detection results of the entity to be resolved, so as to realize the spatial conflict resolution.

8. The system according to claim 7, characterized in that, The spatial grid engine is also used for: The low-altitude flight airspace is divided into grids and modeled to establish the airspace grid cells of the low-altitude flight airspace. The spatial calculations of the task flow engine, the behavior rule engine, and the physical calculation engine are uniformly mapped to the airspace grid cell, so that the task flow engine, the behavior rule engine, and the physical calculation engine can perform low-altitude flight simulation based on the same grid coordinate system.

9. A simulation method for low-altitude flight, characterized in that, The method is applied to a simulation system for low-altitude flight, which includes a mission flow engine, a behavior rule engine, and a physics calculation engine. The method includes: The task flow engine obtains the task execution logic sequence of the target simulation task, which is used to simulate the flight process of at least one simulation entity in the target low-altitude flight domain; and determines the task instructions for each simulation step based on the task execution logic sequence. The behavior rule engine determines the decision result based on the task instructions and / or the current physical state data of each simulation entity, as well as the target rules corresponding to the current target facts matched in the rule base. The decision result includes task adjustment requests and / or control instructions. When the decision result includes the control command, the physics calculation engine determines new physical state data for each of the simulation entities based on the control command. The task flow engine also determines another task instruction based on the task adjustment request when the decision result includes the task adjustment request.

10. An electronic device, characterized in that, The electronic device includes the system as described in any one of claims 1-8.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method as described in claim 9.

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