Group control method and system for cooperative medical service support of vehicles with different functions

Abstract

The invention discloses a group control method and system for cooperative medical service guarantee of vehicles with different functions, and relates to the technical field of medical service guarantee, and the method comprises the steps: obtaining battlefield environment information and target location information, constructing an electronic map containing a dangerous region according to the battlefield environment information, the initial position and the target location position of the medical service support vehicle are marked in the electronic map; based on the electronic map, the medical service support vehicles executing the same task form a vehicle group, and a target path for crossing the dangerous area is generated for the vehicle group; and controlling the vehicle group to move along the target path, and grouping according to the target location of each medical service support vehicle after the vehicle group arrives at a preset position. According to the method, real-time dynamic updating of battlefield environment information is realized on the basis of a multi-layer information superposition mechanism of a dangerous area distribution map and in combination with a filtering algorithm; by introducing an evaluation mechanism of a task relevancy matrix and a vehicle grouping index, the overall function coverage rate of a vehicle group is improved, and the communication interruption risk is reduced in a strong electromagnetic interference environment.

Description

Technical Field

[0001] The present invention relates to the technical field of medical support, and in particular to a group control method and system for coordinated medical support of vehicles with different functions. Background Art

[0002] In the modern battlefield environment, medical support vehicles are important battlefield support forces, and their collaborative combat capabilities directly affect the efficiency of battlefield rescue and equipment maintenance. Traditional medical support vehicle group control methods mainly rely on fixed formations and preset task allocation mechanisms. This method shows obvious limitations when dealing with complex and changing battlefield environments. At present, mainstream group control methods usually adopt behavioral rule-based distributed control or central control-based hierarchical architectures. These methods still face many challenges in dealing with heterogeneous vehicle collaboration, dynamic task allocation, and path optimization. In particular, when considering multi-dimensional constraints such as vehicle functional complementarity, communication constraints, and survival probability, existing technologies are difficult to achieve efficient collaboration and flexible reorganization of vehicle groups.

[0003] In recent years, with the development of artificial intelligence and swarm intelligence technologies, some researchers have tried to apply methods such as reinforcement learning and swarm intelligence optimization to the field of vehicle group control. However, these methods still have problems such as slow convergence and poor robustness when dealing with uncertainties in battlefield environments. Especially when it comes to special needs such as decoy vehicle cover, functional complementarity evaluation, and dynamic grouping, existing technologies often find it difficult to balance the relationship between computational efficiency and control accuracy. In addition, existing path planning algorithms focus more on the optimization problems of single vehicles or homogeneous vehicle groups, and there is a relative lack of research on collaborative path planning for heterogeneous vehicle groups, especially when considering the overall survival probability and functional complementarity of the vehicle group, there is a lack of effective solutions.

[0004] In summary, the existing medical support vehicle group control technology has technical problems such as low group intelligence optimization efficiency, poor coordination of heterogeneous vehicles, and insufficient dynamic reorganization capability when dealing with collaborative control problems in complex battlefield environments. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the present invention provides a group control method and system for coordinated medical support of vehicles with different functions, which can solve the problems mentioned in the background technology.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a group control method for coordinated medical support of vehicles with different functions, comprising: obtaining battlefield environment information and target location information, constructing an electronic map containing dangerous areas according to the battlefield environment information, and marking the initial position and target location of the medical support vehicle in the electronic map; based on the electronic map, forming a vehicle group of medical support vehicles that perform the same task, and using a preset algorithm to generate a target path for the vehicle group that crosses the dangerous area; considering the overall survival probability of the vehicle group in the process of generating the target path; controlling the vehicle group to move along the target path, and after reaching the preset position, grouping the vehicle group according to the target location of each medical support vehicle; the grouping process includes dynamic reorganization of the vehicle group and path updating.

[0008] As a preferred solution of the group control method for coordinated medical support of vehicles with different functions described in the present invention, the battlefield environment information includes the location and type information of the dangerous area; the step of obtaining the battlefield environment information and the target location information, and constructing an electronic map containing the dangerous area according to the battlefield environment information includes: collecting the combat area information and the enemy-occupied area information in the battlefield environment, and fusing the combat area information and the enemy-occupied area information based on a preset fusion algorithm to obtain a dangerous area distribution map; the dangerous area distribution map includes the spatial location information and danger level information of the dangerous area; the dangerous area distribution map is mapped to a preset coordinate system to form an electronic map, and the initial position of the medical support vehicle and the location information of each target location are marked on the electronic map.

[0009] As a preferred solution of the group control method for coordinated medical support of vehicles with different functions described in the present invention, the preset fusion algorithm includes: periodically sampling the engagement area information and the enemy-occupied area information to obtain dynamic change data of the battlefield environment; based on the dynamic change data, real-time updating of the danger level information of the dangerous area, wherein the danger level information is determined by evaluating the threat degree of different types of dangerous areas to medical support vehicles; superimposing the updated distribution map of the dangerous area with the terrain feature information to form an electronic map containing multiple layers of information.

[0010] As a preferred solution of the group control method for coordinated medical support of vehicles with different functions described in the present invention, wherein: based on the electronic map, medical support vehicles performing the same task are grouped into a vehicle group, and a preset algorithm is used to generate a target path for the vehicle group to cross the dangerous area, including the following steps: based on the danger level information in the electronic map and the functional attributes of each medical support vehicle, a task correlation matrix is ​​constructed, and a spectral clustering algorithm is used to analyze the task correlation matrix, and medical support vehicles with task correlation greater than a first preset threshold are grouped into a vehicle group; the task correlation matrix contains functional complementarity and synergy between vehicles; the vehicle group The task execution capability of each medical support vehicle in the group is evaluated. If there is a medical support vehicle with an execution capability lower than a second preset threshold, the task of the vehicle is reallocated to the medical support vehicle with the highest execution capability, otherwise the original task allocation is maintained; the task execution capability is calculated based on the remaining energy, loading status and maneuverability of the vehicle; based on the danger level information, an A* algorithm is used to generate an initial path for the vehicle group, and the initial path is dynamically optimized in combination with a reinforcement learning method to obtain a target path that takes into account the overall survival probability of the vehicle group; if the survival probability of the target path is lower than a third preset threshold, the path is replanned until the survival probability requirement is met.

[0011] As a preferred solution of the group control method for coordinated medical support of vehicles with different functions described in the present invention, the process of constructing the task relevance matrix also includes: constructing a connectivity constraint matrix based on the communication capabilities between vehicles, and fusing the connectivity constraint matrix with the functional complementarity matrix; if the vehicle group includes a bait vehicle, the cover effect of other medical support vehicles is evaluated based on the maneuverability of the bait vehicle to generate a cover effectiveness coefficient; when performing task reallocation, a hierarchical optimization algorithm that takes into account the cover effectiveness coefficient is adopted to update the task relevance matrix with the goal of maximizing the overall collaborative effectiveness of the vehicle group.

[0012] As a preferred solution of the group control method for cooperative medical support of vehicles with different functions described in the present invention, wherein: the vehicle group is controlled to move along the target path, and after reaching the preset position, the vehicle group is grouped according to the target location of each medical support vehicle, including the following steps: based on the task relevance matrix and the cover effectiveness coefficient, the optimal formation position of each medical support vehicle in the vehicle group is calculated, and an adaptive formation control algorithm is used to control the vehicle group to move along the target path to a preset separation position; wherein the adaptive formation control algorithm dynamically adjusts the vehicle spacing according to the real-time communication quality and the surrounding threat intensity during the movement of the vehicle group; at the preset separation position, a Mahalanobis distance matrix is ​​constructed based on the spatial distribution characteristics of the target location, and is generated in combination with the task relevance matrix Vehicle grouping index, the vehicle groups are preliminarily divided according to the vehicle grouping index; if there are medical support vehicles with overlapping functions in the divided new vehicle groups, the vehicles with weak overlapping functions will be deployed to other new vehicle groups with the highest functional complementarity, and bait vehicles will be allocated to each new vehicle group based on the cover effectiveness coefficient; wherein the vehicle grouping index is obtained by comprehensively calculating the spatial concentration of the target location and the collaborative combat effectiveness between vehicles; an ant colony algorithm is used to generate a travel path for each new vehicle group to its respective target location, and the vehicle grouping index and the cover effectiveness coefficient are used as optimization factors for path planning; if it is detected that the travel paths of different new vehicle groups have overlapping areas, the yield priority evaluation between vehicle groups is triggered based on functional complementarity, and a temporary avoidance path is planned for the yielding vehicle group.

[0013] As a preferred solution of the group control method for coordinated medical support of vehicles with different functions described in the present invention, wherein: during the execution of the adaptive formation control algorithm, if any medical support vehicle is detected to deviate from the predetermined formation position, the vehicle group configuration is locally adjusted based on the vehicle grouping index; the local adjustment includes re-evaluating the functional complementarity of the surrounding medical support vehicles, and dynamically updating the relative position relationship and communication topology between vehicles while maintaining the optimal cover effectiveness coefficient.

[0014] To further solve the above technical problems, the present invention provides the following technical solutions: a group control system for coordinated medical support of vehicles with different functions, comprising: a map construction module, used to obtain battlefield environment information and target location information, construct an electronic map containing dangerous areas according to the battlefield environment information, and mark the initial position and target location of the medical support vehicle in the electronic map; a path generation module, used to group medical support vehicles performing the same task into a vehicle group based on the electronic map, and use a preset algorithm to generate a target path for the vehicle group to cross the dangerous area; a control module, used to control the movement of the vehicle group along the target path, and after reaching the preset position, group the vehicle group according to the target location of each medical support vehicle.

[0015] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein when the processor executes the computer program, the steps of the group control method for collaborative medical support of vehicles with different functions as described above are implemented.

[0016] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the group control method for collaborative medical support of vehicles with different functions as described above are implemented.

[0017] Beneficial effects of the present invention: The present invention has achieved remarkable technical effects in practical applications through multi-dimensional evaluation mechanism and algorithm design: First, based on the multi-layer information superposition mechanism of the dangerous area distribution map, combined with the Kalman filtering algorithm, the real-time dynamic update of the battlefield environment information is realized; secondly, by introducing the task relevance matrix and the dynamic evaluation mechanism of the vehicle grouping index, the overall functional coverage of the vehicle group is improved, and the risk of communication interruption is significantly reduced in a strong electromagnetic interference environment; thirdly, the adaptive formation control algorithm and the dynamic deployment mechanism based on functional complementarity enhance the survivability of the vehicle group through dangerous areas while maintaining a high path planning efficiency; finally, through the evaluation of the cover effectiveness of the bait vehicle and the dynamic evaluation mechanism of the priority of giving way, the efficiency of task completion is improved while ensuring the continuity of the task. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 This is a schematic diagram of the overall process of a group control method for coordinated medical support of vehicles with different functions proposed by the present invention;

[0020] Figure 2 A diagram of computer equipment in a group control method for collaborative medical support of vehicles with different functions proposed by the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a group control method for coordinated medical support of vehicles with different functions.

[0024] S1: Obtain battlefield environment information and target location information, construct an electronic map containing dangerous areas based on the battlefield environment information, and mark the initial position and target location of the medical support vehicle on the electronic map.

[0025] Specifically, battlefield environment information includes location and type information of dangerous areas.

[0026] S1.1: Collect the combat area information and enemy-occupied area information in the battlefield environment, and fuse the combat area information and enemy-occupied area information based on a preset fusion algorithm to obtain a dangerous area distribution map; the dangerous area distribution map includes the spatial location information and danger level information of the dangerous area.

[0027] It should be noted that the preset fusion algorithm includes periodic sampling of the combat area information and the enemy-occupied area information to obtain the dynamic change data of the battlefield environment; based on the dynamic change data, the danger level information of the dangerous area is updated in real time, wherein the danger level information is determined by evaluating the threat level of different types of dangerous areas to medical support vehicles; and the updated dangerous area distribution map is superimposed with the terrain feature information to form an electronic map containing multiple layers of information. In this embodiment, the preset fusion algorithm can use the Kalman filtering method, which can effectively eliminate information redundancy and uncertainty. The processed data is used to generate a dangerous area distribution map, wherein the danger level is determined by evaluating multiple factors, including enemy firepower density, number of mobile vehicles, electronic interference intensity, etc.

[0028] S1.2: Map the distribution map of the dangerous area to a preset coordinate system to form an electronic map, and mark the initial position of the medical support vehicle and the location information of each target location on the electronic map. The preset coordinate system adopts a unified geographic coordinate reference.

[0029] It should be noted that in the electronic map construction stage, the present invention superimposes the processed dangerous area distribution map with the terrain feature information. Terrain feature information includes terrain undulations, vegetation coverage, road distribution, etc. The combination of this information and dangerous area information forms a multi-level electronic map. Practice has shown that this multi-layer information superposition method significantly improves the rationality of path planning, enabling medical support vehicles to better utilize terrain cover and improve survivability. In addition, the present invention also establishes a regular information verification mechanism, which continuously optimizes the parameters of the fusion algorithm by comparing with the actual situation to ensure the accuracy and reliability of information processing.

[0030] Preferably, in this embodiment, in order to solve the problem of information acquisition and processing of medical support vehicles in complex battlefield environments, the present invention firstly collects periodic information on the combat areas and enemy-occupied areas through the sensor network deployed in various areas of the battlefield. The collection frequency can be dynamically adjusted according to the battlefield situation, generally once every 1 to 2 minutes, and can be increased to once every 30 seconds when drastic changes are detected. This dynamic sampling mechanism significantly improves the real-time nature of information acquisition, and shortens the update delay of environmental information from the traditional 5-10 minutes to 1 to 2 minutes. Compared with traditional methods, the present invention has achieved significant improvements in threat identification accuracy, information update real-time, and path planning rationality, providing a strong guarantee for the safe operation of medical support vehicles.

[0031] S2: Based on the electronic map, medical support vehicles that perform the same task are organized into vehicle groups, and a preset algorithm is used to generate a target path for the vehicle group to cross the dangerous area.

[0032] Specifically, the overall survival probability of the vehicle group is considered during the generation of the target path.

[0033] S2.1: Based on the danger level information in the electronic map and the functional attributes of each medical support vehicle, a task relevance matrix is ​​constructed, and the spectral clustering algorithm is used to analyze the task relevance matrix, and the medical support vehicles with task relevance greater than the first preset threshold are grouped into a vehicle group; the task relevance matrix includes the functional complementarity and synergy between vehicles.

[0034] Among them, the construction process of the task relevance matrix also includes: constructing a connectivity constraint matrix based on the communication capability between vehicles, and integrating the connectivity constraint matrix with the functional complementarity matrix; if the vehicle group contains a decoy vehicle, the cover effect of other medical support vehicles is evaluated based on the mobility of the decoy vehicle to generate a cover effectiveness coefficient; when redistributing tasks, a hierarchical optimization algorithm considering the cover effectiveness coefficient is adopted to update the task relevance matrix with the goal of maximizing the overall collaborative effectiveness of the vehicle group. It should be noted that the connectivity constraint matrix is ​​constructed by evaluating the communication quality and reliability between vehicles, and includes key parameters such as the maximum communication distance, signal attenuation characteristics and communication bandwidth between vehicles. The matrix elements are represented by the connectivity probability in the interval [0,1], which takes into account the influence of practical factors such as terrain occlusion and electromagnetic interference. In the process of forming a vehicle group, the matrix ensures that the grouping results meet the communication reliability requirements and avoids the failure of coordination caused by communication interruption. The functional complementarity matrix is ​​constructed by analyzing the functional synergy effects of different types of medical support vehicles in the battlefield environment, and the matrix element values ​​reflect the degree of functional complementarity between the two vehicles. Specifically, the matrix construction process takes into account the three dimensions of equipment performance complementarity, mission capability complementarity, and tactical value complementarity, and introduces a dynamic weight adjustment mechanism based on historical mission data.

[0035] It should be noted that the present invention improves the traditional spectral clustering algorithm and enhances the applicability of the algorithm in vehicle grouping by introducing an adaptive similarity calculation method. Specifically, when converting the task relevance matrix into a normalized Laplace matrix, a dynamic weight adjustment mechanism based on vehicle functional attributes is adopted, so that vehicles with strong functional complementarity have a higher tendency to be grouped. In addition, in the process of eigenvector calculation, an eigenvalue screening method based on task urgency is introduced to ensure that the grouping results not only meet the requirements of functional complementarity, but also adapt to changes in battlefield situation.

[0036] S2.2: Evaluate the mission execution capability of each medical support vehicle in the vehicle group. If there is a medical support vehicle with a mission execution capability lower than the second preset threshold, reallocate the task of the vehicle to the medical support vehicle with the highest mission execution capability. Otherwise, maintain the original mission allocation. The mission execution capability is calculated based on the vehicle's remaining energy, loading status and maneuverability.

[0037] S2.3: Based on the danger level information, the A* algorithm is used to generate an initial path for the vehicle group, and the initial path is dynamically optimized in combination with the reinforcement learning method to obtain a target path that takes into account the overall survival probability of the vehicle group; if the survival probability of the target path is lower than the third preset threshold, the path is replanned until the survival probability requirement is met.

[0038] It should be noted that in response to the special needs of path planning in battlefield environments, the present invention improves the heuristic function of the A* algorithm. First, a weighted factor of the danger level is added to the distance estimation, so that the path planning process can dynamically balance the path length and safety. Secondly, a path evaluation mechanism based on the overall survival probability of the vehicle group is introduced, and the candidate paths are comprehensively scored by considering the vulnerability and importance of different vehicles in the vehicle group. This improvement enables the algorithm to significantly improve the overall survivability of the vehicle group while ensuring the optimality of the path. Test data shows that after adopting the improved algorithm, the success rate of the vehicle group crossing the dangerous area has increased by 35%, and the average path planning time has only increased by 10%.

[0039] Preferably, in this embodiment, for the problem of coordinated grouping and path planning of vehicles with different functions in a complex battlefield environment, the present invention proposes a dynamic vehicle group construction method based on multi-dimensional evaluation. The method first realizes the scientific grouping of vehicles by constructing a task relevance matrix, and then ensures that the vehicle group completes the task safely and efficiently through an improved path planning algorithm.

[0040] In the vehicle group construction stage, the present invention constructs a task relevance matrix including functional complementarity and communication constraints by analyzing the functional attributes and task requirements of the vehicles. Specifically, the functional complementarity evaluation is quantified from three dimensions: equipment performance, mission capability, and tactical value, and the weight of each dimension is determined by the hierarchical analysis method. In a certain actual combat exercise, this method successfully allocated a group of medical support vehicles including 3 field emergency vehicles, 2 bait vehicles, and 2 electronic maintenance vehicles into two functionally complementary vehicle groups, where the first vehicle group is responsible for the treatment of the wounded in the front area, and the second vehicle group is responsible for the equipment maintenance in the rear area. The overall functional coverage of the vehicle group reached 95%, which is 45% higher than the traditional fixed grouping method.

[0041] In order to ensure the communication reliability of the vehicle group, the present invention introduces a connectivity constraint matrix. The matrix is ​​constructed by evaluating the communication quality and reliability between vehicles, taking into account factors such as the maximum communication distance and signal attenuation characteristics. Test data shows that in a strong electromagnetic interference environment, the vehicle group configuration optimized based on this matrix reduces the probability of communication interruption from the original 15% to 6%, significantly improving the stability of vehicle group collaboration.

[0042] In the task execution capability assessment phase, the present invention has established a set of assessment mechanisms that comprehensively consider the remaining energy, loading status and maneuverability. When it is detected that the execution capability of a vehicle is lower than the preset threshold, the present invention will automatically trigger the task reallocation process. In an 8-hour exercise, the mechanism successfully handled 3 vehicle performance degradation events, ensuring the continuity of the overall task through timely task adjustments, and improving the task completion efficiency by 30%.

[0043] In terms of path planning, the present invention improves the traditional A* algorithm and introduces a path evaluation mechanism based on the overall survival probability of the vehicle group. When calculating the path cost, the improved algorithm not only considers the distance factor, but also incorporates the danger level and vehicle group characteristics into the evaluation system. In addition, the present invention also introduces a decoy vehicle cover effectiveness evaluation mechanism. By analyzing the maneuverability and battlefield situation of the decoy vehicle, the present invention can provide the best cover solution for other medical support vehicles.

[0044] In general, the present invention achieves efficient coordination of medical support vehicles through a multi-dimensional evaluation mechanism and an improved algorithm. Compared with the existing technology, the present invention has achieved significant improvements in vehicle group formation efficiency, communication reliability, mission adaptability and survivability, and provides a complete and practical solution for medical support in complex battlefield environments.

[0045] S3: Control the vehicle group to move along the target path, and after reaching the preset position, divide the vehicle group into groups according to the target location of each medical support vehicle.

[0046] Specifically, the grouping process includes dynamic reorganization of the vehicle group and path updating.

[0047] S3.1: Based on the task relevance matrix and the cover effectiveness coefficient, the optimal formation position of each medical support vehicle in the vehicle group is calculated, and an adaptive formation control algorithm is used to control the vehicle group to move along the target path to the preset separation position.

[0048] Among them, the adaptive formation control algorithm dynamically adjusts the vehicle spacing according to the real-time communication quality and surrounding threat intensity during the movement of the vehicle group.

[0049] S3.2: At the preset separation position, a Mahalanobis distance matrix is ​​constructed based on the spatial distribution characteristics of the target location, and a vehicle clustering index is generated in combination with the task relevance matrix. The vehicle groups are preliminarily divided according to the vehicle clustering index. If there are medical support vehicles with overlapping functions in the new vehicle groups after the division, the vehicles with weak overlapping functions will be deployed to other new vehicle groups with the highest functional complementarity, and bait vehicles will be allocated to each new vehicle group based on the cover effectiveness coefficient.

[0050] Among them, the vehicle clustering index is obtained by comprehensively calculating the spatial concentration of the target location and the collaborative combat effectiveness between vehicles.

[0051] S3.3: An ant colony algorithm is used to generate a path for each new vehicle group to its respective target location, and the vehicle grouping index and cover effectiveness coefficient are used as optimization factors for path planning. If it is detected that the paths of different new vehicle groups have overlapping areas, the priority evaluation of yielding between vehicle groups is triggered based on functional complementarity, and a temporary avoidance path is planned for the yielding vehicle group.

[0052] It should be noted that during the execution of the adaptive formation control algorithm, if any medical support vehicle is detected to deviate from the predetermined formation position, the vehicle group configuration will be locally adjusted based on the vehicle grouping index; the local adjustment includes re-evaluating the functional complementarity of the surrounding medical support vehicles and dynamically updating the relative position relationship and communication topology between vehicles while maintaining the optimal cover effectiveness coefficient.

[0053] In summary, the present invention has achieved remarkable technical effects in practical applications through multi-dimensional evaluation mechanism and improved algorithm design: First, based on the multi-layer information superposition mechanism of the dangerous area distribution map, combined with the Kalman filtering algorithm, the real-time dynamic update of battlefield environment information is realized; secondly, by introducing the dynamic evaluation mechanism of the task relevance matrix and the vehicle grouping index, the overall functional coverage of the vehicle group is improved, and the risk of communication interruption is significantly reduced in a strong electromagnetic interference environment; thirdly, the adaptive formation control algorithm and the dynamic deployment mechanism based on functional complementarity enhance the survivability of the vehicle group through dangerous areas, while maintaining a high path planning efficiency; finally, through the evaluation of the cover effectiveness of the bait vehicle and the dynamic evaluation mechanism of the priority of giving way, the task completion efficiency is improved while ensuring the continuity of the task. The realization of these effects is due to the breakthroughs of the present invention in algorithm improvement and mechanism innovation, which cannot be achieved by a simple combination of non-existing technologies.

[0054] Embodiment 2 is an embodiment of the present invention, which provides a group control system for coordinated medical support of vehicles with different functions, including: a map construction module, which is used to obtain battlefield environment information and target location information, construct an electronic map containing dangerous areas according to the battlefield environment information, and mark the initial position and target location of the medical support vehicle in the electronic map;

[0055] The path generation module is used to group medical support vehicles that perform the same task into vehicle groups based on electronic maps, and use a preset algorithm to generate a target path for the vehicle group to cross the dangerous area;

[0056] The control module is used to control the movement of the vehicle group along the target path, and after reaching the preset position, the vehicle group is divided into groups according to the target location of each medical support vehicle.

[0057] Example 3, reference Figure 2, is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0058] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0059] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0060] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0061] Example 4 is an embodiment of the present invention, which provides a group control method for coordinated medical support of vehicles with different functions. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0062] To verify the effectiveness of the present invention, this embodiment constructs a simulation platform that simulates a battlefield environment, which includes complex environmental factors such as various types of terrain features, dynamic threat sources, and electromagnetic interference. The experiment uses 10 medical support vehicles with different functions, including 4 field ambulances, 2 equipment maintenance vehicles, 2 electronic maintenance vehicles, and 2 bait vehicles. The experimental scene sets up 3 combat areas of different intensities and 2 enemy-occupied areas, and randomly distributes multiple target points that require medical support in the battlefield environment. To ensure the scientificity and repeatability of the experiment, this embodiment uses the Monte Carlo method to perform 1,000 simulation tests, and the initial conditions and environmental parameters of each test are randomly generated within a preset range.

[0063] During the experiment, the performance differences between the present invention and three prior art solutions were compared, including the traditional control method based on fixed formations (Scheme A), the distributed control method based on behavioral rules (Scheme B), and the method based on centralized hierarchical control (Scheme C). The evaluation indicators cover multiple dimensions such as task completion time, system robustness, functional coverage, and resource utilization efficiency. In particular, this embodiment designs a comprehensive performance evaluation system, which unifies various indicators into the [0,1] interval by weighted summation. In extreme case tests, this embodiment also simulates various abnormal situations such as communication interruption, vehicle failure, and sudden threats to verify the fault tolerance and adaptability of each solution.

[0064] Table 1 Statistical table of experimental data for scheme comparison

[0065] Evaluation Metrics The present invention Plan A Plan B Plan C Average task completion time (min) 42.3 68.5 55.7 51.2 Communication interruption recovery time (s) 2.8 8.6 5.4 4.9 Success rate of vehicle group reorganization (%) 94.6 75.3 82.1 85.7 Function coverage (%) 92.8 71.4 78.9 81.5 Path planning delay (ms) 185 142 256 221 Threat avoidance success rate (%) 96.2 78.9 85.3 88.1 Resource utilization (%) 90.5 72.8 79.4 82.3 System fault tolerance (%) 93.7 70.2 81.5 84.2 Task coordination effectiveness 0.925 0.684 0.775 0.812 Overall Rating 0.932 0.681 0.793 0.835

[0066] As shown in Table 1, through in-depth analysis of experimental data, the present invention has shown significant advantages in multiple key indicators. From the perspective of task completion time, the present invention takes an average of 42.3 minutes, which is still 17.4% faster than the closest solution C, mainly due to the improved adaptive formation control algorithm and efficient dynamic grouping mechanism. In terms of system robustness, the communication interruption recovery time of the present invention is only 2.8 seconds, which is 67.4% higher than the traditional solution, and the success rate of vehicle group reorganization reaches 94.6%, which fully proves the effectiveness of the dynamic allocation mechanism based on the vehicle grouping index. It is particularly noteworthy that in terms of function coverage and resource utilization, two indicators reflecting the overall effectiveness of the system, the present invention reaches 92.8% and 90.5% respectively, which is far ahead of other solutions. This shows that the collaborative control strategy based on functional complementarity proposed by the present invention can better balance task requirements and resource allocation. In terms of comprehensive scoring, the present invention scored 0.932, which is 11.6% higher than the suboptimal solution, highlighting the comprehensive advantages of the solution in a complex battlefield environment. It is worth noting that although the path planning delay of the present invention is slightly higher than that of Solution A, this slight delay increase is acceptable considering the significantly improved planning quality and threat avoidance capabilities. This comprehensive set of experimental data strongly proves that the technical innovations of the present invention in vehicle cooperative control, dynamic task allocation and path planning have indeed brought substantial performance improvements.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A group control method for coordinated medical support of vehicles with different functions, characterized in that: include: Acquire battlefield environment information and target location information, construct an electronic map containing dangerous areas according to the battlefield environment information, and mark the initial position and target location of the medical support vehicle in the electronic map; Based on the electronic map, medical support vehicles that perform the same task are grouped into a vehicle group, and a preset algorithm is used to generate a target path for the vehicle group to cross the dangerous area; the overall survival probability of the vehicle group is considered during the generation of the target path; The vehicle group is controlled to move along the target path, and after reaching the preset position, the vehicle group is grouped according to the target location of each medical support vehicle; the grouping process includes dynamic reorganization of the vehicle group and path update.

2. The group control method for coordinated medical support of vehicles with different functions as claimed in claim 1 is characterized by: The battlefield environment information includes the location and type information of the dangerous area; The step of obtaining battlefield environment information and target location information, and constructing an electronic map containing dangerous areas according to the battlefield environment information comprises: Collecting the combat area information and the enemy-occupied area information in the battlefield environment, and fusing the combat area information and the enemy-occupied area information based on a preset fusion algorithm to obtain a dangerous area distribution map; the dangerous area distribution map includes spatial location information and danger level information of the dangerous area; The dangerous area distribution map is mapped into a preset coordinate system to form an electronic map, and the initial position of the medical support vehicle and the position information of each target location are marked on the electronic map.

3. The group control method for coordinated medical support of vehicles with different functions as described in claim 2 is characterized by: The preset fusion algorithm includes: Periodically sampling the combat area information and the enemy-occupied area information to obtain dynamic change data of the battlefield environment; Based on the dynamically changing data, the danger level information of the dangerous area is updated in real time, wherein the danger level information is determined by evaluating the threat degree of different types of dangerous areas to medical support vehicles; The updated dangerous area distribution map is superimposed with the terrain feature information to form an electronic map containing multiple layers of information.

4. The group control method for coordinated medical support of vehicles with different functions as claimed in claim 3 is characterized by: The method of forming a vehicle group of medical support vehicles that perform the same task based on the electronic map and using a preset algorithm to generate a target path for the vehicle group to cross the dangerous area includes the following steps: Based on the danger level information in the electronic map and the functional attributes of each medical support vehicle, a task relevance matrix is ​​constructed, and a spectral clustering algorithm is used to analyze the task relevance matrix, and medical support vehicles whose task relevance is greater than a first preset threshold are grouped into a vehicle group; The task relevance matrix includes functional complementarity and synergy between vehicles; The task execution capability of each medical support vehicle in the vehicle group is evaluated, and if there is a medical support vehicle with an execution capability lower than a second preset threshold, the task of the vehicle is reallocated to the medical support vehicle with the highest execution capability, otherwise the original task allocation is maintained; the task execution capability is calculated based on the remaining energy, loading status and maneuverability of the vehicle; Based on the danger level information, an A* algorithm is used to generate an initial path for the vehicle group, and the initial path is dynamically optimized in combination with a reinforcement learning method to obtain a target path that takes into account the overall survival probability of the vehicle group; If the survival probability of the target path is lower than a third preset threshold, the path is replanned until the survival probability requirement is met.

5. The group control method for coordinated medical support of vehicles with different functions as claimed in claim 4 is characterized by: The process of constructing the task relevance matrix also includes: Constructing a connectivity constraint matrix based on the communication capabilities between vehicles, and fusing the connectivity constraint matrix with the functional complementarity matrix; If the vehicle group includes a decoy vehicle, the shielding effect of other medical support vehicles is evaluated based on the mobility of the decoy vehicle to generate a shielding effectiveness coefficient; When performing task reallocation, a hierarchical optimization algorithm that takes the cover effectiveness coefficient into consideration is adopted to update the task relevance matrix with the goal of maximizing the overall collaborative effectiveness of the vehicle group.

6. The group control method for coordinated medical support of vehicles with different functions as claimed in claim 5 is characterized by: Controlling the vehicle group to move along the target path, and after reaching the preset position, grouping the vehicles according to the target locations of the medical support vehicles, including the following steps: Based on the task relevance matrix and the cover effectiveness coefficient, the optimal formation position of each medical support vehicle in the vehicle group is calculated, and an adaptive formation control algorithm is used to control the vehicle group to move along the target path to a preset separation position; wherein the adaptive formation control algorithm dynamically adjusts the vehicle spacing according to the real-time communication quality and the surrounding threat intensity during the movement of the vehicle group; At the preset separation position, a Mahalanobis distance matrix is ​​constructed based on the spatial distribution characteristics of the target location, and a vehicle grouping index is generated in combination with the task relevance matrix, and the vehicle group is preliminarily divided according to the vehicle grouping index; if there are medical support vehicles with overlapping functions in the divided new vehicle group, the vehicles with weak overlapping functions are deployed to other new vehicle groups with the highest functional complementarity, and bait vehicles are allocated to each new vehicle group based on the cover effectiveness coefficient; wherein the vehicle grouping index is obtained by comprehensively calculating the spatial aggregation degree of the target location and the coordinated combat effectiveness between vehicles; An ant colony algorithm is used to generate a travel path for each new vehicle group to its respective target location, and the vehicle grouping index and the cover effectiveness coefficient are used as optimization factors for path planning; if it is detected that the travel paths of different new vehicle groups have overlapping areas, the yielding priority evaluation between the vehicle groups is triggered based on functional complementarity, and a temporary avoidance path is planned for the yielding vehicle group.

7. The group control method for coordinated medical support of vehicles with different functions as claimed in claim 6 is characterized by: During the execution of the adaptive formation control algorithm, if it is detected that any medical support vehicle deviates from the predetermined formation position, the vehicle group configuration is locally adjusted based on the vehicle grouping index; the local adjustment includes re-evaluating the functional complementarity of the surrounding medical support vehicles and dynamically updating the relative position relationship and communication topology between vehicles while maintaining the optimal cover effectiveness coefficient.

8. A group control system for coordinated medical support of vehicles with different functions, based on the group control method for coordinated medical support of vehicles with different functions as claimed in any one of claims 1 to 7, characterized in that: include, A map construction module, used to obtain battlefield environment information and target location information, construct an electronic map containing dangerous areas according to the battlefield environment information, and mark the initial position and target location of the medical support vehicle in the electronic map; A path generation module, for grouping medical support vehicles that perform the same task into a vehicle group based on the electronic map, and using a preset algorithm to generate a target path for the vehicle group to cross the dangerous area; The control module is used to control the vehicle group to move along the target path, and after reaching the preset position, group the vehicles according to the target location of each medical support vehicle.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the group control method for collaborative medical support of vehicles with different functions as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the group control method for collaborative medical support of vehicles with different functions as described in any one of claims 1 to 7 are implemented.

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