Rule-based target flight mission autonomous generation expert system and implementation method
Through the independent generation expert system for target flight missions based on rules, the problem of lack of diversity and flexibility in Party B's flight methods is solved, the systematization and intelligence of Party B's flight missions are realized, the capabilities of Party B are enhanced, and the needs of AB's game confrontation are met.
Patent Information
- Application Number
- CN202411704055.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-26
- Publication Date
- 2025-04-15
AI Technical Summary
In the existing technology, Party B's flight methods lack diversity, flexibility, systematization and intelligence, and cannot meet the needs of A and B's game confrontation. Especially when Party A's capabilities are improved, Party B's flight missions are insufficient.
It provides a specialist system for autonomous generation of target flight missions based on rules. It obtains flight element information through the scene information input module. The area range constraint module provides a variety of flight mission area ranges. The flight style construction module generates flight styles. The flight mission generation module automatically generates and checks the tasks. The mission information output module outputs the generated flight mission results.
It has realized the diversity, flexibility, systematization and intelligence of Party B's flight missions, enhanced Party B's flight capabilities, better met the needs of game confrontation, and verified Party A's equipment and personnel capabilities.
Smart Images
Figure CN119830520A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of regional simulation training, and in particular to a rule-based target flight mission autonomous generation expert system and an implementation method thereof. Background Art
[0002] In the current scene of a certain field, the requirements for the game confrontation between parties A and B are gradually increasing. In the game confrontation between parties A and B, the two parties have different behaviors, that is, the systems, positioning, functions and evaluation methods of the two parties are all different; in the process of modeling and simulation, when the ability of party A is improved, party B should also increase flexibility, diversity and other capabilities to make the scene richer, that is, it is equally important to improve the systematization and intelligence capabilities of party B's flight missions and strengthen the construction of party A's equipment. However, the flight method of party B in the current system does not have the characteristics of diversity, flexibility, systematization, intelligence, etc. Therefore, in order to improve the ability of party B in the system and meet the needs of game confrontation, the present invention proposes a rule-based, user-friendly configuration and realistic method for generating behavior for party B's autonomous flight missions. It can make up for the defects of party B's flight capabilities, make the process in a certain field more comprehensive and rich, and better verify the capabilities of party A's equipment and personnel. Summary of the invention
[0003] In view of the defects in the prior art, the purpose of the present invention is to provide an expert system for autonomously generating target flight missions based on rules and an implementation method thereof.
[0004] A method for implementing a rule-based target flight mission autonomous generation expert system according to the present invention includes:
[0005] Step S1: Obtain flight element information in the scene through the scene information input module, including: target type, B point location information, B point target quantity, A key point location information;
[0006] Step S2: providing area ranges for various flight missions through an area range constraint module;
[0007] Step S3: providing a preset typical task construction method and task template through the flight style construction module to generate a corresponding flight style;
[0008] Step S4: automatically generating a flight mission according to the selected area range and flight style through a flight mission generation module;
[0009] Step S5: Specify the JSON file format through the task information output module to output the generated flight mission results. When adding flight missions for Party B to the scene, batch additions can be made.
[0010] Preferably, the step S2 adopts: providing the first to fourth regions and the global range as the regional scale of the current scene through the regional range constraint module; after the flight range is confirmed, screening the available mission equipment and the starting and ending flight mission elements within the current regional range;
[0011] Among them, the second area includes the first area; the third area includes the second area; the fourth area includes the third area; and the global scope includes the fourth area.
[0012] Preferably, the step S3 adopts: providing a task template through a flight style construction module to obtain task elements; selecting an end point coverage style, and selecting a construction method that can eventually cover all of it based on a preset typical task construction method to generate a corresponding flight style.
[0013] Preferably, step S4 adopts: checking the flight mission through the flight mission generation module, using the constraints constructed based on the target attributes in the target library in the constraint checking module to determine whether the flight mission conforms to the physical laws in reality, and retaining the flight missions that meet the preset requirements.
[0014] Preferably, the flight style construction module comprises:
[0015] Mission template construction method submodule: Generate a specified flight mission based on the starting point of Party B, target type and the end point of Party A within the acquired area;
[0016] End point coverage construction method submodule: set a priority for each selected A end point and generate flight tasks in order of priority;
[0017] The preset typical task construction methods include: long-term continuous coverage mode, instantaneous full coverage mode, and link node coverage mode.
[0018] Preferably, the flight mission generation module includes: determining whether the target distance is within the range of the maximum distance to the minimum distance of the target by calculating the distance between the starting point and the end point, checking whether the target affiliation and the camp are consistent with the starting point, checking whether the remaining number of targets in the B point is sufficient, checking whether the starting point selected by the user can generate a reasonable task, and checking whether the randomly selected task elements are in line with the actual situation;
[0019] Preferably, the step of determining whether the target distance is within a range from a maximum distance to a minimum distance of the target by calculating the distances between the starting point and the end point includes:
[0020] When calculating the distance between the starting point and the end point, use the cosine theorem of a spherical triangle
[0021] dis=
[0022] acos(sin(lat1)*sin(lat2)+cos(lat1)*cos(lat2)*cos(lon2-lon1))*R
[0023] Where dis represents the distance between the starting point and the end point, lat1 and lat2 are the latitudes of the starting point and the end point respectively, in radians, lon1 and lon2 are the longitudes of the two points, and R represents the radius of the earth.
[0024] Preferably, the flight mission generation module also includes: each Party B target should determine the actual take-off time in the simulation scene. This module combines multiple factors such as the current moment of the scene, flight mission style, mission end time requirements, target distance, and flight speed to calculate the take-off time for each target in each mission in the simulation scene.
[0025] According to the present invention, a rule-based target flight mission autonomous generation expert system is provided, comprising:
[0026] Scene information input module: obtains flight element information in the scene, including: target type, B-side point location information, B-side point target quantity, A-side key point location information;
[0027] Area range constraint module: provides the area range of various flight missions;
[0028] Flight style construction module: provides preset typical task construction methods and task templates to generate corresponding flight styles;
[0029] Flight mission generation module: automatically generates flight missions based on the selected area and flight style;
[0030] Mission information output module: specifies the JSON file format and outputs the generated flight mission results. When adding flight missions for Party B to the scene, batches can be added.
[0031] Preferably, the area range constraint module includes: providing the first area to the fourth area and the global range as the area scale of the current scene; after the flight range is confirmed, screening the available mission equipment and the start and end point flight mission elements within the current area;
[0032] The second region includes the first region; the third region includes the second region; the fourth region includes the third region; and the global scope includes the fourth region;
[0033] The flight style construction module includes: providing a task template and obtaining task elements; selecting an end point coverage style, selecting a construction method that can eventually cover all of the tasks based on a preset typical task construction method, and generating a corresponding flight style;
[0034] The flight mission generation module includes: checking the flight mission, using the constraints constructed in the constraint checking module based on the target attributes in the target library to determine whether the flight mission conforms to the physical laws in reality, and retaining the flight missions that meet the preset requirements.
[0035] Compared with the prior art, the present invention has the following beneficial effects:
[0036] 1. According to the area range selection module provided by the present invention, the technical effect of directly determining the impact range of the flight mission can be achieved, making the flight mission closer to reality; according to the mission template provided by the present invention, accurate flight mission generation can be achieved; according to the terminal coverage flight style provided by the present invention, the effect of automatically generating flight missions and performing rational checks can be achieved, omitting the complex mission configuration process; according to the typical style provided by the present invention, a number of flight missions of a certain scale can be automatically generated, omitting complex steps such as considering the mission effective time.
[0037] 2. According to a rule-based target flight mission autonomous generation expert system and implementation method provided by the present invention, the task generation process is closer to the real process and has stronger usability; fewer task elements need to be configured for task generation, and the generation process is more diversified and intelligent; the task generation effect is better, and typical styles provide a mature flight system; it is more flexible and can provide exclusive task plans based on scenario input. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:
[0039] Figure 1 Autonomously generate expert system schematics for rule-based target flight missions.
[0040] Figure 2 Overrides the constructor flow chart for the endpoint.
[0041] Figure 3 A flowchart of the typical style construction method. DETAILED DESCRIPTION
[0042] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.
[0043] Example 1
[0044] According to the present invention, a rule-based target flight mission autonomous generation expert system is applicable to the B camp in a certain field. Only a small amount of information input such as the A and B camps in the scene and the target model and quantity and simple operation steps such as flight mode selection can generate flexible and diverse flight missions that meet the reality.
[0045] The rule-based target flight mission autonomous generation expert system, such as Figure 1 As shown, including:
[0046] The regional range constraint module provides the first to fourth regions and the global range as the regional scale of the current scene. After the flight range is confirmed, it automatically selects the available mission equipment, starting point and end point and other flight mission elements within the current regional range;
[0047] The flight style construction module provides task templates, which is a construction method for manually entering task elements; endpoint coverage, which is a construction method in which users select party A’s endpoints in batches and the system automatically generates tasks that can eventually cover all of them; typical styles, which is a construction method for setting different flight modes for different scenarios. Generate corresponding flight styles based on the construction algorithm;
[0048] The flight mission generation module checks the flight missions generated by the construction algorithm, uses constraints constructed based on target attributes in the target library, determines whether the flight missions conform to the physical laws in reality, and retains reasonable and feasible flight missions.
[0049] The scene information input module obtains flight element information such as target type, B-party point location information, B-party point target quantity, A-party key point location, etc. in the scene through object-oriented JSON files;
[0050] The mission information output module develops a clear and readable JSON file format based on the principles of hierarchy and generalization, and returns the flight missions generated by the system to the scene in a unified and standardized manner. When adding new flight missions for Party B to the scene, they can be added in batches.
[0051] The flight style construction module includes: a task template construction method module, which allows manual entry of the B-party point, target type, and A-party point target within the area to generate a specified flight mission. The user needs to select the filtered starting point, end point, and target information. The system will determine whether the selection is reasonable based on constraints such as distance, camp, and type. If there is a conflict, an error message will be returned to prompt the user to reselect. If the selection is reasonable, the mission effective time can be calculated, and after adding a short delay, it can be directly added to the simulation scene.
[0052] The end point coverage construction method module allows batch selection of Party A’s end points, and the system will automatically generate flight missions that can cover all the end points. Furthermore, the system allows setting a priority for each selected Party A flight target, and when generating flight missions, the Party A flight targets with higher priorities will be given priority. The specific algorithm flow can be seen in Figure 2 First, obtain all the destination information selected by the user, including its location information, i.e., longitude and latitude, and the destination priority. Then perform a destination constraint check, i.e., whether there is at least one set of tasks in the scene that can take off to the specified destination. If not, report an error message and prompt the user to reselect the destination. After determining the destination information, calculate the number of tasks corresponding to each destination according to the priority given in advance. After determining the number of tasks, randomly select the starting point and target type to form a flight mission. Before adding a task to the scene, it will also check whether it meets the constraints of realistic conditions. For reasonable and feasible tasks, the time will be calculated and then added to the simulation scene.
[0053] The typical style construction method module includes three typical flight styles, namely long-term continuous coverage mode, instantaneous full coverage mode, and link node coverage mode. The flight styles are generated according to the construction algorithms of different styles. Among them, long-term continuous coverage is used for the key forces of Party A, which can provide 12 hours of long-term continuous coverage, with a smaller coverage range and more accurate coverage targets; the instantaneous full coverage mode is used for the key areas of Party A's key cities, which can cover more targets in a shorter time; the link node coverage mode is used for Party A's backbone nodes, which can provide coverage of backbone nodes and possible nodes around them, with a more comprehensive coverage range and certain redundancy.
[0054] When generating a typical flight mission, the process is visible Figure 3 . The user is prompted to perform long-term continuous mode, instantaneous comprehensive mode or link node selection mode. After determining the generation mode, the system will check whether there are corresponding flight mission targets. For the long-term continuous coverage mode, key force targets are required in the scene; for the instantaneous comprehensive coverage mode, key cities are required as targets in the scene; for the link node coverage mode, important nodes are required as targets in the scene. After determining that there are available targets, the system will automatically generate a series of flight missions. For the long-term continuous mode, no less than 100 high-precision, small-range tasks will be generated, and flight missions within a range of 5km near key forces will be continuously carried out within 12 hours; for the comprehensive instantaneous mode, no less than 30 tasks will be generated, and flight missions within a range of 20km near key cities will be carried out within 5 minutes; for the link node mode, no less than 50 tasks will be generated, and flight missions to link nodes within a range of 150km near key nodes will be carried out within 30 minutes. After the task is generated, a constraint check will be performed on each task, and reasonable and feasible tasks will be added to the simulation scene.
[0055] When calculating the distance constraint, first calculate the distance between the start point and the end point, and then determine whether the distance is within the distance range of the selected target model. If it is within the range, the constraint requirement is met, otherwise the task element needs to be reselected. When calculating the distance between the start point and the end point, the cosine theorem of the spherical triangle is used, and the formula is as follows:
[0056] dis=
[0057] acos(sin(lat1)*sin(lat2)+cos(lat1)*cos(lat2)*cos(lon2-lon1))*R
[0058] Where dis represents the distance between the starting point and the end point, lat1 and lat2 are the latitudes of the starting point and the end point respectively, in radians, lon1 and lon2 are the longitudes of the two points, and R represents the radius of the earth.
[0059] Example 2
[0060] A method for implementing a rule-based target flight mission autonomous generation expert system according to the present invention includes:
[0061] Step 1: Use the JSON file to obtain flight element information such as the target type in the scene, the location information of Party B's points, the number of Party B's point targets, and the location of Party A's key points.
[0062] Step 2: The user selects the scene area scale, including the first to fourth areas and the global range.
[0063] Step 3: The system automatically selects available mission equipment, starting point, end point and other flight mission elements based on the scenario range selected by the user.
[0064] Step 4: The user selects the flight style construction method.
[0065] Step 5: Select the task template construction method:
[0066] S1: The user enters the starting point of Party B, the target type, and the end point of Party A within the area to generate a designated flight mission.
[0067] S2: The system performs constraint checks to confirm whether the specified task meets realistic conditions.
[0068] S3: If the constraint check passes, the time recording task is calculated.
[0069] S4: If the constraint check fails, return to S1 to prompt the user to make a new selection.
[0070] Step 6: Select the endpoint coverage construction method:
[0071] S1: The user selects the endpoints of Party A in batches and specifies the priority
[0072] S2: The system checks whether all points have the ability to generate valid tasks.
[0073] S3: If some points cannot be checked, the system returns to S1 to prompt the user to add additional options.
[0074] S4: The system considers the mission priority, automatically generates the flight mission, calculates the mission effective time and records the mission.
[0075] Step 7: Select a typical style construction method:
[0076] Step 8: Select from the three typical modes: long-term continuous coverage mode, instantaneous full coverage mode, and link node coverage mode.
[0077] S1: Select long-term continuous coverage mode:
[0078] a: The system checks whether there is a key force target in the scene.
[0079] b: If not, return to a to prompt an error message and ask the user to make a new selection.
[0080] c: The system selects high-precision targets and generates no less than 100 flight missions with a duration of 12 hours within 5 km of key forces.
[0081] S2: Select instant full coverage mode:
[0082] a: The system checks whether there are key city targets in the scene.
[0083] b: If not, return to a to prompt an error message and ask the user to make a new selection.
[0084] c: The system selects high-energy targets and generates no less than 30 flight missions within 20km of key forces with a landing time difference within 5 minutes.
[0085] S3: Select link node coverage mode:
[0086] a: The system checks whether there are backbone node targets in the scene.
[0087] b: If not, return to a to prompt an error message and ask the user to make a new selection.
[0088] c: The system selects high-energy targets and generates no less than 50 flight missions within 150km of key forces with a landing time difference within 30 minutes.
[0089] Step 9: Return the task to the scene via a JSON file.
[0090] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.
[0091] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.
Claims
1. A method for implementing a rule-based target flight mission autonomous generation expert system, characterized in that: include: Step S1: Obtain flight element information in the scene through the scene information input module, including: target type, B point location information, B point target quantity, A key point location information; Step S2: providing area ranges for various flight missions through an area range constraint module; Step S3: providing a preset typical task construction method and task template through the flight style construction module to generate a corresponding flight style; Step S4: automatically generating a flight mission according to the selected area range and flight style through a flight mission generation module; Step S5: Specify the JSON file format through the task information output module to output the generated flight mission results. When adding flight missions for Party B to the scene, batch additions can be made.
2. The method for implementing the rule-based target flight mission autonomous generation expert system according to claim 1, characterized in that: The step S2 adopts: providing the first to fourth regions and the global range as the regional scale of the current scene through the regional range constraint module; after the flight range is confirmed, screening the available mission equipment and the starting and ending flight mission elements within the current regional range; Among them, the second area includes the first area; the third area includes the second area; the fourth area includes the third area; and the global scope includes the fourth area.
3. The method for implementing the rule-based target flight mission autonomous generation expert system according to claim 1, characterized in that: The step S3 adopts: providing a task template through a flight style construction module to obtain task elements; Select the end point coverage style, select the construction method that can eventually cover it all based on the preset typical task construction method, and generate the corresponding flight style.
4. The method for implementing the rule-based target flight mission autonomous generation expert system according to claim 1, characterized in that: The step S4 adopts: checking the flight mission through the flight mission generation module, using the constraints constructed based on the target attributes in the target library in the constraint checking module to determine whether the flight mission conforms to the physical laws in reality, and retaining the flight missions that meet the preset requirements.
5. The method for implementing the rule-based target flight mission autonomous generation expert system according to claim 1, characterized in that: The flight style construction module includes: Mission template construction method submodule: Generate a specified flight mission based on the starting point of Party B, target type and the end point of Party A within the acquired area; End point coverage construction method submodule: set a priority for each selected A end point and generate flight tasks in order of priority; The preset typical task construction methods include: long-term continuous coverage mode, instantaneous full coverage mode, and link node coverage mode.
6. The method for realizing the rule-based target flight mission autonomous generation expert system according to claim 4, characterized in that: The flight mission generation module includes: determining whether the target distance is within the range of the maximum distance to the minimum distance of the target by calculating the distance between the starting point and the end point, checking whether the target affiliation and the camp are consistent with the starting point, checking whether the remaining number of targets in the B-party point is sufficient, checking whether the starting point selected by the user can generate a reasonable task, and checking whether the randomly selected task elements are in line with the actual situation.
7. The method for implementing the rule-based target flight mission autonomous generation expert system according to claim 6, characterized in that: The step of determining whether the target distance is within a range from a maximum distance to a minimum distance of the target by calculating the distances between the starting point and the end point includes: When calculating the distance between the starting point and the end point, use the cosine theorem of a spherical triangle dis= acos(sin(lat1)*sin(lat2)+cos(lat1)*cos(lat2)*cos(lon2-lon1))*R Where dis represents the distance between the starting point and the end point, lat1 and lat2 are the latitudes of the starting point and the end point respectively, in radians, lon1 and lon2 are the longitudes of the two points, and R represents the radius of the earth.
8. The method for realizing the rule-based target flight mission autonomous generation expert system according to claim 1, characterized in that: The flight mission generation module also includes: each Party B target should determine the actual take-off time in the simulation scene. This module combines multiple factors such as the current moment of the scene, flight mission style, mission end time requirements, target distance, and flight speed to calculate the take-off time of each target in each mission in the simulation scene.
9. A rule-based target flight mission autonomous generation expert system implementation system, characterized in that: include: Scene information input module: obtains flight element information in the scene, including: target type, B-side point location information, B-side point target quantity, A-side key point location information; Area range constraint module: provides the area range of various flight missions; Flight style construction module: provides preset typical task construction methods and task templates to generate corresponding flight styles; Flight mission generation module: automatically generates flight missions based on the selected area and flight style; Mission information output module: specifies the JSON file format and outputs the generated flight mission results. When adding flight missions for Party B to the scene, batches can be added.
10. The rule-based target flight mission autonomous generation expert system according to claim 9, characterized in that: The regional range constraint module includes: providing the first to fourth regions and the global range as the regional scale of the current scene; after the flight range is confirmed, screening the available mission equipment and the starting and ending flight mission elements within the current regional range; The second region includes the first region; the third region includes the second region; the fourth region includes the third region; and the global scope includes the fourth region; The flight style construction module includes: providing a task template and obtaining task elements; selecting an end point coverage style, selecting a construction method that can eventually cover all of the tasks based on a preset typical task construction method, and generating a corresponding flight style; The flight mission generation module includes: checking the flight mission, using the constraints constructed in the constraint checking module based on the target attributes in the target library to determine whether the flight mission conforms to the physical laws in reality, and retaining the flight missions that meet the preset requirements.