Model-based multi-machine collaborative action simulation and evaluation method and system

Abstract

The present disclosure relates to the technical field of computer intelligent control and visual simulation, and specifically to a model-based multi-machine collaborative action simulation and evaluation method and system. The method is based on the design concept of an open model, provides a hypothetical model editing interface, a multi-machine collaborative simulation action range selection interface, and a scenario deployment interface, and deploys action scenarios in multiple levels and multiple dimensions according to the configuration parameters input by the user, thereby improving the flexibility of scenario deployment; through the action simulation and evaluation interface, the action scenario is visually simulated, the simulation process is controlled and managed, and during the simulation process, the situation information of the action scenario is displayed, and the simulation results are summarized. When the simulation process is completed, the simulation results are evaluated and the evaluation results are displayed, so that the user can intuitively observe the simulation process and the use of the collaborative function, which is convenient for the user to conduct subsequent analysis and decision-making. This improves the intuitiveness and actionability of the simulation system and adapts to different simulation needs.

Description

Technical Field

[0001] The present disclosure relates to the technical field of computer intelligent control and visual simulation, and in particular to a model-based multi-machine collaborative action simulation and evaluation method and system. Background Art

[0002] With the rapid development of drone swarm technology, the breakthrough of a single advanced technology has gradually entered a bottleneck period. The traditional development idea of ​​"integrating all advanced technologies into a single advanced platform" can no longer support the needs of operations such as disaster relief and drills. Therefore, the development of drone swarm technology has begun to turn to a distributed collaborative execution mode to adapt to the current complex and changing geographical environment.

[0003] The distributed, unmanned collaborative execution mode distributes different functions to different dedicated platforms, such as detection drones, work drones, network relay drones, electromagnetic anti-interference drones, etc. These platforms can be flexibly dispatched according to the actual environment, with a variety of capability combinations, to achieve capability aggregation, thereby greatly improving operational effectiveness.

[0004] Although there are some action simulation systems currently, most of these systems can only provide simulation of data and conclusions, and cannot provide users with intuitive experience. Specifically, these systems have the following major defects:

[0005] 1. Lack of intuitiveness: Users cannot watch the simulation process in real time and cannot intuitively understand key information such as the multiple relationships between platforms, sensor usage, work and action timing.

[0006] 2. Single function: Most existing simulation systems only focus on data calculation and result output, lack in-depth analysis and presentation of cluster action processes, movements and collaborative effectiveness, and cannot support multi-agent collaborative action simulation.

[0007] 3. Insufficient openness: The system lacks sufficient openness and is difficult to customize and expand according to user needs, which limits its application in actual operations.

[0008] How to make up for the above defects of the simulation system in the existing technology, improve the intuitiveness and actionability of the simulation system, and enhance the flexibility and scalability of the simulation system to meet different simulation needs is an urgent problem to be solved. Summary of the invention

[0009] In order to solve the problems in the related art, the embodiments of the present disclosure provide a model-based multi-machine collaborative action simulation and evaluation method and system.

[0010] In a first aspect, the embodiments of the present disclosure provide a model-based multi-machine collaborative action simulation and evaluation method, which adaptively loads and displays a background map according to map loading configuration parameters, and provides geographic information data of the background map;

[0011] Providing a scenario model editing interface, and obtaining model configuration parameters input by a user based on the scenario model editing interface; creating a corresponding scenario model according to the model configuration parameters, or updating an already created scenario model;

[0012] A multi-machine collaborative simulation action range selection interface is provided, and based on the multi-machine collaborative simulation action range selection interface, the multi-machine collaborative simulation action range configuration parameters input by the user are obtained, and based on the multi-machine collaborative simulation action range configuration parameters, the corresponding multi-machine collaborative simulation action range background map is loaded and displayed; based on the multi-machine collaborative simulation action range background map, a multi-machine collaborative action scenario deployment interface is provided, and based on the multi-machine collaborative action scenario deployment interface, the action scenario configuration parameters input by the user are obtained, and the corresponding multi-machine collaborative action scenario is created and deployed according to the action scenario configuration parameters and the created multiple assumption models, and the description data of the multi-machine collaborative action scenario is generated; wherein the description data of the multi-machine collaborative action scenario includes: node data and node sequence data, and the multi-machine collaborative action scenario deployment interface includes an action simulation instruction menu, and based on the action simulation instruction menu, the action simulation instruction corresponding to the specified multi-machine collaborative action scenario input by the user is received, and the action simulation instruction and the description data of the specified multi-machine collaborative action scenario are sent to the action simulation and evaluation interface;

[0013] The action simulation and evaluation interface is provided, the action simulation instruction and the description data of the specified multi-machine collaborative action scenario are received based on the action simulation and evaluation interface, the specified multi-machine collaborative action scenario is visually simulated according to the action simulation instruction and the node data in the node sequence indicated by the node sequence data, and the simulation process is controlled and managed; during the simulation process, the situation information of the specified multi-machine collaborative action scenario is displayed in the action simulation and evaluation interface, and the simulation results are summarized; after the simulation process is completed, the simulation results are evaluated, and the evaluation results of the specified multi-machine collaborative action scenario are displayed in the action simulation and evaluation interface.

[0014] In a second aspect, the present disclosure provides a model-based multi-machine collaborative action simulation and evaluation system, the simulation and evaluation system comprising: a map resource construction module, a scenario model editing module, a multi-machine collaborative action scenario deployment module and an action simulation and evaluation module, wherein:

[0015] The map resource construction module is configured to: adaptively load and display the background map according to the map loading configuration parameters, and provide geographic information data of the background map;

[0016] The assumption model editing module is configured to: provide an assumption model editing interface, obtain model configuration parameters input by a user based on the assumption model editing interface; create a corresponding assumption model according to the model configuration parameters, or update the created assumption model;

[0017] The multi-machine collaborative action scenario deployment module is configured to: provide a multi-machine collaborative simulation action range selection interface, obtain the multi-machine collaborative simulation action range configuration parameters input by the user based on the multi-machine collaborative simulation action range selection interface, and load and display the corresponding multi-machine collaborative simulation action range background map based on the multi-machine collaborative simulation action range configuration parameters; provide a multi-machine collaborative action scenario deployment interface based on the multi-machine collaborative simulation action range background map, obtain the action scenario configuration parameters input by the user based on the multi-machine collaborative action scenario deployment interface, create and deploy the corresponding multi-machine collaborative action scenario according to the action scenario configuration parameters and the created multiple assumption models, and generate the description data of the multi-machine collaborative action scenario; wherein the description data of the multi-machine collaborative action scenario includes: node data and node sequence data, the multi-machine collaborative action scenario deployment interface includes an action simulation instruction menu, receives the action simulation instruction corresponding to the specified multi-machine collaborative action scenario input by the user based on the action simulation instruction menu, and sends the action simulation instruction and the description data of the specified multi-machine collaborative action scenario to the action simulation and evaluation interface;

[0018] The action simulation and evaluation module is configured to: provide the action simulation and evaluation interface, receive the action simulation instructions and the description data of the specified multi-machine collaborative action scenario based on the action simulation and evaluation interface, perform visual simulation of the specified multi-machine collaborative action scenario according to the action simulation instructions and the node data in the node sequence indicated by the node sequence data, and control and manage the simulation process; during the simulation process, display the situation information of the specified multi-machine collaborative action scenario in the action simulation and evaluation interface, and summarize the simulation results; when the simulation process is completed, evaluate the simulation results, and display the evaluation results of the specified multi-machine collaborative action scenario in the action simulation and evaluation interface.

[0019] According to the technical solution provided by the embodiment of the present disclosure, based on the design concept of open model, by providing a hypothetical model editing interface, a multi-machine collaborative simulation action range selection interface and a scenario deployment interface, the multi-machine collaborative action scenario is configured and deployed in multiple levels and dimensions according to various configuration parameters input by the user, so that the user can quickly configure and deploy the scenario according to different action requirements, thereby improving the flexibility and accuracy of scenario deployment; by providing the action simulation and evaluation interface, the specified multi-machine collaborative action scenario is visually simulated, and the simulation process is controlled and managed. During the simulation process, the situation information of the specified multi-machine collaborative action scenario is displayed, and the simulation results are summarized. When the simulation process is completed, the simulation results are evaluated, and the evaluation results of the specified multi-machine collaborative action scenario are displayed in the action simulation and evaluation interface, so that the user can observe the simulation process and the use of the collaborative function in real time and intuitively, which is convenient for the user to conduct subsequent analysis and decision-making. As a result, the intuitiveness and actionability of the simulation system are improved, and the flexibility and extensibility of the simulation system are enhanced, so as to adapt to different simulation needs.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings:

[0022] Figure 1 A flow chart of a model-based multi-machine collaborative action simulation and evaluation method according to an embodiment of the present disclosure is shown;

[0023] Figure 2 A flowchart of a method for creating and deploying a multi-machine collaborative action scenario according to an embodiment of the present disclosure is shown;

[0024] Figure 3 A schematic diagram showing a specific implementation of a multi-machine collaborative simulation action range selection interface in an action scenario according to an embodiment of the present disclosure;

[0025] Figure 4 A schematic diagram showing a specific implementation of a multi-machine collaborative action scenario deployment interface in an action scenario according to an embodiment of the present disclosure;

[0026] Figure 5 A flowchart of a method for creating and deploying a corresponding multi-machine collaborative action scenario according to the action scenario configuration parameters and a plurality of created scenario models and generating description data of the multi-machine collaborative action scenario according to an embodiment of the present disclosure is shown;

[0027] Figure 6A flowchart showing a method for respectively configuring the scenario model of the first team and the scenario model of the second team according to the action resource configuration parameters and the action task configuration parameters according to an embodiment of the present disclosure;

[0028] Figure 7 A schematic diagram showing a specific implementation of generating an action unit node according to an embodiment of the present disclosure;

[0029] Figure 8 A schematic diagram showing a specific implementation after generating multiple action unit nodes in a multi-machine collaborative action scenario deployment interface according to an embodiment of the present disclosure;

[0030] Fig. 9 A schematic diagram showing a specific implementation of generating a formation node according to an embodiment of the present disclosure is shown;

[0031] Fig.10 A schematic diagram showing a specific implementation of generating an action path node according to an embodiment of the present disclosure is shown;

[0032] Fig.11 A specific implementation schematic diagram of generating a 2D graphic style node according to an embodiment of the present disclosure is shown;

[0033] Fig.12 A flowchart of a method for performing visual simulation on the specified multi-machine collaborative action scenario according to the node sequence indicated by the node sequence data in sequence according to an embodiment of the present disclosure is shown;

[0034] Fig.13 A flow chart showing a method for controlling and managing a simulation process according to an embodiment of the present disclosure is shown;

[0035] Fig.14 A schematic diagram showing the display of evaluation results in the action simulation and evaluation interface according to an embodiment of the present disclosure;

[0036] Fig.15 A flow chart of another model-based multi-machine collaborative action simulation and evaluation method according to an embodiment of the present disclosure is shown;

[0037] Fig.16 A schematic diagram showing the structure of a model-based multi-machine collaborative action simulation and evaluation system according to an embodiment of the present disclosure is shown;

[0038] Fig.17 An operation sequence diagram of a model-based multi-machine collaborative action simulation and evaluation system according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0039] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0040] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, behaviors, components, parts, or a combination thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof exist or are added.

[0041] It should also be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] As mentioned above, most of the existing action simulation systems are simulations with only data and conclusions, which cannot provide users with intuitive experience. Once the scene is input, it is impossible to watch the simulation process in real time, and it is even more impossible to mine the multiple relationships between platforms, sensors, equipment usage timing and other information, and it is impossible to support multi-agent collaborative action simulation.

[0043] In order to solve the above problems in the prior art, the present disclosure proposes a model-based multi-machine collaborative action simulation and evaluation system for multiple aviation equipment (such as unmanned aerial vehicles and manned aircraft), where "multi-machine" refers to multiple aircraft, which can be multiple unmanned aerial vehicles, multiple manned aircraft, or multiple unmanned aerial vehicles and manned aircraft. "Multi-machine collaborative action" can be multiple aircraft collaboratively executing drill actions, or multiple aircraft collaboratively executing other types of actions, such as transportation, rescue, etc., depending on the configuration parameters of the action scenario and the user's input. Based on the functional division of specific implementation, the system consists of a map resource construction module, a hypothetical model editing module, a multi-machine collaborative action scenario deployment module, an action simulation and evaluation module, and an auxiliary tool module.

[0044] Specifically, the present disclosure adaptively loads and displays global map loading or regional high-definition maps according to map loading configuration parameters through a map resource construction module, and adopts different loading algorithms according to their characteristics when loading different maps, thereby improving the efficiency of map loading, optimizing resource usage and loading speed, and providing accurate geographic location information support for the deployment of action scenarios (such as rescue scenarios), meeting the purposefulness, accuracy and reliability of the deployment location of action units, and making the conditions for action deployment closer to reality.

[0045] In order to ensure the timeliness and compatibility of hypothetical models (such as aircraft models, automatic navigation aircraft models), based on the design concept of open models, the present invention provides a hypothetical model editing interface and a management interface through a hypothetical model editing module, sets a configurable interface for various parameters of the model, supports the creation, update, display and deletion of hypothetical models according to the parameters input by the user, enhances the user's ability to customize the model, and has the ability to modify related parameters, which is convenient for users to build and manage complex hypothetical models according to actual needs, thereby achieving unlimited possibilities for model capability configuration under the condition of limited model appearance.

[0046] The present disclosure supports users to select the multi-machine collaborative simulation action range through the multi-machine collaborative action scenario deployment module, and creates and deploys the multi-machine collaborative action scenario based on the area according to the action scenario configuration parameters input by the user. This includes forming an action area and generating action units, as well as configuring the attributes of the action units such as mounting, routes, action rules, and action tasks. In this way, users can quickly configure and deploy scenarios according to different action requirements, which improves the flexibility and accuracy of scenario deployment.

[0047] The present invention performs visual simulation of multi-machine collaborative action scenarios through action simulation and evaluation modules, controls and manages the simulation process, displays situation information during the simulation process, analyzes, summarizes and evaluates the simulation results, thereby realizing visualization and dynamic management of the simulation process, improving the accuracy and credibility of the simulation results, and allowing users to observe the simulation process and the use of collaborative functions in real time and intuitively, facilitating subsequent analysis and decision-making by users.

[0048] The present disclosure improves the openness of the simulation platform through auxiliary tool modules without affecting the simulation process and results, and provides users with better sensory and usage experience (for example: zooming in and out of the model and map scale).

[0049] In addition, in order to restore the actual behavior of the aircraft with high fidelity and make the simulation closer to the real action scene, the present invention innovatively developed a series of algorithms to simulate the flight of real aircraft, so that after the simulation begins, the drone can still complete the action mission autonomously without human intervention.

[0050] In this way, through the joint collaboration of the modules as described above, the intuitiveness and actionability of the simulation system are improved, and the flexibility and scalability of the simulation system are enhanced, thereby adapting to different simulation needs.

[0051] Figure 1 FIG. 1 is a flow chart of a model-based multi-machine collaborative action simulation and evaluation method according to an embodiment of the present disclosure. Figure 1 As shown, the simulation and evaluation method includes the following steps S110-S140:

[0052] In step S110, a background map is adaptively loaded and displayed according to the map loading configuration parameters, and geographic information data of the background map is provided.

[0053] The deployment of action scenarios requires accurate geographic location information support. In order to meet the purpose, accuracy and reliability of the deployment location of action elements, make the conditions of action deployment as close to reality as possible, and help users plan action scripts, the technical solution of the disclosed embodiment supports the loading and display of two types of maps, namely: global maps and regional high-definition maps. Through the global map, a globe is generated, based on the global map as the terrain support of the action scenario, and when deploying a specific action scenario, a high-definition map of a specific simulated action range is selected to load, so as to determine the detailed location information of the map, and the demand further meets the deployment requirements.

[0054] When implementing this step S110, the map resource construction module can provide a corresponding calling interface or implement it based on a corresponding message processing mechanism. When it is necessary to load and display a global map or a regional high-definition map, the map loading type parameters that distinguish different maps are used as part of the map loading configuration parameters as input parameters, and the corresponding interface is called to implement the loading and display of the corresponding map.

[0055] According to an embodiment of the present disclosure, the method of adaptively loading a background map according to a map loading configuration parameter includes:

[0056] When the map loading configuration parameter is global map loading, the present disclosure derives a new map loading algorithm suitable for 3D spheres based on the algorithm principle of flat tile maps, thereby realizing the function of loading corresponding map tiles at any required longitude and latitude. The 3D sphere tile loading algorithm loads and displays the global map through local loading and drawing, including: with the current viewport as the center, only a preset number of map tiles within the viewport range and at the edge of the viewport are loaded at a time, and then according to the change of the viewport, new map tiles are dynamically loaded and map tiles that are no longer visible are unloaded, thereby greatly reducing the amount of real-time data writing and improving the software operation efficiency.

[0057] When implementing global map loading and display, the following two algorithm interfaces are mainly used:

[0058] 1. Tile map loading algorithm interface

[0059] The input parameters of the tile map loading algorithm interface are: longitude and latitude range, map level; the output data is: map tile path information array, corresponding row and column array. In the specific interface implementation, it mainly involves two parts of processing, namely: 1) Determination of the image to be loaded: Each map tile has its own longitude and latitude range for display. By inputting the longitude and latitude range and image level, all images in this level and longitude and latitude range are locked, and their path information is returned. 2) Position determination: The number of tile rows and columns at each level is fixed. The number of rows and columns of all images to be loaded is returned to determine their position on the sphere composed of faces with the corresponding number of rows and columns.

[0060] 2. Draw local area tile algorithm interface

[0061] The input parameters of the algorithm interface for drawing local area tiles are: longitude and latitude range, number of drawing rows, number of drawing columns, and drawing height; the output data is: map tile path information array, corresponding row and column array. In the specific interface implementation, the present disclosure mainly involves two parts of processing, namely: 1) Drawing conditions: define a specific class, and use the methods provided by the class to create / replace a section, which will be used as a map tile. 2) Drawing method: According to the longitude and latitude range and the number of tile rows and columns, calculate the longitude and latitude corresponding to the four vertices of each tile. Each tile consists of two triangular faces. By drawing the triangular faces in sequence, the required tile matrix is ​​finally obtained.

[0062] When designing and implementing the global map loading and display module, the present disclosure takes into account that: as the camera zooms in to the ground, the map level gradually increases, and the number of map tiles per unit area also increases accordingly. When the map level exceeds nine levels, due to the large number of tiles, it is no longer possible to efficiently load and generate the corresponding tile map in real time, resulting in large fluctuations in the number of frames, which affects both operational efficiency and user experience. The nine-level map cannot reliably meet the user's design requirements, so another map loading method is needed to further improve the required map resources on the basis of the global earth. Therefore, on the basis of realizing the loading and display of the global map, the present disclosure provides a regional high-definition map loading and display function for the map of a specific action area, which is used to load and display the corresponding regional high-definition map according to the regional identification information selected by the user.

[0063] When the map loading configuration parameter is regional HD map loading, the tile loading algorithm is also used. Unlike the global map loading method, a single HD map tile occupies a high memory. In order to prevent obvious frame rate fluctuations caused by loading HD maps during action scene deployment or simulation and ensure the running quality of the simulation process, the 3D sphere tile loading algorithm is used to load and display the corresponding regional HD map by loading and drawing all map tiles within the local area at one time. The currently loaded HD map information will be stored in the memory. After switching the action scene, the HD map will be loaded again from the memory, which effectively improves the loading efficiency.

[0064] When loading and displaying regional HD maps, it is mainly implemented through the tile map loading algorithm interface. The input parameters of the tile map loading algorithm interface are: regional HD map name, regional HD map latitude and longitude range, and regional HD map tile row and column number; the output data is: map tile path information array, corresponding row and column array. In the specific interface implementation, it mainly involves two parts of processing, namely: 1) Generate regional HD map grid surface: Generate a grid surface corresponding to the number of HD map rows and columns in the specified latitude and longitude range and location. 2) Determine the image to be loaded: According to the name and number of rows and columns of the regional HD map, search for all HD map tiles under the specified path in turn and return their information.

[0065] When conducting a multi-machine coordinated action simulation, a global map can be loaded and displayed at the beginning of the simulation to provide the main operation view. Then, when the simulation action range is selected later, a high-definition map of the display area can be loaded and displayed, and geographic information data corresponding to any point on the map can be provided, such as longitude and latitude information.

[0066] The present invention uses a map resource construction module to adaptively load and display global map loading or regional high-definition maps according to map loading configuration parameters, and adopts different loading algorithms according to their characteristics when loading different maps, thereby improving the efficiency of map loading, optimizing resource usage and loading speed, and providing accurate geographic location information support for the deployment of action scenarios, meeting the purposefulness, accuracy and reliability of the deployment location of action units, and making the conditions for action deployment closer to reality.

[0067] In step S120, a hypothetical model editing interface is provided, and model configuration parameters input by a user are obtained based on the hypothetical model editing interface; a corresponding hypothetical model is created according to the model configuration parameters, or an already created hypothetical model is updated.

[0068] The assumption model involved in this disclosure refers to the result of abstracting and simplifying the actual scene or system according to specific simulation requirements. It aims to provide an operational virtual scene with time and space constraints for the simulation system by simulating various entities, events and interaction processes in the actual environment. The establishment of the assumption model is mainly to verify and optimize system performance, as well as to provide decision support and training.

[0069] According to an embodiment of the present disclosure, the present disclosure also provides a hypothetical model management interface, based on which the description parameters of the created specified hypothetical model input by the user are obtained, and the corresponding specified hypothetical model is displayed or deleted according to the description parameters of the specified hypothetical model.

[0070] When implementing step S120, the present disclosure takes into account in the system design process: the deployment of action scenarios, mainly the deployment of action elements. Considering the various models of existing aircraft and different branches of the same model aircraft and the iterative updates of subsequent aircraft, there will inevitably be a large number of aircraft. Although the appearance is generally the same, there will inevitably be differences in various parameters and equipment configurations. In order to ensure the timeliness and compatibility of the assumption model (such as: aircraft model, automatic navigation aircraft model), based on the design concept of open model, the present disclosure provides an assumption model editing interface and a management interface through an assumption model editing module, supports the creation, update, display and deletion of assumption models according to the parameters input by the user, and sets a configurable interface for each parameter of the model through the bottom layer implementation, and has the ability to modify related parameters. The user can re-edit the existing model according to the existing model interface, and can also create a model according to the custom parameters, model the main equipment and then form an assumption model set, which enhances the user's ability to customize the model and facilitates the user to build and manage complex assumption models according to actual needs, thereby achieving unlimited possibilities for model capability configuration under the condition of limited model appearance.

[0071] At the same time, the present invention couples the specified capability indicators of the hypothetical model (for example, flight characteristics, detection capabilities, electromagnetic interference capabilities, etc.) with the action units created based on the corresponding hypothetical model. For example, when the action unit is selected in the system, it will have the characteristics and capability constraints of the action unit and the ability to modify related parameters, thereby improving the authenticity of the simulation.

[0072] In the present disclosure, the assumption models include but are not limited to three types, namely: mission aircraft model, detection aircraft model and automatic navigation aircraft model. When editing and managing the assumption models, corresponding operations can be implemented by providing a menu bar containing buttons such as "add", "delete", and "edit" in the operation visual interface. For example, when the user clicks "add", the parameter input interface of the corresponding model is entered. After the user enters the various parameters of the corresponding model, the user clicks "confirm", and the system will create the corresponding model according to the specific parameters entered by the user.

[0073] Specifically, for the mission aircraft model:

[0074] When editing a mission aircraft model, based on the existing mission aircraft appearance model, users can edit the name and model, maximum speed, maximum range, maximum ceiling, payload, price, radar type (such as detection radar, optoelectronic radar) and various parameters and indicators of various radars in the Add Mission Aircraft Model interface to create a mission aircraft model. Model data is stored in XML data format. Different teams of mission aircraft can be distinguished in the editing interface and display interface.

[0075] Among them, the mission aircraft model has three main behaviors in the simulation process: flying, detecting and executing missions. When simulating action scenarios based on the mission aircraft model, the present disclosure takes into account that these three behaviors related to the mission aircraft are affected by the following data in the context:

[0076] During the flight, the track is determined by the action scenario, and the speed along the track is affected by the track planning speed and the maximum speed.

[0077] During the detection process, the detection range is affected by the detection distance, the detection range and the distance of the opponent's electronic interference. The detection distance determines the distance of the basic detection capability, and the detection range determines the maximum detection angle relative to the aircraft axis. In the case of interference suppression by the opponent's target, the detection distance will be affected by the suppression distance of the opponent's target attributes (for the mission aircraft) and will be reduced.

[0078] After the enemy target enters the detection range, the actual detection probability obtained after the detection probability is affected by attributes such as ranging accuracy and angle measurement accuracy will be used to determine whether we have discovered the enemy target.

[0079] The conditions for the occurrence of the "execute mission" behavior are first that our aircraft has an "action distance" mission setting. Our aircraft will execute the action after the distance is less than the action distance. If not, it is determined by the following method: the necessary action distance and the effective action distance are affected by the action weight parameter. The action weight is affected by the distance between the aircraft and the maximum action distance of the automatic navigation aircraft. Action must be taken within the necessary action distance.

[0080] After the action occurs, the guidance phase will begin. In the guidance phase, if our aircraft keeps the opponent's target within the detection range within the guidance time, the guidance is successful, otherwise the guidance fails and the automatic navigation aircraft loses the target. The guidance time is set by the "Guidance Time" task, and the detection range is calculated in the same way as the detection range of the opponent's target.

[0081] Specifically, for detecting aircraft models:

[0082] When editing a detection aircraft model, based on the existing detection aircraft appearance model, users can edit the name and model, maximum speed, maximum range, maximum ceiling, detection radar radius, detection radar range, distance measurement accuracy, angle measurement accuracy and other data in the Add Detection Aircraft Model interface, and finally create a detection aircraft model. The model data is stored in XML data format. Different teams of detection aircraft can be distinguished in the editing interface and display interface.

[0083] Specifically, for the automatic navigation aircraft model:

[0084] The types of automatic navigation aircraft are more than the mission aircraft in terms of iteration, so the automatic navigation aircraft model also faces the same problem as the mission aircraft model, so the same solution is adopted, and the automatic navigation aircraft model is openly edited to improve the compatibility of the model. The present disclosure sets the following parameters for the automatic navigation aircraft model: name, maximum action distance, effective action distance, necessary action distance, flight speed, automatic navigation aircraft type, price, and overload. The probability of successful contact at different interaction distances is different. The probability of successfully reaching the target and changing its state at the necessary action distance is 100%. Among them, state changes include but are not limited to: disability.

[0085] When editing the automatic navigation aircraft model, based on the existing automatic navigation aircraft appearance model, edit the corresponding data in the Add Mission Aircraft Model interface, and finally determine to create an automatic navigation aircraft model for carrying and use by other action units. The model data is stored in XML data format.

[0086] When simulating action scenarios based on the automatic navigation aircraft model, the present disclosure takes into account the following characteristics related to the automatic navigation aircraft: the main attributes of the automatic navigation aircraft are speed and maximum action distance, effective action distance and necessary action distance, and these attributes affect the flight of the automatic navigation aircraft and the judgment of successful reach.

[0087] The flight path of the automatic navigation aircraft is that the initial speed direction after launch is consistent with the direction of the launching aircraft. During the flight, the attitude will be adjusted to keep heading towards the target. The speed during the flight is determined by the value of the attribute "speed".

[0088] During the flight of the automatic navigation aircraft, a contact judgment will be performed to determine whether it is close to the target. If the target is touched and a contact judgment occurs, the automatic navigation aircraft will enter the successful contact judgment. If it still fails to touch after exceeding the maximum action distance, the automatic navigation aircraft will self-deactivate.

[0089] The successful reach of an automatic navigation aircraft is determined by three factors. Factor 1: Flight distance factor. An automatic navigation aircraft will definitely reach successfully if its flight distance is less than the necessary action distance. When the flight distance is greater than the necessary action distance, the closer it is to the necessary action distance, the higher the success rate of reach, and the closer it is to the maximum action distance, the lower the success rate of reach. Factor 2: Direction factor. The closer the angle between the target aircraft's axis direction and the direction of the automatic navigation aircraft is to vertical, the lower the success rate of reach, and the closer they are to the same direction or opposite direction, the higher the success rate of reach. Factor 3: Target electronic action capability. The more false targets there are in the target electronic action capability, the lower the success rate of reach of the automatic navigation aircraft. On the contrary, if there is no false target capability, the success rate of reach is only affected by factors 1 and 2.

[0090] For the mission aircraft model, the present disclosure considers the three main behaviors of the mission aircraft model: flight, detection, and mission execution, and sets relevant parameters to affect these behaviors. This setting method makes the simulation process closer to the actual action scene and improves the accuracy of the simulation.

[0091] For the detection aircraft model, the present disclosure sets parameters such as detection radar radius and detection radar range to simulate its detection capability. This setting method enables the detection aircraft to play its actual role in the simulation process and improves the flexibility of the simulation.

[0092] For the automatic navigation aircraft model, the present disclosure sets parameters such as the maximum action distance, effective action distance, and necessary action distance to simulate its action capability. This setting method enables the automatic navigation aircraft to accurately reflect its performance characteristics during the simulation process, thereby improving the accuracy of the simulation.

[0093] In step S130, a multi-machine collaborative simulation action range selection interface is provided, and multi-machine collaborative simulation action range configuration parameters input by the user are obtained based on the multi-machine collaborative simulation action range selection interface; based on the multi-machine collaborative simulation action range configuration parameters, a corresponding multi-machine collaborative simulation action range background map is loaded and displayed; a multi-machine collaborative action scenario deployment interface is provided based on the multi-machine collaborative simulation action range background map, and action scenario configuration parameters input by the user are obtained based on the multi-machine collaborative action scenario deployment interface; a corresponding multi-machine collaborative action scenario is created and deployed according to the action scenario configuration parameters and a plurality of created assumption models, and description data of the multi-machine collaborative action scenario is generated; wherein the description data of the multi-machine collaborative action scenario includes: node data and node sequence data, and the multi-machine collaborative action scenario deployment interface includes an action simulation instruction menu, and action simulation instructions corresponding to the specified multi-machine collaborative action scenario input by the user are received based on the action simulation instruction menu, and the action simulation instructions and the description data of the specified multi-machine collaborative action scenario are sent to the action simulation and evaluation interface.

[0094] The present disclosure provides an open action scenario deployment environment. This step S130 uses the map operation vision provided by steps S110~120 and the created hypothetical model as support to achieve the creation and deployment of multi-machine collaborative action scenarios, and provides trigger operations for simulation and evaluation of the deployed multi-machine collaborative action scenarios.

[0095] The simulation system provided by the present invention supports the creation and deployment of multiple action scenarios, and provides an action scenario management interface. Based on the action scenario management interface, the description parameters of the created specified multi-machine collaborative action scenario input by the user are obtained, and the corresponding specified multi-machine collaborative action scenario is loaded, updated, reset or deleted according to the description parameters of the specified multi-machine collaborative action scenario.

[0096] When creating a multi-machine collaborative action scenario, it is mainly divided into two parts: simulation action range selection and action scenario deployment. Action scenario deployment is further divided into action rule deployment, action resource deployment, graphic deployment, etc. After creating a multi-machine collaborative action scenario, the simulation of the action scenario is triggered through the corresponding command menu item.

[0097] Figure 2 A flowchart of a method for creating and deploying a multi-machine collaborative action scenario according to an embodiment of the present disclosure is shown. Figure 2 As shown, it can be achieved through the following steps S131~S133:

[0098] In step S131, a multi-machine collaborative simulation action range selection interface is provided, and multi-machine collaborative simulation action range configuration parameters input by the user are obtained based on the multi-machine collaborative simulation action range selection interface. Based on the multi-machine collaborative simulation action range configuration parameters, a corresponding multi-machine collaborative simulation action range background map is loaded and displayed.

[0099] Among them, the multi-machine collaborative simulation action range refers to the geographical or logical space range covered or operated by multiple aircraft (such as drones) when performing a common task during the simulation, which can be a map area of ​​a specific location. In specific implementation, the supported simulation action ranges can be displayed in a list by name in the multi-machine collaborative simulation action range selection interface for user selection. The system then obtains and identifies the simulation action range name selected by the user as the multi-machine collaborative simulation action range configuration parameter, and converts the multi-machine collaborative simulation action range configuration parameter into a map loading configuration parameter that can be recognized by the map resource construction module, and then sends an action area map loading request carrying the map loading configuration parameter to the map resource construction module, and loads and displays the corresponding multi-machine collaborative simulation action range background map through the map resource construction module.

[0100] The present disclosure visualizes the action scene, and provides an intuitive visual environment for users through the background map of the action range of multi-machine collaborative simulation, so that users can have a clear understanding of the terrain, landforms, obstacles, key locations, etc. of the action area before deploying the action scene. This helps users to plan the action path and choose the action strategy more accurately. In addition, the geographic information data (such as terrain height, slope, vegetation distribution, etc.) in the background map can affect the action capability, concealment and communication effect of multiple machines. Based on this information, users can make more reasonable decisions when deploying action scenes, such as choosing suitable take-off points, landing points, detection routes, etc. In addition, before the action simulation, displaying the multi-machine collaborative action area through the background map can help users understand the action environment in advance and make full preparations for the subsequent simulation process. This includes setting appropriate action goals, formulating simulation rules, configuring simulation parameters, etc. In addition, using real or near-real background maps for action scene deployment can improve the authenticity and credibility of the simulation. This helps users to more accurately evaluate the effectiveness and effect of multi-machine collaborative actions and provide valuable references for subsequent practical applications.

[0101] In step S132, a multi-machine collaborative action scenario deployment interface is provided based on the multi-machine collaborative simulation action range background map, action scenario configuration parameters input by the user are obtained based on the multi-machine collaborative action scenario deployment interface, corresponding multi-machine collaborative action scenarios are created and deployed according to the action scenario configuration parameters and the multiple assumption models that have been created, and description data of the multi-machine collaborative action scenario is generated; wherein the description data of the multi-machine collaborative action scenario includes: node data and node sequence data.

[0102] In the deployment of action scenarios, based on the various assumption models that have been created, they need to be deployed on the background map of the simulated action range and the conditions for their current actions need to be edited.

[0103] In step S133, the multi-machine collaborative action scenario deployment interface includes an action simulation instruction menu, receives action simulation instructions corresponding to the specified multi-machine collaborative action scenario input by the user based on the action simulation instruction menu, and sends the action simulation instructions and the description data of the specified multi-machine collaborative action scenario to the action simulation and evaluation interface.

[0104] After completing the deployment of the action scenario, if it is necessary to simulate the action scenario, the user can start and control the simulation process through the action simulation command menu provided by the system.

[0105] The following is a detailed description of steps S131 to S133:

[0106] Specifically, the model configuration parameters include but are not limited to: team affiliation parameters, the team affiliation parameters include but are not limited to: the first team and the second team, the created assumption model corresponding to the first team is recorded as the assumption model of the first team, and the created assumption model corresponding to the second team is recorded as the assumption model of the second team, so as to use the assumption models of different teams to deploy action resources of different teams. For example: when creating a mission aircraft model and a detection aircraft model, the mission aircraft model and the detection aircraft model can be distinguished as the first team and the second team according to the team affiliation parameters.

[0107] The action scenario configuration parameters include but are not limited to: action rule configuration parameters, action resource configuration parameters and graphic resource configuration parameters, wherein the action rule configuration parameters include but are not limited to: action area configuration parameters and action task configuration parameters, and the node data include but are not limited to: action area node data, action unit node data, formation node data, action path node data and 2D graphic style node data.

[0108] Figure 3 The following is a schematic diagram showing a specific implementation of the multi-machine collaborative simulation action range selection interface in an action scenario according to an embodiment of the present disclosure. Figure 3 As shown, when implementing this step S131, the user can check the name of the simulated action range (first action range, second action range, third action range or fourth action range) of the action scenario simulation, and click the "OK" button after selection to load and display the corresponding multi-machine collaborative simulation action range background map, and automatically display the multi-machine collaborative action scenario deployment interface on the multi-machine collaborative simulation action range background map.

[0109] Figure 4 FIG. 2 shows a schematic diagram of a specific implementation of a multi-machine collaborative action scenario deployment interface in an action scenario according to an embodiment of the present disclosure. Figure 4 As shown, the multi-machine collaborative action scenario deployment interface consists of an action simulation instruction menu, a node controller interface, a node editing interface, and a hypothetical model display interface.

[0110] Among them, the menu items in the action simulation instruction menu include but are not limited to: play, pause, fast, slow and restart. The action simulation instruction corresponding to the play is used to start the simulation process of the multi-machine collaborative action scene, the action simulation instruction corresponding to the pause is used to pause the simulation process of the multi-machine collaborative action scene, the action simulation instruction corresponding to the fast is used to speed up the simulation process of the multi-machine collaborative action scene, the action simulation instruction corresponding to the slow is used to slow down the simulation process of the multi-machine collaborative action scene, and the action simulation instruction corresponding to the restart is used to restart the simulation process of the multi-machine collaborative action scene.

[0111] The node controller interface includes an "Add Node" button. When the user clicks the button, the node editing interface pops up on the right side of the action simulation instruction menu. The user can select the node type and edit the corresponding node content. After editing, click "Apply", and the corresponding node item will be displayed in the node list of the node controller interface. For example: Figure 4 The node list of the node editing interface shown shows the Set Action Area node.

[0112] The hypothetical model display interface is used to display the hypothetical model of the first team and the hypothetical model of the second team, so that the user can deploy the action units of each team on the background map of the multi-machine collaborative simulation action range according to the hypothetical model of the first team and the hypothetical model of the second team.

[0113] When the step S132 is specifically implemented, the action scenario may be deployed based on the mission aircraft model, the detection aircraft model, and the automatic navigation aircraft model created in the step S120.

[0114] Figure 5A flowchart of a method for creating and deploying a corresponding multi-machine collaborative action scenario and generating description data of the multi-machine collaborative action scenario according to the action scenario configuration parameters and the created multiple scenario models according to an embodiment of the present disclosure is shown. Figure 5 As shown, it can be achieved through the following steps S510~S540:

[0115] In step S510, an action area is formed on the background map of the multi-machine collaborative simulation action range according to the action area configuration parameters, and corresponding action area node data is generated. The action area is used to define an area for simulating action between the action units of the first team and the action units of the second team.

[0116] like Figure 4 As shown, when the user chooses to set the action area node, the node editing interface is the interface for editing the action area node. The action area is a rectangular area composed of two longitude and latitude anchor points (upper left anchor point and lower right anchor point). The action area configuration parameters include but are not limited to: upper left anchor point position information, lower right anchor point position information and action area color. By inputting the position information (longitude, latitude, altitude) and color of the two longitude and latitude anchor points, an action area range is generated. If the action unit that enters the action area leaves the action area, it is deemed that its action has failed and will return to the starting point.

[0117] In specific implementation, the longitude and latitude coordinates of any position on the map can be quickly obtained by setting the longitude and latitude pipette on the interface for obtaining the specific action area configuration parameters, which facilitates the user's operation.

[0118] At the same time, by visualizing the action area, users can more intuitively see the distribution and configuration of the action units of the first team and the second team in the background map of the simulated action range, as well as the relative positions and relationships between the action units. This helps users quickly understand the action layout, identify key areas and potential problems, and more easily formulate and adjust action strategies, optimize the deployment of action units, action paths and formation configurations to improve action efficiency, thereby formulating more scientific and reasonable action plans and decisions.

[0119] After the action area is formed, the generated action area node data is used for storage and management of the action area node information.

[0120] In step S520, the assumption model of the first team and the assumption model of the second team are configured respectively according to the action resource configuration parameters and the action task configuration parameters to generate other data in the node data except the action area node data and the 2D graphic style node data.

[0121] Figure 6A flowchart of a method for configuring the first team's scenario model and the second team's scenario model according to the action resource configuration parameters and the action task configuration parameters according to an embodiment of the present disclosure is shown. Figure 6 As shown, this step S520 is specifically implemented through the following steps S521 to S523:

[0122] In step S521, through a preset deployment method, according to the node editing parameters in the action resource configuration parameters, based on the hypothetical model of the first team and the hypothetical model of the second team, the action units of the first team and the action units of the second team are deployed on the background map of the multi-machine collaborative simulation action range outside the action area, and corresponding action tasks are configured for the action units of the first team and the action units of the second team according to the action task configuration parameters, and corresponding action unit node data is generated.

[0123] Specifically, the preset deployment methods include but are not limited to three methods, one is the drag-and-drop deployment method, the other is the node editing method, and the other is a combination of the two methods. Among them, the drag-and-drop deployment method is the most convenient method. The user generates the corresponding action unit by dragging the assumption model displayed in the assumption model display interface to the multi-machine collaborative simulation action range background map outside the action area. The node editing method is to generate the corresponding action unit node by clicking the add node button in the node controller, selecting the node type as the generated action unit and editing the corresponding node information in the provided node editing interface.

[0124] If the action unit nodes are generated only by dragging, it cannot meet all deployment conditions, or the accuracy of the deployment position cannot be reliably guaranteed. Therefore, the drag-and-drop deployment method is combined with the most primitive but effective node editing method. First, drag and drop, and then supplement or change the node attributes generated on this basis, thereby conveniently and accurately realizing the comprehensive configuration and management of the action unit. The specific operation is: select the assumption model to be generated in the assumption model display interface, press and hold the left mouse button and drag the model to the selected position in the map, and then release the left mouse button to generate the corresponding action unit, select the action unit, and click the Add Node button in the node controller to view and edit the information of the action unit, modify the corresponding parameters, and adjust the action unit to the ideal state. Figure 7 The specific implementation diagram of generating an action unit node according to an embodiment of the present disclosure is shown. The information of the action unit includes but is not limited to: the position information, rotation parameters, scaling parameters, model ID, instance ID, team ID, whether to play automatically, etc. of the action unit.

[0125] Figure 8A schematic diagram of a specific implementation after generating multiple action unit nodes in a multi-machine collaborative action scenario deployment interface according to an embodiment of the present disclosure is shown. The action units of the first team include the first action unit, the second action unit, and the third action unit, and the action units of the second team include the fourth action unit, the fifth action unit, and the sixth action unit.

[0126] In specific implementation, users can click on the action unit entity in the action scene to view the basic attribute information of the action unit and the status of the missiles carried. For example, if the action unit is a mission aircraft, then for the action unit, you can see the corresponding model, maximum speed, maximum range, maximum ceiling, number of missiles carried, price, type of radar (detection radar, optoelectronic radar) and various parameter indicators of various radars. At the same time, the corresponding action task can be configured for the action unit. Specifically, the action task configuration parameters input by the user can be obtained through the corresponding action task configuration interface, and then the action tasks of the action units of the first team and the action units of the second team can be configured.

[0127] The present disclosure supports the creation, modification, viewing and deletion of action tasks. When creating an action task, it is necessary to obtain the action task configuration parameters input by the user, including the name, type and type-related parameters of the action task. The type of the action task may include the action distance, guidance time, radar-on distance from the other party's target, etc. For example, an action task named "cooperative action" may be configured for the first action unit. The action task includes three task types: action distance, guidance time and radar-on distance from the other party's target. The parameters of these three task types may be configured separately.

[0128] After the action units of both teams are generated and configured, the generated action unit node data is used for storage and management of action unit node information.

[0129] In step S522, the action units of the first team and the action units of the second team are configured and formed respectively according to the master-slave attributes and formation parameters in the action resource configuration parameters to form formation nodes corresponding to the first team and the second team respectively, and generate corresponding formation node data, wherein the master-slave attributes include: host attributes and slave attributes.

[0130] This public support is for the formation of action units of the same team that have been deployed. Each formation consists of a master and multiple slaves. The slaves in the formation will temporarily lose active flight and maneuvering evasion actions. All flight actions follow the master, and the timing of actions follows the master.

[0131] Before forming a formation, you need to set the master and slave attributes for the action units. By default, they are all slaves. Set the required action unit as the master.

[0132] Likewise, formation nodes can be added using the Node Controller interface by clicking the Add Node button and selecting the node type as "Formation Edit". Fig. 9 A specific implementation schematic diagram of generating a formation node according to an embodiment of the present disclosure is shown. When generating a formation node, the formation parameters input by the user are obtained through the node editing interface. The formation parameters include: formation code, host ID, slave members, and the detection capability of the entire formation. If the detection capability is not set, all values ​​are 0, and the final formation detection capability will be consistent with the host detection capability. The selection of slave members can be selected in sequence according to the slave ID, or the selection button on the right side of the slave member can be used. After clicking the button, press and hold the left mouse button to start the selection, and release the left mouse button to end the selection. In the selection range, all slaves consistent with the host team will be selected. Click the detachment button behind each slave, and the corresponding slave will be released from the formation.

[0133] After the formation nodes of both teams are generated and configured, the generated formation node data is used for storage and management of formation node information.

[0134] In step S523, the movement paths and movement speeds for entering the action area are configured for the action units of the first team and the action units of the second team according to the action path information in the action resource configuration parameters, corresponding action path nodes are formed, and corresponding action path node data are generated.

[0135] This document supports adding movement paths for deployed action units with host attributes. Slaves in the formation do not need to be added. If a movement path is added for them, it will be automatically ignored.

[0136] Likewise, a motion path node can be added using the node controller interface by clicking the Add Node button and selecting the node type as "Add Motion Path". Fig.10 A specific implementation diagram of generating an action path node according to an embodiment of the present disclosure is shown. When generating an action path node, the action path information input by the user is obtained through the node editing interface, and the action path information includes: the start time (i.e., the take-off time), the model ID of the added path, the moving speed (the speed is not limited by the maximum speed of the action unit itself), and the target point set (the main path points of the moving path, and the actual path will be automatically smoothed between the path points).

[0137] After the action path nodes of both teams are generated and configured, the generated action path node data is used for storage and management of the action path node information.

[0138] In step S530, a 2D graphic style is set for the multi-machine collaborative action scene according to the graphic resource configuration parameters to form a corresponding 2D graphic style node, and generate corresponding 2D graphic style node data. The 2D graphic style is used to define the display format of the graphic information of the multi-machine collaborative action scene simulation process.

[0139] Since the deployment plan and purpose in the action scenario are abstract, and the conditions, background and purpose of the simulation cannot be intuitively understood, it is necessary to intuitively describe the various information, requirements and purposes of the simulation outside the simulation process. The present disclosure supports the addition of 2D graphic elements in the action scenario deployment to clarify other factors such as the purpose and background of the action, and can generate multiple 2D graphic styles by obtaining the graphic resource configuration parameters input by the user, and each style freely combines text content with background content.

[0140] Fig.11 The specific implementation schematic diagram of generating a 2D graphic style node according to an embodiment of the present disclosure is shown. When generating a 2D graphic style node, the graphic resource configuration parameters input by the user are obtained through the node editing interface, and the graphic resource configuration parameters include: graphic ID, font size, font color, font style, text content, existence time, background style, etc.

[0141] After the 2D graphic style node is generated, the generated 2D graphic style node data is used for storage and management of the 2D graphic style node information.

[0142] In step S540, the node sequence data is generated according to the generation order of the node data.

[0143] According to the above description, the process of generating node sequence data is based on the generation order of node data. Fig.11 The node controller interface identifies the serial number of each node from 0 to 11 to generate a unique identifier for each node, which is used to uniquely identify each node data in subsequent steps. In specific implementation, it can be implemented by creating a list or array.

[0144] After the deployment of the multi-machine coordinated action scenario is completed based on step S130, if a simulation of the action scenario is required, the user can click Fig.11 Click the "Play" button in the action simulation command menu shown to start the simulation process. During playback, the simulation rate can be adjusted through the "Fast" and "Slow" buttons.

[0145] The present disclosure designs each resource in the action scene (such as action area, action unit, formation, action path and 2D graphic style) into a node, so that these resources can be combined and reused like building blocks. This modular design not only improves the flexibility of the system, but also makes it more efficient when creating and modifying action scenes. At the same time, organizing resources in the form of nodes can clearly display the logical structure of the action scene. Each node represents a specific resource or operation, and the connection and order between nodes reflect the dependency relationship and action process between resources. This structured representation method helps to understand and analyze the action scene. In addition, simulating the action scene in the order of node generation can ensure the orderliness and controllability of the simulation process. The system can process each resource in sequence according to the node order, thereby simulating the dynamic change process of the action scene. At the same time, this sequential simulation method also helps to troubleshoot and optimize performance during the simulation process.

[0146] In addition, the node-based design allows the action scenes to be displayed graphically on the interface, thereby improving the visualization of the system. Users can intuitively see each node and the connection relationship between them, as well as the real-time status of the simulation process through the interface. In addition, users can also interact with the system through the interface, such as adjusting node parameters, adding or deleting nodes, etc., to achieve flexible configuration and control of the action scenes.

[0147] In step S140, the action simulation and evaluation interface is provided, the action simulation instruction and the description data of the specified multi-machine collaborative action scenario are received based on the action simulation and evaluation interface, the specified multi-machine collaborative action scenario is visually simulated according to the action simulation instruction and the node data in the node order indicated by the node sequence data, and the simulation process is controlled and managed. During the simulation process, the situation information of the specified multi-machine collaborative action scenario is displayed in the action simulation and evaluation interface, and the simulation results are summarized. When the simulation process is completed, the simulation results are evaluated, and the evaluation results of the specified multi-machine collaborative action scenario are displayed in the action simulation and evaluation interface.

[0148] According to an embodiment of the present disclosure, the visual simulation of the specified multi-machine collaborative action scenario according to the action simulation instruction and the node data according to the node sequence indicated by the node sequence data includes:

[0149] When the action simulation instruction is an action simulation instruction corresponding to the play, the designated multi-machine collaborative action scene is visually simulated according to the node data in sequence according to the node sequence indicated by the node sequence data.

[0150] Among them, the specified multi-machine collaborative action scenario refers to a multi-machine collaborative action scenario that has been created and deployed and needs to be simulated; the node sequence data specifically refers to the node sequence data in the description data of the specified multi-machine collaborative action scenario, and the node data specifically refers to the node data in the description data of the specified multi-machine collaborative action scenario.

[0151] When the action scenario is deployed and the simulation begins, users prefer to see the real-time simulation rather than the simulation with only data and conclusions. In the specific implementation of the visual simulation, the present disclosure supports the perspective following of the action unit, and receives the user input instructions to start the perspective following or exit the perspective following through the menu items on the action simulation and evaluation interface. After starting the perspective following, the system needs to determine the simulation order of each node according to the instructions of the node sequence data. When the simulation reaches a certain action path node, the system can calculate the current position of the action unit corresponding to the node. Then, move the perspective to the position, or keep a certain distance and angle to follow the action unit; when the simulation reaches a certain node (for example, the action unit starts to act or performs a specific task), the system can continue to follow the action unit to show its behavior and status. The perspective can be smoothly adjusted according to the movement and action of the action unit to keep the attention on the action unit; if you choose to follow the action unit in a formation, the system can follow the overall movement of the formation, or select a specific action unit in the formation to follow. When the formation moves or changes formation, the perspective can be adjusted accordingly to keep the attention on the formation.

[0152] Fig.12 A flowchart of a method for visually simulating the specified multi-machine collaborative action scenario according to the node sequence indicated by the node sequence data is shown according to an embodiment of the present disclosure. Specifically, the method includes the following steps S1210-1220:

[0153] In step S1210, a visual simulation is performed on the movement path of the action unit entering the action area according to the action path node data.

[0154] In step S1220, based on the action unit node data, after the action unit enters the action area, the interactive behavior and action path between the action units of different teams are visually simulated, and after leaving the action area, the behavior status of the action unit is visually simulated.

[0155] The action units refer to the action units of the first team and the action units of the second team.

[0156] In the specific implementation, first, the system will extract the action area node data and action path node data from the description data of the multi-machine collaborative action scene. The action area node data defines the specific area where the two action parties conduct simulated interaction, while the action path node data describes in detail the movement path of each action unit from the starting position to the action area, including the shape, length, direction and movement speed of the path.

[0157] Next, the system will draw the movement path of each action unit on the background map based on the action path node data. This is usually achieved by drawing a series of continuous line segments or curves on the map, the starting point of these line segments or curves is the starting position of the action unit, and the end point is a certain entry point of the action area. At the same time, the system will also calculate the time required for the action unit to move along these paths based on the movement speed data, and display these movement paths at an appropriate speed during the visual simulation process.

[0158] To enhance the visualization effect, the system also adds visual effects to the movement path, such as dynamic arrows, movement trajectory lines, etc., to more intuitively display the movement direction and speed of the action unit. In addition, the system can also apply different visual attributes such as color, line style and width to the movement path according to the type of action unit (such as mission aircraft, detection aircraft, etc.) and the different movement environments, so that users can more clearly identify different action units and movement paths.

[0159] When the action unit enters the action area, the system will simulate the behavior and action path between the action units of different teams based on the action unit node data. This usually includes the following two aspects:

[0160] 1. Behavior simulation: The system will calculate the possible behavior of each action unit in the action area according to the attributes and action rule configuration parameters of the action unit. Then, the system will display these behaviors in the visual interface through animation, sound and other special effects.

[0161] 2. Action path simulation: In the action area, the movement path of the action unit may change due to factors such as terrain obstacles. Therefore, the system needs to dynamically calculate the new movement path of each action unit based on the real-time situation and the properties of the action unit. Then, the system will update these movement paths in real time in the visual interface so that users can clearly see the dynamic movement process of the action unit in the action area.

[0162] When the action units leave the action area, the system will simulate the behavior status of these action units based on the action unit node data. This usually includes key attributes such as the remaining health of the action units, the number of projectiles, the amount of fuel, and whether they are in a waiting, patrolling, or returning state. The system will display these behavior states in the visual interface through icons, color changes, etc., so that users can understand the current status of the action units in a timely manner.

[0163] During the entire visual simulation process, the system also continuously updates and refreshes the information on the visual interface according to the current situation and the attributes of the action unit to ensure that the user can obtain the most accurate action situation and the status information of the action unit in real time. At the same time, the system also needs to provide some interactive functions, such as zooming, panning, selection, etc., so that users can more conveniently observe and analyze the current situation.

[0164] Fig.13 A flow chart of a method for controlling and managing a simulation process according to an embodiment of the present disclosure is shown. Specifically, the following steps S1310-1330 are included:

[0165] In step S1310, the life cycle of the action unit is managed according to the action unit node data and the action area node data.

[0166] In step S1320, data communication for coordinated actions between action units of the same team is managed according to the formation node data.

[0167] In step S1330, the action state of the action unit during the simulation is obtained, and the interactive behavior of the action unit is controlled according to the action state of the action unit to generate an optimized moving path.

[0168] During the simulation, each action unit has its own specific life cycle, including creation, activation (performing various actions in the action area), damage, repair, and final state change. Throughout the life cycle, the system will continuously monitor the state of the action unit and update or adjust its properties as needed. This ensures that the simulated action unit can truly reflect the dynamic changes in the actual scene.

[0169] In addition, during the simulation, the action units of the same team need to maintain close coordination to ensure the success of the action strategy. This usually involves data communication between action units to share key information. For example: the action unit will send information to other allied units as needed. When the action target is found, the detection aircraft will send information about the other target's location and movement direction to the mission aircraft. The system will ensure that this information can be sent and received correctly according to the communication protocols and rules in the formation node data. The action unit that receives the information will process this information according to its current status and action mission, and respond accordingly. For example, the mission aircraft will adjust its movement path and action strategy based on the information provided by the detection aircraft. Managing these data communications according to the formation node data can ensure the accuracy and timeliness of the information.

[0170] In addition, during the simulation, the action status of the action unit will continue to change, depending on multiple factors such as its current position, the opponent's actions, and the support of the same team. In order to ensure that the action unit can make the best action decision, the system needs to obtain its action status in real time and adjust the interaction behavior and movement path according to these states.

[0171] According to an embodiment of the present disclosure, the action states include: a response initiation state, an evasion state and a circling state.

[0172] When the behavior of the action unit is controlled according to the action state of the action unit and an optimized movement path is generated, in order to make the simulation closer to the real action scene, the present disclosure provides the following three specific algorithms to simulate the real aircraft flight:

[0173] Algorithm 1:

[0174] When the action state of the action unit is to initiate the response state, a detour response curve movement path is generated in the following manner:

[0175] The dot products of the left vertical direction and the right vertical direction of the action unit and the action target direction are calculated respectively, and the direction whose dot product result is less than 0 is selected as the detour offset direction f.

[0176] The dot product of the direction of the action unit and the direction of the action target is calculated and the modulus is obtained. The offset weight r is calculated according to the modulus m and the distance l between the action unit and the action target by the following formula:

[0177] ;

[0178] Wherein, a is the distance between the action unit and the action target when the direction of the action unit is opposite to the direction of the action target and the offset weight r is 1;

[0179] The third point P3 is calculated according to the coordinate point P1 of the action unit and the coordinate point P2 of the action target by the following formula:

[0180] ;

[0181] Where b is a constant.

[0182] The detour corresponding curve moving path is calculated using a third-order Bezier curve algorithm according to the coordinate point P1 of the action unit, the coordinate point P2 of the action target and the third point P3.

[0183] Algorithm 2:

[0184] When it is determined that the action unit has found the action target and meets the action conditions, it is determined whether the action unit carries an automatic navigation aircraft and a target detector (such as a radar). If so, the action unit is controlled to launch the automatic navigation aircraft, and the position information of the action target is sent to the automatic navigation aircraft by controlling the target detector carried by the action unit, so that the automatic navigation aircraft corrects its own direction according to the received position information of the action target and flies in the direction of the action target; when it is determined that the total flight distance of the automatic navigation aircraft is greater than the maximum range in the automatic navigation aircraft's own attributes, it is regarded as a target recognition failure; when the automatic navigation aircraft does not receive the position information of the action target within a preset time, the automatic navigation aircraft is controlled to fly in the current direction until the state changes; when it is determined that the automatic navigation aircraft successfully touches the action target during flight, the state change probability is calculated according to the necessary action distance, effective action distance and maximum action distance in the automatic navigation aircraft's own attributes, and whether the state of the action target changes is determined according to the state change probability.

[0185] Among them, the target detector carried by the action unit is used to determine whether the action unit has discovered the action target in the following manner: a spherical scanning interval is generated with the current coordinate point of the target detector as the sphere center and the detection distance of the target detector as the radius, and the action target in the spherical scanning interval is acquired with a preset period; whether the action target is scanned by the target detector is determined by the current position of the target detector, the scanning angle of the target detector and the current position of the action target; when it is determined that the action target is scanned by the target detector, it is determined that the action unit has discovered the action target.

[0186] When the action state of the action unit is in the evading state, the left and right evading curved movement paths are generated in the following manner:

[0187] The dot product of the direction of the action unit and the left vertical direction and the right vertical direction of the avoidance object is calculated respectively, and the direction in which the dot product result is greater than 0 is selected as the avoidance offset direction.

[0188] The coordinate point of the action unit is taken as the first point, the coordinate point at the first preset distance in the avoidance offset direction of the coordinate point of the action unit is taken as the second point, the coordinate point at the center point between the first point and the second point and at a height drop of the second preset distance is taken as the third point, and the left and right avoidance curve moving paths are calculated using a third-order Bezier curve algorithm based on the first point, the second point and the third point.

[0189] Algorithm 3:

[0190] When the action state of the action unit is circling, a free circling curve movement path is generated in the following manner:

[0191] According to the action area configuration parameters, half of the distance between the two closest parallel sides in the action area is calculated to obtain the maximum radius of the circling path. .

[0192] The coordinate point of the action unit is taken as the first point, and the coordinate point of the action unit is rotated clockwise by a first preset angle around the center point of the action area, and the distance from the center point of the action area to the center point of the action area is 0 to as the second point, the coordinate point of the action unit rotated clockwise around the center point of the action area by a second preset angle and the coordinate point RP3 away from the center point of the action area is taken as the third point, and the free spiral curve moving path is calculated using the third-order Bezier curve algorithm according to the first point, the second point and the third point; wherein,

[0193] ;

[0194] is the distance from the first point to the center point of the action area, is the distance from the second point to the center point of the action area.

[0195] The present invention improves the authenticity and accuracy of the simulation by controlling the behavior of the action unit according to its action state and generating an optimized movement path.

[0196] According to an embodiment of the present disclosure, the displaying of the situation information of the specified multi-machine coordinated action scenario in the action simulation and evaluation interface includes:

[0197] The situation information of the designated multi-machine coordinated action scenario is displayed in the action simulation and evaluation interface in a 2D graphic style indicated by the 2D graphic style node data.

[0198] According to an embodiment of the present disclosure, summarizing the simulation results includes:

[0199] The loss type, quantity and amount of the action units and carried equipment corresponding to the first team and the second team in the specified multi-machine coordinated action scenario are recorded, wherein the carried equipment includes: automatic navigation aircraft and projectiles.

[0200] According to an embodiment of the present disclosure, the evaluating the simulation result includes:

[0201] According to the loss type, quantity and amount of the action units and the equipment carried by the first team and the second team, the synergy effectiveness between the action units of the same team is evaluated, and the effectiveness analysis result of the formation between the action units of the first team and the second team is obtained, and the effectiveness analysis result is used as the evaluation result.

[0202] When conducting specific evaluations, we can analyze the collaborative action data between different action units within each team, including data communication, action paths, behaviors, etc., to evaluate the role of these collaborative actions in reducing losses and improving action efficiency. Then, based on the loss situation and collaborative action data, we calculate the collaborative effectiveness index of each team.

[0203] The coordination effectiveness indicators include: average loss rate, action success rate, task completion time, etc. Finally, the coordination effectiveness indicators of the two teams are compared to evaluate which team's coordinated action is more effective, or to compare the coordination effectiveness of different formations or different action units within the same team.

[0204] The effectiveness analysis results may include: efficient coordination, inefficient coordination and chaotic coordination. Efficient coordination indicates that the coordinated actions between action units are close, which can effectively reduce losses and improve action efficiency. The types of losses are mostly non-contact losses (such as failures) and the number is small; inefficient coordination indicates that the coordinated actions between action units are not close enough, resulting in increased losses and reduced action efficiency. The types of losses are mostly contact losses (such as state changes) and the number is large; chaotic coordination indicates that the coordinated actions between action units are chaotic, the losses are extremely serious, and the action efficiency is low.

[0205] Fig.14 FIG. 1 is a schematic diagram showing the evaluation results in the action simulation and evaluation interface according to an embodiment of the present disclosure. Fig.14As shown, the action simulation and evaluation interface includes a situation information display interface, a damage table and an evaluation table. The situation information display interface displays the situation information of both teams during the simulation process, the damage table displays the damage to both teams, and the evaluation table displays the collaborative effectiveness evaluation results of each team.

[0206] By evaluating the collaborative effectiveness, the present disclosure can discover problems and deficiencies in collaborative actions, thereby making targeted improvements and providing a basis for the allocation of action resources and decision-making in actual actions.

[0207] Fig.15 FIG. 2 is a flowchart of another model-based multi-machine collaborative action simulation and evaluation method according to an embodiment of the present disclosure. Fig.15 As shown, in addition to the above steps S110 to S140, the simulation and evaluation method further includes the following step S150:

[0208] In step S150, a tool instruction input by the user is obtained based on the current interface, and the tool instruction corresponds to a designated auxiliary tool; a parameter configuration interface of the designated auxiliary tool is provided according to the tool instruction, and operation parameters corresponding to the designated auxiliary tool input by the user are obtained based on the parameter configuration interface of the designated auxiliary tool, and the operation corresponding to the designated auxiliary tool is performed according to the operation parameters of the designated auxiliary tool.

[0209] The current interface refers to any operation interface currently displayed in the system, for example, it may be a multi-machine collaborative simulation action range selection interface or a multi-machine collaborative action scenario deployment interface.

[0210] When developing and designing the simulation system provided by the present invention, based on some problems encountered in the process, such as: the user interaction interface occupies a large number of viewports, the IDs of distant action elements overlap unclearly, and the size of the model cannot adapt to the usage habits of everyone, a special and completely independent module is used to provide a variety of auxiliary tools for solving the above problems in order to improve the user experience, while not causing any impact on the action simulation.

[0211] The designated auxiliary tools include but are not limited to any one of the following auxiliary tools: distance measuring tool, map resource editing tool, visibility tool, zoom tool, free screenshot tool and visual mode selection tool.

[0212] Specifically, the distance measuring tool is used to measure the distance of any path in the map and calculate the time required to fly the entire route at a certain speed; the free screenshot tool is used to take a screenshot of the simulation system interface at any time and save it to a specified folder; the display and hiding tool includes the display and hiding of the action unit ID, the display and hiding of the UI interface, and the display and hiding of the formation line, among which the display and hiding of the action unit ID is used to uniformly display or hide the IDs of all action units in the action scene, the display and hiding of the UI interface is used to uniformly display or hide the UI in the viewport to provide a pure observation field of view, and the display and hiding of the formation line is used to uniformly display or hide the formation in the action scene. The master-slave relationship lines of the elements are uniformly displayed or hidden; the zoom tool is used to uniformly enlarge or reduce the visual size of the action units in the action scene; the visual mode selection tool is used to provide a standard visual mode and a no-light mode. In the no-light mode, the brightness will tend to be balanced and there will be no shadows; the map resource editing tool is used to mark resource points for the map. Users can use the provided map resource editing interface to click the "Add" button set above to set the resource point type and enter the resource point name and location. After closing the interface, the relevant information of the resource point is automatically generated and saved. All resource points can also be cleared through the provided menu items.

[0213] Fig.16 FIG. 2 is a schematic diagram showing a structure of a model-based multi-machine collaborative action simulation and evaluation system according to an embodiment of the present disclosure. Fig.16 As shown, the simulation and evaluation system includes: a map resource construction module, a hypothesis model editing module, a multi-machine collaborative action scenario deployment module and an action simulation and evaluation module.

[0214] Fig.17 The following is an operation sequence diagram of a model-based multi-machine collaborative action simulation and evaluation system according to an embodiment of the present disclosure.

[0215] The map resource construction module is configured to adaptively load and display the background map according to the map loading configuration parameters, and provide geographic information data of the background map.

[0216] The assumed model editing module is configured to: provide a assumed model editing interface, and obtain model configuration parameters input by the user based on the assumed model editing interface; create a corresponding assumed model according to the model configuration parameters, or update the created assumed model.

[0217] The multi-machine collaborative action scenario deployment module is configured to: provide a multi-machine collaborative simulation action range selection interface, obtain the multi-machine collaborative simulation action range configuration parameters input by the user based on the multi-machine collaborative simulation action range selection interface, and load and display the corresponding multi-machine collaborative simulation action range background map based on the multi-machine collaborative simulation action range configuration parameters; provide a multi-machine collaborative action scenario deployment interface based on the multi-machine collaborative simulation action range background map, obtain the action scenario configuration parameters input by the user based on the multi-machine collaborative action scenario deployment interface, create and deploy the corresponding multi-machine collaborative action scenario according to the action scenario configuration parameters and the created multiple assumption models, and generate the description data of the multi-machine collaborative action scenario; wherein the description data of the multi-machine collaborative action scenario includes: node data and node sequence data, the multi-machine collaborative action scenario deployment interface includes an action simulation instruction menu, receives the action simulation instruction corresponding to the specified multi-machine collaborative action scenario input by the user based on the action simulation instruction menu, and sends the action simulation instruction and the description data of the specified multi-machine collaborative action scenario to the action simulation and evaluation interface.

[0218] The action simulation and evaluation module is configured to: provide the action simulation and evaluation interface, receive the action simulation instructions and the description data of the specified multi-machine collaborative action scenario based on the action simulation and evaluation interface, perform visual simulation of the specified multi-machine collaborative action scenario according to the action simulation instructions and the node data in the node sequence indicated by the node sequence data, and control and manage the simulation process; during the simulation process, display the situation information of the specified multi-machine collaborative action scenario in the action simulation and evaluation interface, and summarize the simulation results; when the simulation process is completed, evaluate the simulation results, and display the evaluation results of the specified multi-machine collaborative action scenario in the action simulation and evaluation interface.

[0219] According to an embodiment of the present disclosure, the simulation and evaluation system also includes: an auxiliary tool module, which is configured to: obtain tool instructions input by the user based on the current interface, wherein the tool instructions correspond to the designated auxiliary tool; provide a parameter configuration interface of the designated auxiliary tool according to the tool instructions, obtain operation parameters corresponding to the designated auxiliary tool input by the user based on the parameter configuration interface of the designated auxiliary tool, and perform the operation corresponding to the designated auxiliary tool according to the operation parameters of the designated auxiliary tool; wherein the designated auxiliary tool includes any one of the following auxiliary tools: a distance measuring tool, a map resource editing tool, a display and hiding tool, a zoom tool, a free screenshot tool, and a visual mode selection tool.

[0220] According to the technical solution provided by the embodiment of the present disclosure, based on the design concept of open model, by providing a hypothetical model editing interface, a multi-machine collaborative simulation action range selection interface and a scenario deployment interface, the multi-machine collaborative action scenario is configured and deployed in multiple levels and dimensions according to various configuration parameters input by the user, so that the user can quickly configure and deploy the scenario according to different action requirements, thereby improving the flexibility and accuracy of scenario deployment; by providing the action simulation and evaluation interface, the specified multi-machine collaborative action scenario is visually simulated, and the simulation process is controlled and managed. During the simulation process, the situation information of the specified multi-machine collaborative action scenario is displayed, and the simulation results are summarized. When the simulation process is completed, the simulation results are evaluated, and the evaluation results of the specified multi-machine collaborative action scenario are displayed in the action simulation and evaluation interface, so that the user can observe the simulation process and the use of the collaborative function in real time and intuitively, which is convenient for the user to conduct subsequent analysis and decision-making. As a result, the intuitiveness and actionability of the simulation system are improved, and the flexibility and extensibility of the simulation system are enhanced, so as to adapt to different simulation needs.

[0221] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.

Claims

1. A model-based multi-machine collaborative action simulation and evaluation method, characterized in that: include: Adaptively load and display the background map according to the map loading configuration parameters, and provide geographic information data of the background map; Providing a scenario model editing interface, and obtaining model configuration parameters input by a user based on the scenario model editing interface; creating a corresponding scenario model according to the model configuration parameters, or updating an already created scenario model; Providing a multi-machine collaborative simulation action range selection interface, obtaining multi-machine collaborative simulation action range configuration parameters input by a user based on the multi-machine collaborative simulation action range selection interface, and loading and displaying a corresponding multi-machine collaborative simulation action range background map based on the multi-machine collaborative simulation action range configuration parameters; Based on the background map of the multi-machine collaborative simulation action range, a multi-machine collaborative action scenario deployment interface is provided; based on the multi-machine collaborative action scenario deployment interface, action scenario configuration parameters input by the user are obtained; according to the action scenario configuration parameters and the multiple assumption models that have been created, a corresponding multi-machine collaborative action scenario is created and deployed, and description data of the multi-machine collaborative action scenario is generated; wherein the description data of the multi-machine collaborative action scenario includes: node data and node sequence data; the multi-machine collaborative action scenario deployment interface includes an action simulation instruction menu; based on the action simulation instruction menu, an action simulation instruction corresponding to a specified multi-machine collaborative action scenario input by the user is received; and the action simulation instruction and the description data of the specified multi-machine collaborative action scenario are sent to the action simulation and evaluation interface; The action simulation and evaluation interface is provided, the action simulation instruction and the description data of the specified multi-machine collaborative action scenario are received based on the action simulation and evaluation interface, the specified multi-machine collaborative action scenario is visually simulated according to the action simulation instruction and the node data in the node sequence indicated by the node sequence data, and the simulation process is controlled and managed; during the simulation process, the situation information of the specified multi-machine collaborative action scenario is displayed in the action simulation and evaluation interface, and the simulation results are summarized; after the simulation process is completed, the simulation results are evaluated, and the evaluation results of the specified multi-machine collaborative action scenario are displayed in the action simulation and evaluation interface.

2. The method according to claim 1, characterized in that The method further comprises: Based on the current interface, a tool instruction input by the user is obtained, and the tool instruction corresponds to the designated auxiliary tool; a parameter configuration interface of the designated auxiliary tool is provided according to the tool instruction, and based on the parameter configuration interface of the designated auxiliary tool, an operation parameter corresponding to the designated auxiliary tool input by the user is obtained, and an operation corresponding to the designated auxiliary tool is performed according to the operation parameter of the designated auxiliary tool; The designated auxiliary tools include any one of the following auxiliary tools: distance measuring tool, map resource editing tool, visibility tool, zoom tool, free screenshot tool and visual mode selection tool.

3. The method according to claim 1, characterized in that The map loading configuration parameters include: map loading type parameters, and the adaptive loading of the background map according to the map loading configuration parameters includes: When the map loading type parameter is global map loading, a 3D spherical tile loading algorithm is used to load and display the global map through local loading and drawing, including: taking the current viewport as the center, only loading a preset number of map tiles within the viewport range and at the edge of the viewport at a time; dynamically loading new map tiles and unloading map tiles that are no longer visible according to changes in the viewport; When the map loading type parameter is regional high-definition map loading, a 3D sphere tile loading algorithm is used to load and display the corresponding regional high-definition map by loading and drawing all map tiles within the local area at one time.

4. The method according to claim 1, characterized in that: The model configuration parameters include: team affiliation parameters, the team affiliation parameters include: the first team and the second team, the action scenario configuration parameters include: action rule configuration parameters, action resource configuration parameters and graphic resource configuration parameters, the created assumption model corresponding to the first team is recorded as the assumption model of the first team, and the created assumption model corresponding to the second team is recorded as the assumption model of the second team, the action rule configuration parameters include: action area configuration parameters and action task configuration parameters, the node data include: action area node data, action unit node data, formation node data, action path node data and 2D graphic style node data, the corresponding multi-machine collaborative action scenario is created and deployed according to the action scenario configuration parameters and the created multiple assumption models, and the description data of the multi-machine collaborative action scenario is generated, including: An action area is formed on the background map of the multi-machine collaborative simulation action range according to the action area configuration parameters, and corresponding action area node data is generated, wherein the action area is used to define an area for simulating actions between the action units of the first team and the action units of the second team; The assumption model of the first team and the assumption model of the second team are configured respectively according to the action resource configuration parameters and the action task configuration parameters, and other data in the node data except the action area node data and the 2D graphic style node data are generated, including: through a preset deployment method, according to the node editing parameters in the action resource configuration parameters, based on the assumption model of the first team and the assumption model of the second team, the action units of the first team and the action units of the second team are deployed on the background map of the multi-machine collaborative simulation action range outside the action area, and the action units of the first team and the action units of the second team are configured according to the action task configuration parameters. The unit configures the corresponding action task and generates the corresponding action unit node data; the action units of the first team and the action units of the second team are configured and formed according to the master-slave machine attributes and formation parameters in the action resource configuration parameters, respectively, to form formation nodes corresponding to the first team and the formation nodes of the second team, respectively, and generate corresponding formation node data, wherein the master-slave machine attributes include: master machine attributes and slave machine attributes; according to the action path information in the action resource configuration parameters, the action units of the first team and the action units of the second team are configured with a moving path and a moving speed entering the action area, to form corresponding action path nodes, and generate corresponding action path node data; Setting a 2D graphic style for the multi-machine collaborative action scene according to the graphic resource configuration parameters, forming a corresponding 2D graphic style node, and generating corresponding 2D graphic style node data, wherein the 2D graphic style is used to define a display format of graphic information of the multi-machine collaborative action scene simulation process; The node sequence data is generated according to the generation order of the node data.

5. The method according to claim 4, characterized in that The menu items in the action simulation instruction menu include: play, pause, fast, slow and restart. The action simulation instruction corresponding to the play is used to start the simulation process of the multi-machine collaborative action scene, the action simulation instruction corresponding to the pause is used to pause the simulation process of the multi-machine collaborative action scene, the action simulation instruction corresponding to the fast is used to speed up the simulation process of the multi-machine collaborative action scene, the action simulation instruction corresponding to the slow is used to slow down the simulation process of the multi-machine collaborative action scene, and the action simulation instruction corresponding to the restart is used to restart the simulation process of the multi-machine collaborative action scene; The performing visual simulation of the designated multi-machine coordinated action scenario according to the action simulation instruction and the node data in accordance with the node sequence indicated by the node sequence data includes: When the action simulation instruction is an action simulation instruction corresponding to the play, visual simulation of the specified multi-machine collaborative action scene is performed in sequence according to the node data in the node sequence indicated by the node sequence data, including: Performing a visual simulation of the movement path of the action unit entering the action area according to the action path node data; According to the action unit node data, after the action unit enters the action area, the interactive behavior and action path between the action units of different teams are visually simulated, and after leaving the action area, the behavior status of the action unit is visually simulated.

6. The method according to claim 4, characterized in that The control and management of the simulation process includes: Managing the life cycle of the action unit according to the action unit node data and the action area node data; Managing data communication for coordinated actions between action units of the same team according to the formation node data; The action state of the action unit during the simulation is obtained, and the interactive behavior of the action unit is controlled according to the action state of the action unit to generate an optimized moving path.

7. The method according to claim 6, characterized in that The action states include: a start-up response state, an evasive state, and a circling state. The controlling of the interactive behavior of the action unit and the generation of an optimized moving path according to the action state of the action unit include: When the action state of the action unit is to initiate the response state, a detour response curve movement path is generated in the following manner: Calculate the dot product of the left vertical direction and the right vertical direction of the action unit and the action target direction respectively, and select the direction where the dot product result is less than 0 as the detour offset direction f; The dot product of the direction of the action unit and the direction of the action target is calculated and the modulus is obtained. The offset weight r is calculated according to the modulus m and the distance l between the action unit and the action target by the following formula: ； Wherein, a is the distance between the action unit and the action target when the direction of the action unit is opposite to the direction of the action target and the offset weight r is 1; The third point P3 is calculated according to the coordinate point P1 of the action unit and the coordinate point P2 of the action target by the following formula: ； Where b is a constant; Calculate the detour corresponding curve moving path using a third-order Bezier curve algorithm according to the coordinate point P1 of the action unit, the coordinate point P2 of the action target and the third point P3; When it is determined that the action unit has found the action target and meets the action conditions, it is determined whether the action unit carries an automatic navigation aircraft and a target detector. If so, the action unit is controlled to launch the automatic navigation aircraft, and the position information of the action target is sent to the automatic navigation aircraft by controlling the target detector carried by the action unit, so that the automatic navigation aircraft corrects its own direction according to the received position information of the action target and flies in the direction of the action target; when it is determined that the total flight distance of the automatic navigation aircraft is greater than the maximum range in the automatic navigation aircraft's own attributes, it is regarded as a target recognition failure; when the automatic navigation aircraft fails to receive the position information of the action target within a preset time, the automatic navigation aircraft is controlled to fly in the current direction until the state changes; when it is determined that the automatic navigation aircraft successfully touches the action target during flight, the state change probability is calculated according to the necessary action distance, effective action distance and maximum action distance in the automatic navigation aircraft's own attributes, and whether the state of the action target has changed is determined according to the state change probability; The target detector carried by the action unit is used to determine whether the action unit has found the action target in the following manner: a spherical scanning interval is generated with the current coordinate point of the target detector as the sphere center and the detection distance of the target detector as the radius, and the action target within the spherical scanning interval is acquired at a preset period; the action target is determined to be scanned by the target detector according to the current position of the target detector, the scanning angle of the target detector and the current position of the action target; if it is determined that the action target is scanned by the target detector, it is determined that the action unit has found the action target; When the action state of the action unit is in the evading state, the left and right evading curved movement paths are generated in the following manner: Calculate the dot product of the direction of the action unit and the left vertical direction and the right vertical direction of the avoidance object respectively, and select the direction where the dot product result is greater than 0 as the avoidance offset direction; The coordinate point of the action unit is taken as the first point, the coordinate point at the first preset distance in the avoidance offset direction of the coordinate point of the action unit is taken as the second point, the coordinate point at the center point between the first point and the second point and at the second preset distance lower in height is taken as the third point, and the left and right avoidance curve movement path is calculated using a third-order Bezier curve algorithm according to the first point, the second point and the third point; When the action state of the action unit is circling, a free circling curve movement path is generated in the following manner: According to the action area configuration parameters, half of the distance between the two closest parallel sides in the action area is calculated to obtain the maximum radius of the circling path. ; The coordinate point of the action unit is taken as the first point, and the coordinate point of the action unit is rotated clockwise by a first preset angle around the center point of the action area, and the distance from the center point of the action area to the center point of the action area is 0 to as the second point, the coordinate point of the action unit rotated clockwise around the center point of the action area by a second preset angle and the coordinate point RP3 away from the center point of the action area is taken as the third point, and the free spiral curve moving path is calculated using the third-order Bezier curve algorithm according to the first point, the second point and the third point; wherein, ； is the distance from the first point to the center point of the action area, is the distance from the second point to the center point of the action area.

8. The method according to claim 4, characterized in that The displaying of the situation information of the specified multi-machine coordinated action scenario in the action simulation and evaluation interface includes: Displaying the situation information of the specified multi-machine coordinated action scenario in the action simulation and evaluation interface in the 2D graphic style indicated by the 2D graphic style node data; The simulation results are summarized, including: Recording the loss type, quantity and amount of the action units and carried equipment corresponding to the first team and the second team in the specified multi-machine coordinated action scenario, wherein the carried equipment includes: automatic navigation aircraft and projectiles; The evaluating of the simulation results comprises: According to the loss type, quantity and amount of the action units and the equipment carried by the first team and the second team, the synergy effectiveness between the action units of the same team is evaluated, and the effectiveness analysis result of the formation between the action units of the first team and the second team is obtained, and the effectiveness analysis result is used as the evaluation result.

9. A model-based multi-machine collaborative action simulation and evaluation system, characterized in that: The simulation and evaluation system includes: a map resource construction module, a scenario model editing module, a multi-machine collaborative action scenario deployment module and an action simulation and evaluation module, wherein: The map resource construction module is configured to: adaptively load and display the background map according to the map loading configuration parameters, and provide geographic information data of the background map; The assumption model editing module is configured to: provide an assumption model editing interface, obtain model configuration parameters input by a user based on the assumption model editing interface; create a corresponding assumption model according to the model configuration parameters, or update the created assumption model; The multi-machine collaborative action scenario deployment module is configured to: provide a multi-machine collaborative simulation action range selection interface, obtain the multi-machine collaborative simulation action range configuration parameters input by the user based on the multi-machine collaborative simulation action range selection interface, and load and display the corresponding multi-machine collaborative simulation action range background map based on the multi-machine collaborative simulation action range configuration parameters; provide a multi-machine collaborative action scenario deployment interface based on the multi-machine collaborative simulation action range background map, obtain the action scenario configuration parameters input by the user based on the multi-machine collaborative action scenario deployment interface, create and deploy the corresponding multi-machine collaborative action scenario according to the action scenario configuration parameters and the created multiple assumption models, and generate the description data of the multi-machine collaborative action scenario; wherein the description data of the multi-machine collaborative action scenario includes: node data and node sequence data, the multi-machine collaborative action scenario deployment interface includes an action simulation instruction menu, receives the action simulation instruction corresponding to the specified multi-machine collaborative action scenario input by the user based on the action simulation instruction menu, and sends the action simulation instruction and the description data of the specified multi-machine collaborative action scenario to the action simulation and evaluation interface; The action simulation and evaluation module is configured to: provide the action simulation and evaluation interface, receive the action simulation instructions and the description data of the specified multi-machine collaborative action scenario based on the action simulation and evaluation interface, perform visual simulation of the specified multi-machine collaborative action scenario according to the action simulation instructions and the node data in the node sequence indicated by the node sequence data, and control and manage the simulation process; during the simulation process, display the situation information of the specified multi-machine collaborative action scenario in the action simulation and evaluation interface, and summarize the simulation results; when the simulation process is completed, evaluate the simulation results, and display the evaluation results of the specified multi-machine collaborative action scenario in the action simulation and evaluation interface.

10. The simulation and evaluation system according to claim 9, characterized in that: The simulation and evaluation system further includes: an auxiliary tool module, The auxiliary tool module is configured to: obtain a tool instruction input by a user based on a current interface, wherein the tool instruction corresponds to a designated auxiliary tool; provide a parameter configuration interface of the designated auxiliary tool according to the tool instruction, obtain an operation parameter corresponding to the designated auxiliary tool input by a user based on the parameter configuration interface of the designated auxiliary tool, and perform an operation corresponding to the designated auxiliary tool according to the operation parameter of the designated auxiliary tool; The designated auxiliary tools include any one of the following auxiliary tools: distance measuring tool, map resource editing tool, visibility tool, zoom tool, free screenshot tool and visual mode selection tool.

Images