A method and device for multi-agent motion modeling based on environment

Abstract

The present invention discloses a method and apparatus for environment-based multi-agent motion modeling. The method comprises: obtaining task requirement information; constructing a multi-agent dynamic environment management model; and processing the task requirement information based on the multi-agent dynamic environment management model to obtain simulation result information. By utilizing the multi-agent dynamic environment management model and a multi-agent adaptive motion solution model, the present invention achieves high-precision prediction and simulation of the formation-maintaining motion state of multi-agents under different environmental conditions, thereby improving the authenticity and application value of simulations.

Description

Technical Field

[0001] The present invention relates to the field of modeling and simulation, and in particular to an environment-based multi-agent motion modeling method and device. Background Art

[0002] In the current field of simulation modeling, traditional motion modeling typically focuses on analyzing the behavior of a single object, easily overlooking the direct impact of environmental factors and other agents on its motion state. For example, when ground vehicles move, they typically need to follow roads and maintain specific spacing, while also taking into account factors such as changing road conditions. To address this limitation, this paper introduces the concept of environment dominance, utilizing a multi-agent dynamic environment management model to dynamically manage and adjust the route information of each agent. While fully considering environmental factors, the multi-agent motion solution model is dispatched on demand, thereby improving the realism and application value of simulations. Summary of the Invention

[0003] The technical problem to be solved by the present invention is that the present invention provides an environment-based multi-agent motion modeling method and device, which utilizes a multi-agent dynamic environment management model and a multi-agent adaptive motion solution model to achieve high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0004] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a multi-agent motion modeling method based on an environment, the method comprising:

[0005] S1, obtain task requirement information;

[0006] S2, building a multi-agent dynamic environment management model;

[0007] S3, based on the multi-agent dynamic environment management model, processing the task requirement information to obtain simulation result information.

[0008] As an optional implementation, in the first aspect of the embodiment of the present invention, the step of constructing a multi-agent dynamic environment management model includes:

[0009] S21, obtaining the motion input interface requirement information of the intelligent agent;

[0010] S22, obtaining the position status output interface requirement information of the intelligent agent;

[0011] S23, obtaining movement route information of the intelligent agent based on the basic environment dynamic database;

[0012] S24, constructing a multi-agent dynamic environment management model based on the motion input interface requirement information, the position state output interface requirement information and the agent motion route information.

[0013] As an optional implementation, in the first aspect of the embodiment of the present invention, the processing of the task requirement information based on the multi-agent dynamic environment management model to obtain simulation result information includes:

[0014] S31, parsing the task requirement information to obtain movement request information;

[0015] The movement request information includes starting point information, destination point information, travel route information, travel formation information and task time information;

[0016] S32: Based on the multi-agent dynamic environment management model, the motion request information is processed to obtain simulation result information.

[0017] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the processing of the motion request information based on the multi-agent dynamic environment management model to obtain simulation result information includes:

[0018] S321, using the travel route information to match a basic environment dynamic database to obtain road information and terrain information;

[0019] S322, calculating movement time information using the road information, the terrain information, and the agent performance information;

[0020] S323, based on the multi-agent dynamic environment management model, performing calculation processing on the motion time information and the agent performance information to obtain calculated position information of the agent;

[0021] S324: Process the calculated position information of the intelligent agent to obtain simulation result information.

[0022] As an optional implementation, in the first aspect of the embodiment of the present invention, the step of calculating the motion time information and the agent performance information based on the multi-agent dynamic environment management model to obtain the agent calculated position information includes:

[0023] S3231, obtaining the marching formation information;

[0024] S3232, parsing the traveling formation information to obtain the total number of vehicles in the traveling formation;

[0025] S3233, using the multi-agent dynamic environment management model, obtaining a moving vehicle information set;

[0026] S3234, based on the multi-agent dynamic environment management model, using the multi-agent adaptive motion solution model, the moving vehicle information set and the total value of the moving fleet vehicles are processed to obtain the agent expected position information and the agent solved position information.

[0027] As an optional implementation, in the first aspect of the embodiment of the present invention, the multi-agent dynamic environment management model is used to process the moving vehicle information set and the total number of vehicles in the moving fleet using a multi-agent adaptive motion solution model to obtain the agent expected position information and the agent solved position information, including:

[0028] S32341, obtain current time information;

[0029] S32342, using the current time information, calculate and obtain vehicle travel time information;

[0030] S32343, calculating and processing the vehicle travel time information and the travel vehicle information set to obtain vehicle position information;

[0031] S32344, using the vehicle location information to match the basic environment dynamic database to obtain current road status information;

[0032] S32345, determining whether the current road state information value is equal to a first preset value, and obtaining a first road state determination value;

[0033] S32346: When the first road state judgment value is yes, the vehicle travel time information and the travel vehicle information set are processed based on the first multi-intelligence motion solver model to obtain the agent's expected position information and the agent's solved position information;

[0034] When the first road state judgment value is no, executing S32347;

[0035] S32347, determining whether the current road state information value is equal to a second preset value, and obtaining a second road state determination value;

[0036] When the second road state judgment value is yes, processing the vehicle travel time information and the travel vehicle information set based on the second multi-intelligent motion solver model to obtain the agent expected position information and the agent solved position information;

[0037] When the second road state judgment value is no, executing S32348;

[0038] S32348, determining whether the current road state information value is equal to a third preset value, and obtaining a third road state determination value;

[0039] When the third road state judgment value is yes, the vehicle travel time information and the travel vehicle information set are processed based on the first multi-intelligence motion solver model to obtain the agent expected position information and the agent solved position information;

[0040] When the third road state judgment value is negative, executing S32349;

[0041] S32349, obtain current road obstacle information, and update the current road obstacle information to the basic environment dynamic database, and execute S321.

[0042] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the first multi-intelligent motion solver model expression is:

[0043]

[0044] in, represents the expected position of the i-th vehicle; represents the calculated position of the i-th vehicle; represents the initial position vector of the i-th vehicle, represents the maneuvering direction unit vector of the i-th vehicle, V represents the maneuvering speed; T i represents the maneuvering time of the i-th vehicle; i represents the vehicle index in the said marching formation; Represents the unit vector of the formation direction; S 间距 Indicates the spacing between formations;

[0045] The second multi-intelligent motion solver model expression is:

[0046]

[0047] in, represents the expected position of the i-th vehicle; represents the calculated position of the i-th vehicle; V represents the maneuvering speed represents the maneuvering direction unit vector of the i-th vehicle; ΔT i represents the integration step of the i-th vehicle; a represents the initial value of the integration; b represents the end value of the integration.

[0048] A second aspect of an embodiment of the present invention discloses an environment-based multi-agent motion modeling device, the device comprising: a data acquisition module, a management model building module, and a simulation processing module;

[0049] The data acquisition module is used to obtain task requirement information;

[0050] The management model construction module is used to construct a multi-agent dynamic environment management model;

[0051] The simulation processing module is used to process the task requirement information based on the multi-agent dynamic environment management model to obtain simulation result information.

[0052] A third aspect of the present invention discloses another environment-based multi-agent motion modeling device, the device comprising:

[0053] a memory storing executable program code;

[0054] a processor coupled to the memory;

[0055] The processor calls the executable program code stored in the memory to execute part or all of the steps in the environment-based multi-agent motion modeling method disclosed in the first aspect of the embodiment of the present invention.

[0056] The fourth aspect of the present invention discloses a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, and when the computer instructions are called, they execute part or all of the steps in the environment-based multi-agent motion modeling method disclosed in the first aspect of the embodiment of the present invention.

[0057] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0058] In an embodiment of the present invention, a multi-agent dynamic environment management model is used to dynamically manage and adjust the route information of each agent, and a multi-agent adaptive motion solution model is used to schedule the motion solution algorithm of the multi-agent on demand according to the environmental information, thereby achieving high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 1 is a schematic diagram of a scenario of an environment-based multi-agent motion modeling device provided by an embodiment of the present invention;

[0061] Figure 2 This is a flow chart of an environment-based multi-agent motion modeling method disclosed in an embodiment of the present invention;

[0062] Figure 3 1 is a schematic structural diagram of an environment-based multi-agent motion modeling device disclosed in an embodiment of the present invention;

[0063] Figure 4 It is a structural diagram of another environment-based multi-agent motion modeling device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0066] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0067] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that one of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0068] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, the corresponding data exist for the computer device to process. The details will not be repeated here.

[0069] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making.

[0070] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0071] Computer vision (CV) is the science of making machines "see." Specifically, it refers to machine vision, where cameras and computers replace the human eye in identifying and measuring objects, performing further image processing to create images more suitable for human observation or transmission to instrumentation. As a scientific discipline, computer vision studies related theories and technologies, aiming to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, optical character recognition (OCR), video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0072] Unimodal information is data consisting of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require integrating multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can often be achieved on the task.

[0073] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are generally used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In the embodiment of the present application, the large model can be ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Qianyi Tongwen model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, Wenxin Yiyan and other large-scale language models, which are not limited in the embodiment of the present application.

[0074] The embodiments of the present application provide an environment-based multi-agent motion modeling method, system, apparatus, computer equipment, and computer-readable storage medium, which are described in detail below.

[0075] See also Figure 1 , Figure 1 This is a scene diagram of a multi-agent motion analysis and evaluation system provided in an embodiment of the present application. The evaluation and analysis system may include a computer device 100, in which an environment-based multi-agent motion modeling device is integrated, such as Figure 1 Computer equipment in.

[0076] In the embodiments of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. A cloud server is composed of a large number of computers or network servers based on cloud computing.

[0077] It is understood that the computer device 100 used in the embodiments of the present application can be a device that includes both receiving and transmitting hardware, that is, a device that has receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device that has a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. The specific computer device 100 can be a desktop terminal or a mobile terminal. The computer device 100 can also be a mobile phone, a tablet computer, a laptop computer, etc.

[0078] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computer devices as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the system can also include one or more other services, which are not limited here.

[0079] In addition, if Figure 1 As shown, the evaluation and analysis system may further include a memory 200 for storing simulation data, such as expert database data and simulation result data.

[0080] It should be noted that Figure 1 The scenario diagram of the evaluation and analysis system shown is only an example. The evaluation and analysis system and scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided by the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the simulation control management system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present application is also applicable to similar technical problems.

[0081] This invention discloses an environment-based multi-agent motion modeling method and device. This method utilizes a multi-agent dynamic environment management model to dynamically manage and adjust the route information of each agent. Furthermore, a multi-agent adaptive motion solution model is used to schedule the multi-agent motion solution algorithm on demand based on the environmental information. This method enables high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, improving the realism and application value of simulations. These are described in detail below.

[0082] Example 1

[0083] See also Figure 2 , Figure 2 This is a flow chart of a multi-agent motion modeling method based on an environment disclosed in an embodiment of the present invention. Figure 2The described environment-based multi-agent motion modeling method is applied to a multi-agent motion analysis and evaluation system, such as a local server or cloud server used in the multi-agent motion analysis and evaluation system, and the embodiments of the present invention do not limit this. Figure 2 As shown, the environment-based multi-agent motion modeling method may include the following operations:

[0084] S1, obtain task requirement information;

[0085] It should be noted that the task requirement information refers to the parameter information set by the user according to the task requirements;

[0086] The mission requirement information includes departure point information, destination point information, travel route information, travel formation information and mission time information;

[0087] It should be noted that, in this embodiment, the starting point information is the school campus; the travel route information is Guanting Avenue; the destination information is a certain camp; the marching formation information is three infantry fighting vehicles forming a longitudinal line formation with a distance of 50 meters between the infantry fighting vehicles; the mission time information is 2 hours;

[0088] S2, building a multi-agent dynamic environment management model;

[0089] S3, based on the multi-agent dynamic environment management model, processing the task requirement information to obtain simulation result information.

[0090] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the route information of each agent is dynamically managed and adjusted by using the multi-agent dynamic environment management model, and the motion solution algorithm of the multi-agent is scheduled on demand according to the environmental information by using the multi-agent adaptive motion solution model, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0091] In another optional embodiment, in the above step S2, the building of the multi-agent dynamic environment management model includes:

[0092] S21, obtaining the motion input interface requirement information of the intelligent agent;

[0093] S22, obtaining the position status output interface requirement information of the intelligent agent;

[0094] S23, obtaining movement route information of the intelligent agent based on the basic environment dynamic database;

[0095] S24, constructing a multi-agent dynamic environment management model based on the motion input interface requirement information, the position state output interface requirement information, and the agent motion route information;

[0096] It should be noted that the multi-agent dynamic environment management model includes an input interface module, an initialization module, a motion demand processing module, a motion position solving module, a motion position updating module and an output interface module;

[0097] It should be noted that the input interface module is used to provide an intelligent body movement demand interface and receive intelligent body movement demand information;

[0098] It should be noted that the initialization module is used to initialize the basic environment dynamic database and the agent position status;

[0099] It should be noted that the movement demand processing module is used to process the movement demand information of the intelligent body, obtain and store the intelligent movement demand information and the movement route information of the intelligent body;

[0100] It should be noted that the motion position calculation module is used to calculate the intelligent motion demand information and obtain the intelligent body position state information;

[0101] It should be noted that the movement position updating module is used to update the basic environment dynamic database based on the movement route information of the agent and the position status information of the agent;

[0102] The output interface module is used to provide the agent position status information output interface and output the agent position status information;

[0103] It should be noted that the input interface module, the initialization module, the motion demand processing module, the motion position solving module, the motion position and the output interface module updating module are sequentially data-connected.

[0104] It can be seen that the implementation of the multi-agent dynamic environment management model construction method described in the embodiment of the present invention provides a technical basis for the subsequent use of the multi-agent dynamic environment management model to dynamically manage and adjust the route information of each agent, thereby improving the authenticity and application value of the simulation.

[0105] In another optional embodiment, in the above step S3, the processing of the task requirement information based on the multi-agent dynamic environment management model to obtain simulation result information includes:

[0106] S31, parsing the task requirement information to obtain movement request information;

[0107] The movement request information includes starting point information, destination point information, travel route information, travel formation information and task time information;

[0108] S32: Based on the multi-agent dynamic environment management model, the motion request information is processed to obtain simulation result information.

[0109] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the multi-agent motion solution algorithm is scheduled on demand using the multi-agent adaptive motion solution model according to environmental information, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0110] In another optional embodiment, in the above step S32, the processing of the motion request information based on the multi-agent dynamic environment management model to obtain simulation result information includes:

[0111] S321, using the travel route information to match a basic environment dynamic database to obtain road information and terrain information;

[0112] It should be noted that, in this embodiment, the road information is a total length of 100 kilometers, a speed limit of 120 kilometers per hour, a height limit of 4 meters, a paved road, a road width of 15 meters, four lanes in both directions, and dense forests on both sides of the road;

[0113] It should be noted that, in this embodiment, the terrain information is flat terrain;

[0114] S322, calculating movement time information using the road information, the terrain information, and the agent performance information;

[0115] It should be noted that, in this embodiment, the agent performance information represents the performance information of the infantry fighting vehicle, including vehicle length 7.5 meters, vehicle width 2.5 meters, vehicle height 2.7 meters, and maximum speed 94 kilometers per hour;

[0116] It should be noted that the calculation means dividing the total distance of the road by the travel speed according to the terrain information to obtain the movement time information;

[0117] It should be noted that, in this embodiment, the terrain information is flat terrain, and the vehicle speed is not affected. Driving at the maximum speed only takes 100 / 94=1.1 hours, and 1.1 hours < 2 hours, so the task can be completed on time);

[0118] S323, based on the multi-agent dynamic environment management model, performing calculation processing on the motion time information and the agent performance information to obtain the agent expected position information and the agent calculated position information;

[0119] S324: Process the expected position information of the intelligent agent and the calculated position information of the intelligent agent to obtain simulation result information.

[0120] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the multi-agent motion solution algorithm is scheduled on demand using the multi-agent adaptive motion solution model according to environmental information, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0121] In another optional embodiment, in step S323, the step of calculating the motion time information and the agent performance information based on the multi-agent dynamic environment management model to obtain the agent expected position information and the agent calculated position information includes:

[0122] S3231, obtaining the marching formation information;

[0123] S3232, parsing the traveling formation information to obtain the total number of vehicles in the traveling formation;

[0124] S3233, using the multi-agent dynamic environment management model, obtaining a moving vehicle information set;

[0125] S3234, based on the multi-agent dynamic environment management model, using the multi-agent adaptive motion solution model, the moving vehicle information set and the total value of the moving fleet vehicles are processed to obtain the agent expected position information and the agent solved position information.

[0126] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the multi-agent motion solution algorithm is scheduled on demand using the multi-agent adaptive motion solution model according to environmental information, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0127] In another optional embodiment, in step S3234, the multi-agent dynamic environment management model is used to process the moving vehicle information set and the total number of vehicles in the moving fleet using a multi-agent adaptive motion solution model to obtain the agent expected position information and the agent solved position information, including:

[0128] S32341, obtain current time information;

[0129] S32342, using the current time information, calculate and obtain vehicle travel time information;

[0130] S32343, calculating and processing the vehicle travel time information and the travel vehicle information set to obtain vehicle position information;

[0131] S32344, using the vehicle location information to match the basic environment dynamic database to obtain current road status information;

[0132] S32345, determining whether the current road state information value is equal to a first preset value, and obtaining a first road state determination value;

[0133] It should be noted that the first preset value is 1, indicating that the road is a straight route and the road condition is good, and the moving vehicle can use the economic speed to travel in a straight line;

[0134] S32346: When the first road state judgment value is yes, the vehicle travel time information and the travel vehicle information set are processed based on the first multi-intelligence motion solver model to obtain the agent's expected position information and the agent's solved position information;

[0135] When the first road state judgment value is no, executing S32347;

[0136] S32347, determining whether the current road state information value is equal to a second preset value, and obtaining a second road state determination value;

[0137] It should be noted that the second preset value is 2, which indicates that there is a curve in the forward direction of the vehicle, the road is in good condition and there are no other vehicles, and the vehicle maneuvers at a lower speed;

[0138] When the second road state judgment value is yes, processing the vehicle travel time information and the travel vehicle information set based on the second multi-intelligent motion solver model to obtain the agent expected position information and the agent solved position information;

[0139] When the second road state judgment value is no, executing S32348;

[0140] S32348, determining whether the current road state information value is equal to a third preset value, and obtaining a third road state determination value;

[0141] It should be noted that the second preset value is 3, which means that there is an obstacle in the forward direction of the vehicle, but the road condition is good and the vehicle can still travel in a straight line at an economic speed;

[0142] When the third road state judgment value is yes, the vehicle travel time information and the travel vehicle information set are processed based on the first multi-intelligence motion solver model to obtain the agent expected position information and the agent solved position information;

[0143] When the third road state judgment value is negative, executing S32349;

[0144] S32349, obtain current road obstacle information, and update the current road obstacle information to the basic environment dynamic database, and execute S321.

[0145] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the multi-agent motion solution algorithm is scheduled on demand using the multi-agent adaptive motion solution model according to environmental information, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0146] In another optional embodiment, the first multi-intelligent motion solver model is expressed as follows:

[0147]

[0148] in, represents the expected position of the i-th vehicle; represents the calculated position of the i-th vehicle; represents the initial position vector of the i-th vehicle, represents the maneuvering direction unit vector of the i-th vehicle, V represents the maneuvering speed; T i represents the maneuvering time of the i-th vehicle; i represents the vehicle index in the said marching formation; Represents the unit vector of the formation direction; S 间距 Indicates the spacing between formations;

[0149] It should be noted that, in this embodiment, S 间距 The value is 50 meters;

[0150] The above second multi-intelligent motion solver model expression is:

[0151]

[0152] in, represents the expected position of the i-th vehicle; represents the calculated position of the i-th vehicle; V represents the maneuvering speed; represents the maneuvering direction unit vector of the i-th vehicle; ΔT i represents the integration step of the i-th vehicle; a represents the initial value of the integration; b represents the end value of the integration;

[0153] It should be noted that, in this embodiment, the S 间距 The value is 50 meters;

[0154] It should be noted that, in this embodiment, the value of a is set to 0.1; the value of b is set to 3.0;

[0155] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the first multi-agent motion solver model and the second multi-agent motion solver model of the multi-agent are scheduled on demand according to environmental information using the multi-agent adaptive motion solver model, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of the multi-agent under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0156] In another optional embodiment, in step S32346, the processing of the vehicle travel time information and the travel vehicle information set based on the first multi-intelligence motion solver model to obtain the agent expected position information and the agent solved position information includes:

[0157] S323461, parsing the traveling vehicle information set to obtain vehicle speed information and traveling vehicle information of all traveling vehicles;

[0158] S323462: Calculate any of the vehicle speed information and the traveling vehicle information using the first multi-intelligence motion solver model to obtain the agent's expected position information and the agent's calculated position information.

[0159] It should be noted that, in this embodiment, the first multi-intelligence motion solver model is used to solve any of the vehicle speed information and the traveling vehicle information to obtain the agent's expected position information and the agent's solved position information, including:

[0160] Using the first multi-intelligent motion solver model, the speed information and vehicle information of the infantry fighting vehicle 1 are solved to obtain the first solved position information of the infantry fighting vehicle 1, the first expected position information of the infantry fighting vehicle 2, the first solved position information of the infantry fighting vehicle 2, the first expected position information of the infantry fighting vehicle 3, and the first solved position information of the infantry fighting vehicle 3, including:

[0161] Step 1: Determine the straight route of the infantry fighting vehicle 1 and obtain the route position point set, formation information and spacing information of the infantry fighting vehicle 1;

[0162] It should be noted that the position point set of the route of the infantry fighting vehicle 1 includes the initial position point (1.75338, 0.55845, 0), the intermediate position point (1.75339, 0.55845, 0) and the terminal position point (1.75340, 0.55845, 0);

[0163] It should be noted that, in this embodiment, the formation direction unit vector Opposite to the unit vector of the maneuvering direction of the infantry fighting vehicle 1, is (0.98337, 0.18156, 0);

[0164] It should be noted that, in this embodiment, the route position point set of the infantry fighting vehicle 1 is the expected position information of the infantry fighting vehicle 1;

[0165] It should be noted that the spacing information S 间距 The value is 50 meters;

[0166] Step 2: Determine the speed information of the infantry fighting vehicle 1;

[0167] It should be noted that in this embodiment, due to the relatively loose mission time and good road conditions, an economic speed of 80 km / h (about 22.22 m / s) is used for straight-line driving, that is, the speed information of the infantry fighting vehicle 1 is 80 km / h;

[0168] Step 3: Perform coordinate transformation on the first position point to obtain the initial position vector of the infantry fighting vehicle 1 and the unit vector of the maneuvering direction of the infantry fighting vehicle 1

[0169] Set the maneuver time T1 of infantry fighting vehicle 1 to 3 seconds;

[0170] Step 4: Using the first multi-intelligent motion solver model, the initial position vector of the infantry fighting vehicle 1, the speed information of the infantry fighting vehicle 1, the maneuvering direction unit vector of the infantry fighting vehicle 1, and the maneuvering time of the infantry fighting vehicle 1 are processed to obtain the first step position information of the infantry fighting vehicle 1;

[0171] It should be noted that, in this embodiment, the first step of calculating the position information of the vehicle 1 is as follows:

[0172]

[0173] Step 5: converting the first step position information of the fighting vehicle 1 to obtain the first position information of the fighting vehicle 1;

[0174] It should be noted that the calculated position information of the infantry fighting vehicle 1 obtained by converting the expected position information of the infantry fighting vehicle 1 (981093.2992, 5313475.96229, 3375925.56564) is (1.75338, 0.55845, 0);

[0175] Step 6: Using the first multi-intelligent motion solver model, the initial position vector of the infantry fighting vehicle 1, the formation direction unit vector, and the spacing information are processed to obtain the first step expected position information of the fighting vehicle 2;

[0176] It should be noted that, in this embodiment, the first step of calculating the expected position information of the chariot 2 is expressed as follows:

[0177]

[0178] Step 7: performing coordinate conversion processing on the first step expected position information of the infantry fighting vehicle 2 to obtain the first expected position information of the infantry fighting vehicle 2;

[0179] It should be noted that, in the embodiment of the present invention, the first expected position information of the infantry fighting vehicle 2 is (1.75337, 0.55845, 0);

[0180] Step 8, determining the maneuvering speed of the infantry fighting vehicle 2;

[0181] It should be noted that, since the infantry fighting vehicle 1 maintains a constant speed of 80 kilometers per hour, the infantry fighting vehicle 2 also adopts a constant speed of 80 kilometers per hour to maintain the formation, so the speed information of the infantry fighting vehicle 2 is 80 kilometers per hour (about 22.22 meters per second);

[0182] Step nine, obtaining the initial position information of the infantry fighting vehicle 2 based on the formation information;

[0183] It should be noted that, in this embodiment, the initial position point information of the infantry fighting vehicle 2 is (-980978.46018, 5313497.34786, 3375925.27811);

[0184] Step 10: Determine the initial position vector of infantry fighting vehicle 2 Infantry fighting vehicle 2 maneuver direction unit vector and the maneuvering time of tank 2, T2;

[0185] It should be noted that in this embodiment, the movement is linear and the road is flat. The maneuvering direction unit vector of the infantry fighting vehicle 2 is the same as that of the infantry fighting vehicle 1, which is (-0.98337, -0.18156, 0). The maneuvering time period is set to 3 seconds.

[0186] Step 11: Using the first multi-intelligent motion solver model, the initial position vector of the infantry fighting vehicle 2, the speed information of the infantry fighting vehicle 2, the maneuvering direction unit vector of the infantry fighting vehicle 2, and the maneuvering time of the infantry fighting vehicle 2 are processed to obtain the first step position information of the infantry fighting vehicle 2;

[0187] It should be noted that, in this embodiment, the first step of calculating the position information of the vehicle 2 is as follows:

[0188]

[0189] Step 12: performing coordinate conversion processing on the first step position information of the infantry fighting vehicle 2 to obtain the first position information of the infantry fighting vehicle 2;

[0190] It should be noted that the first calculated position information of the infantry fighting vehicle 2 is (1.75337, 0.55845, 0);

[0191] Step 13: Using the first multi-intelligent motion solver model, the initial position vector of the infantry fighting vehicle 3, the formation direction unit vector, and the spacing information are processed to obtain the first step expected position information of the fighting vehicle 3;

[0192] It should be noted that the calculation process of the expected position information of the chariot 3 in the first step is as follows:

[0193]

[0194] Using the first multi-intelligent motion solver model, the initial position vector of the infantry fighting vehicle 3, the speed information of the infantry fighting vehicle 3, the maneuvering direction unit vector of the infantry fighting vehicle 3, and the maneuvering time of the infantry fighting vehicle 3 are processed to obtain the first step solved position information of the infantry fighting vehicle 3;

[0195] It should be noted that the calculation process of the first step of calculating the position information of the chariot 3 is as follows:

[0196]

[0197] Performing coordinate conversion processing on the first step expected position information of the fighting vehicle 3 and the first step solved position information of the fighting vehicle 3 to obtain first expected position information of the infantry fighting vehicle 3 and first solved position information of the infantry fighting vehicle 3;

[0198] It should be noted that the first expected position information of the infantry fighting vehicle 3 and the first solved position information of the infantry fighting vehicle 3 are both (1.75336, 0.55845, 0).

[0199] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the first multi-agent motion solver model is used to process the vehicle driving time information and the driving vehicle information set to obtain the expected position information of the agent and the solved position information of the agent, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0200] In another optional embodiment, in step S32347, the processing of the vehicle travel time information and the travel vehicle information set based on the second multi-intelligent motion solver model to obtain the agent expected position information and the agent solved position information includes:

[0201] S323471, using the travel route information to match the basic environment dynamic database to obtain curve turning angle information;

[0202] S323472, parsing the traveling vehicle information set to obtain vehicle speed information and traveling vehicle information of all traveling vehicles;

[0203] S323473, using a second multi-intelligent motion solver model to solve the curve turning angle information, the traveling vehicle speed information, and the traveling vehicle information for any of the vehicle speed information and the traveling vehicle information, to obtain the agent's expected position information and the agent's solved position information;

[0204] It should be noted that, in this embodiment, the second multi-intelligent motion solver model is used to solve the curve turning angle information, the traveling vehicle speed information, and the traveling vehicle information for any of the vehicle speed information and the traveling vehicle information to obtain the agent's expected position information and the agent's solved position information, including:

[0205] Using the first multi-intelligent motion solver model, the speed information of infantry fighting vehicle 1 and the vehicle information of infantry fighting vehicle 1 are solved to obtain the second solved position information of infantry fighting vehicle 1, the second expected position information of infantry fighting vehicle 2, the second solved position information of infantry fighting vehicle 2, the second expected position information of infantry fighting vehicle 3, and the second solved position information of infantry fighting vehicle 3, including:

[0206] A01, determine the maneuvering speed of infantry fighting vehicle 1 and obtain the speed information of infantry fighting vehicle 1;

[0207] It should be noted that in this embodiment, due to a bend in the road ahead, according to the cornering strategy, it is necessary to reduce speed and maneuver at a speed of 40 kilometers per hour (about 11.11 meters per second). That is, the speed information of the infantry fighting vehicle 1 is 40 kilometers per hour (about 11.11 meters per second);

[0208] A02, based on the turning route determination model, calculates the turning route and obtains a turning route position point set;

[0209] The cornering route determination model expression is:

[0210]

[0211] Among them, R is the turning radius; g is the acceleration of gravity; θ is the turning angle of the curve; L is the turning distance; V is the maneuvering speed;

[0212] It should be noted that, in this embodiment, the g value is 9.8 m / s^2; θ is 0.5236 (i.e., 30°);

[0213] Substitute the speed information, g value, and θ value of the infantry fighting vehicle 1 into the turning route determination model to obtain the turning radius and turning distance:

[0214]

[0215] According to the turning radius and turning distance, the curve is fitted to obtain the turning route position point set as (1.75354, 0.55846, 0), (1.753539, 0.558459, 0), (1.753538, 0.558458, 0);

[0216] A03, determining the initial position vector information of infantry fighting vehicle 1, the speed information of infantry fighting vehicle 1, the maneuvering direction unit vector of infantry fighting vehicle 1, the integration step length of infantry fighting vehicle 1, the initial integration value, and the final integration value;

[0217] It should be noted that, in this embodiment, the initial position vector information of the infantry fighting vehicle 1 is (-981861.6583, 5313334.07456, 3375925.54978);

[0218] The integration step length ΔT1 of the infantry fighting vehicle 1 is 0.1 seconds;

[0219] A04, using a second multi-intelligent motion solver model, processes the initial position vector information of the combat vehicle 1, the speed information of the infantry fighting vehicle 1, the maneuvering direction unit vector of the infantry fighting vehicle 1, the integration step length of the infantry fighting vehicle 1, the initial integration value, and the final integration value to obtain the second solved position information of the infantry fighting vehicle 1;

[0220] It should be noted that the calculation process of the position information of the second step vehicle 3 is as follows:

[0221]

[0222] A05, performing coordinate conversion processing on the second resolved position information of the infantry fighting vehicle 1 to obtain second resolved position information of the infantry fighting vehicle 1;

[0223] It should be noted that, in the embodiment of the present invention, the second calculated position information of the infantry fighting vehicle 1 is (1.75353, 0.55845, 0);

[0224] A06, using the second multi-intelligent motion solver model, processing the initial position vector of the infantry fighting vehicle 1, the formation direction unit vector, and the spacing information to obtain expected position information of the second infantry fighting vehicle 2;

[0225] It should be noted that the initial position vector of the infantry fighting vehicle 1 is is (-981894.0049, 5313328.01956, 3375925.54978), s 间距 The turning radius R is 21.83 meters, and the turning angle of the curve can be calculated. Combine the maneuvering direction unit vector of infantry fighting vehicle 1 and the road information to make the heading correction to ensure that the expected position of infantry fighting vehicle 2 is within the curve, and we get is (0.983359, 0.18167, 0);

[0226] It should be noted that, in this embodiment, the calculation expression for the expected position information of the second infantry fighting vehicle 2 is:

[0227]

[0228] A07, performing coordinate conversion processing on the second expected position information of the infantry fighting vehicle 2 to obtain second expected position information of the infantry fighting vehicle 2;

[0229] It should be noted that, in the embodiment of the present invention, the second expected position information of the infantry fighting vehicle 2 is (1.753521, 0.558452, 0);

[0230] A08, determine the maneuvering speed and initial position vector of infantry fighting vehicle 2 Infantry fighting vehicle 2 maneuver direction unit vector and the maneuvering time of tank 2, T2;

[0231] It should be noted that, since infantry fighting vehicle 1 decelerates to a constant speed of 40 km / h in order to safely make a turn, infantry fighting vehicle 2 also adopts a speed reduction strategy to make a turn at a speed of 40 km / h.

[0232] It should be noted that, in this embodiment, the current position of the infantry fighting vehicle 2 in the Earth-centered Earth-fixed coordinate system ECEF is (-981812.37274, 5313343.36471, 3375925.26206), and the maneuvering direction unit vector of the infantry fighting vehicle 2 in the current integration step is corrected to (-0.983359, -0.18167, 0) after the curve, and the integration step is 0.1 seconds.

[0233] A09, using the second multi-intelligent motion solver model, processing the initial position vector of the infantry fighting vehicle 2, the velocity information of the infantry fighting vehicle 2, the maneuvering direction unit vector of the infantry fighting vehicle 2, and the integral step size of the infantry fighting vehicle 2 to obtain the solved position information of the second infantry fighting vehicle 2;

[0234] It should be noted that the calculation process of the position information of the second chariot 2 is as follows:

[0235]

[0236] A10, performing coordinate conversion processing on the second resolved position information of the infantry fighting vehicle 2 to obtain second resolved position information of the infantry fighting vehicle 2;

[0237] It should be noted that the second calculated position information of the infantry fighting vehicle 2 is (1.75352, 0.55845, 0);

[0238] A11, using the second multi-intelligent motion solver model, processing the initial position vector of the infantry fighting vehicle 2, the formation direction unit vector, and the spacing information to obtain expected position information of the second infantry fighting vehicle 3;

[0239] It should be noted that the initial position vector of the infantry fighting vehicle 2 is is (-981845.14813, 5313337.30965, 3375925.26206), s 间距 The turning radius R is 21.83 meters, and the turning angle of the curve can be calculated. Combine the maneuvering direction unit vector of infantry fighting vehicle 2 and the road information to make the heading correction to ensure that the expected position of infantry fighting vehicle 3 is within the curve, and get is (0.983359, 0.18167, 0);

[0240] It should be noted that the calculation process of the expected position information of the second step combat vehicle 3 is as follows:

[0241]

[0242] Using the second multi-intelligent motion solver model, the initial position vector of the infantry fighting vehicle 3, the speed information of the infantry fighting vehicle 3, the maneuvering direction unit vector of the infantry fighting vehicle 3, and the integral step length of the infantry fighting vehicle 3 are processed to obtain the second solved position information of the infantry fighting vehicle 3 for processing;

[0243] It should be noted that the initial position vector of the infantry fighting vehicle 3 is (-981764.14669, 5313352.43653, 3375925.00911), the maneuvering direction unit vector of the infantry fighting vehicle 3 is corrected to (-0.983359, -0.18167, 0) after the curve, the integration step of the infantry fighting vehicle 3 is 0.1 second, and the speed information of the infantry fighting vehicle 3 is the same as that of the infantry fighting vehicle 1;

[0244] It should be noted that the calculation process of the position information of the second step vehicle 3 is as follows:

[0245]

[0246] A12, converting the expected position information of the second infantry fighting vehicle 3 and the calculated position information of the second infantry fighting vehicle 3 to obtain second expected position information of the infantry fighting vehicle 3 and second calculated position information of the infantry fighting vehicle 3;

[0247] It should be noted that the second expected position information of the infantry fighting vehicle 3 is (1.753513, 0.558451, 0), and the second solved position information of the infantry fighting vehicle 3 is (1.75351, 0.55845, 0).

[0248] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the second multi-agent motion solver model is used to process the vehicle driving time information and the driving vehicle information set to obtain the expected position information of the agent and the solved position information of the agent, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0249] In another optional embodiment, in step S324, the processing of the expected position information of the agent and the calculated position information of the agent to obtain simulation result information includes:

[0250] S3241, obtain real-time environmental data information;

[0251] S3242, calculating a difference value between the real-time environmental data information and the position information calculated by the agent to obtain first position difference information;

[0252] It should be noted that the difference value calculation refers to performing a difference calculation;

[0253] S3243, determining whether the first position difference information is less than a preset first difference value threshold, and obtaining a first position difference determination result;

[0254] It should be noted that the first difference value threshold represents the vehicle vertical position threshold, which limits the vehicle to being above the ground level;

[0255] It should be noted that, in this embodiment, the first difference value threshold is set to 0;

[0256] S3244: When the first position difference determination result is negative, the agent's calculated position information is updated to the real-time environmental data information, and S3245 is executed.

[0257] When the first position difference determination result is yes, executing S3245;

[0258] S3245, calculating a difference between the expected position information of the agent and the calculated position information of the agent to obtain second position difference information;

[0259] S3246, determining whether the second position difference information is less than a second difference value threshold, and obtaining a second position difference determination result;

[0260] It should be noted that the second difference value threshold represents the vehicle horizontal position threshold;

[0261] It should be noted that, in this embodiment, the second difference value threshold is set to the width of Guanting Avenue, that is, 15 meters;

[0262] S3247, when the second position difference determination result is yes, obtaining simulation result information;

[0263] When the second position difference judgment result is negative, the expected position information of the intelligent agent is updated to the calculated position information of the intelligent agent to obtain simulation result information;

[0264] It should be noted that the simulation result information represents the calculated position information of the intelligent agent displayed on the simulation interface.

[0265] It can be seen that the multi-agent motion modeling method described in the embodiment of the present invention is implemented, and the basic environment dynamic database is used to feedback and adjust the position information solved by the agent, thereby realizing high-precision prediction and simulation of the formation-maintaining motion state of multiple agents under different environmental conditions, thereby improving the authenticity and application value of the simulation.

[0266] Example 2

[0267] See also Figure 3 , Figure 3This is a schematic diagram of the structure of an environment-based multi-agent motion modeling device disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a multi-agent motion analysis and evaluation system, such as a local server or a cloud server for a multi-agent motion analysis and evaluation system, and the embodiment of the present invention does not limit this. Figure 3 As shown, the system may include:

[0268] Data acquisition module 101, management model construction module 102 and simulation processing module 103;

[0269] The data acquisition module 101 is used to acquire task requirement information;

[0270] The management model construction module 102 is used to construct a multi-agent dynamic environment management model;

[0271] The simulation processing module 103 is used to process the task requirement information based on the multi-agent dynamic environment management model to obtain simulation result information.

[0272] Example 3

[0273] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an environment-based multi-agent motion modeling device disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a multi-agent motion analysis and evaluation system, such as a local server or a cloud server for a multi-agent motion analysis and evaluation system, and the embodiment of the present invention does not limit this. Figure 4 As shown, the device may include:

[0274] A memory 202 storing executable program code;

[0275] a processor 201 coupled to a memory 202;

[0276] The processor 201 calls the executable program code stored in the memory 202 to execute the steps in the environment-based multi-agent motion modeling method described in the first embodiment.

[0277] Example 4

[0278] An embodiment of the present invention discloses a computer-readable storage medium that stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the environment-based multi-agent motion modeling method described in the first embodiment.

[0279] Example 5

[0280] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the environment-based multi-agent motion modeling method described in Example 1.

[0281] The device embodiments described above are merely illustrative. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0282] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0283] Finally, it should be noted that the environment-based multi-agent motion modeling method and device disclosed in the embodiments of the present invention only disclose a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multi-agent motion modeling method based on an environment, characterized in that: The method comprises: S1, obtain task requirement information; S2, building a multi-agent dynamic environment management model; S3, processing the task requirement information based on the multi-agent dynamic environment management model to obtain simulation result information; The processing of the task requirement information based on the multi-agent dynamic environment management model to obtain simulation result information includes: S31, parsing the task requirement information to obtain movement request information; The movement request information includes starting point information, destination point information, travel route information, travel formation information and task time information; S32, processing the motion request information based on the multi-agent dynamic environment management model to obtain simulation result information; The processing of the motion request information based on the multi-agent dynamic environment management model to obtain simulation result information includes: S321, using the travel route information to match a basic environment dynamic database to obtain road information and terrain information; S322, calculating movement time information using the road information, the terrain information, and the agent performance information; S323, based on the multi-agent dynamic environment management model, performing calculation processing on the motion time information and the agent performance information to obtain calculated position information of the agent; S324, processing the calculated position information of the intelligent agent to obtain simulation result information; The method of calculating the motion time information and the agent performance information based on the multi-agent dynamic environment management model to obtain the agent calculated position information includes: S3231, obtaining the marching formation information; S3232, parsing the traveling formation information to obtain the total number of vehicles in the traveling formation; S3233, using the multi-agent dynamic environment management model, obtaining a moving vehicle information set; S3234, based on the multi-agent dynamic environment management model, uses the multi-agent adaptive motion solution model to process the moving vehicle information set and the total value of the moving fleet vehicles to obtain the agent's expected position information and the agent's solved position information.

2. The environment-based multi-agent motion modeling method according to claim 1, characterized in that: The construction of a multi-agent dynamic environment management model includes: S21, obtaining the motion input interface requirement information of the intelligent agent; S22, obtaining the position status output interface requirement information of the intelligent agent; S23, obtaining movement route information of the intelligent agent based on the basic environment dynamic database; S24, constructing a multi-agent dynamic environment management model based on the motion input interface requirement information, the position state output interface requirement information and the agent motion route information.

3. The environment-based multi-agent motion modeling method according to claim 1, characterized in that: The multi-agent dynamic environment management model is based on the multi-agent adaptive motion solution model, and the moving vehicle information set and the total value of the moving vehicle fleet are processed to obtain the agent expected position information and the agent solved position information, including: S32341, obtain current time information; S32342, using the current time information, calculate and obtain vehicle travel time information; S32343, calculating and processing the vehicle travel time information and the travel vehicle information set to obtain vehicle position information; S32344, using the vehicle location information to match the basic environment dynamic database to obtain current road status information; S32345, determining whether the current road state information value is equal to a first preset value, and obtaining a first road state determination value; S32346: When the first road state judgment value is yes, the vehicle travel time information and the travel vehicle information set are processed based on the first multi-intelligence motion solver model to obtain the agent's expected position information and the agent's solved position information; When the first road state judgment value is no, executing S32347; S32347, determining whether the current road state information value is equal to a second preset value, and obtaining a second road state determination value; When the second road state judgment value is yes, processing the vehicle travel time information and the travel vehicle information set based on the second multi-intelligent motion solver model to obtain the agent expected position information and the agent solved position information; When the second road state judgment value is no, executing S32348; S32348, determining whether the current road state information value is equal to a third preset value, and obtaining a third road state determination value; When the third road state judgment value is yes, processing the vehicle travel time information and the travel vehicle information set based on the first multi-agent motion solver model to obtain agent expected position information and agent solved position information; When the third road state judgment value is negative, executing S32349; S32349, obtain current road obstacle information, and update the current road obstacle information to the basic environment dynamic database, and execute S321.

4. The environment-based multi-agent motion modeling method according to claim 3, characterized in that: The expression of the first multi-intelligent motion solver model is: in, represents the expected position of the i-th vehicle; represents the calculated position of the i-th vehicle; represents the initial position vector of the i-th vehicle, represents the maneuvering direction unit vector of the i-th vehicle, V represents the maneuvering speed; T i represents the maneuvering time of the i-th vehicle; i represents the vehicle index in the said marching formation; Represents the unit vector of the formation direction; S 间距 Indicates the spacing between formations; The second multi-intelligent motion solver model expression is: in, represents the expected position of the i-th vehicle; represents the calculated position of the i-th vehicle; V represents the maneuvering speed; represents the maneuvering direction unit vector of the i-th vehicle; ΔT i represents the integration step of the i-th vehicle; a represents the initial value of the integration; b represents the end value of the integration.

5. An environment-based multi-agent motion modeling device, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the environment-based multi-agent motion modeling method as described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which, when called, are used to execute the environment-based multi-agent motion modeling method as described in any one of claims 1-4.

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