A data processing method and device based on local information interaction

Abstract

The present invention discloses a data processing method and device based on local information interaction, the method comprising: obtaining exploration mission area information and drone group information; the exploration mission area information comprises M exploration mission grid information; the drone group information comprises N drone information; the exploration mission grid information comprises a first exploration time exploration value, grid coordinate information, and grid type; the drone information comprises drone exploration value information, drone coordinate information, and drone exploration radius; the drone exploration value information comprises M drone mission grid exploration value information; based on the exploration mission area information and the drone group information, determining target drone exploration information; the target drone exploration information comprises M target drone exploration value information; based on the target drone exploration information, determining target area exploration result information; the target area exploration result information comprises M target mission grid exploration result information.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a data processing method and device based on local information interaction. Background Art

[0002] In the process of multi-UAV collaboration, the cooperative control system needs to have the capabilities of high reliability, strong robustness, rapid response and rapid reorganization. The formation control methods of the cluster mainly include behavior-based formation control methods, virtual structure-based formation control methods, artificial potential field-based formation control methods, leader-follower formation control methods and consistency formation control methods. Although fruitful results have been achieved in the theory of multi-UAV formation flight, physical flight tests can only achieve cooperative formation flight in a simple communication environment, and the corresponding robustness of the control method is low. Therefore, a data processing method and device are provided to improve the autonomous decision-making ability of UAV swarms in complex working environments and improve the accuracy and efficiency of rapid exploration of unknown spaces. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a data processing method and device based on local information interaction, which is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a data processing method, which includes:

[0005] Acquire exploration mission area information and drone group information; the exploration mission area information includes M exploration mission grid information; the drone group information includes N drone information; the exploration mission grid information includes a first exploration time exploration value, grid coordinate information, and grid type; the drone information includes drone exploration value information, drone coordinate information, and drone exploration radius; the drone exploration value information includes the M drone mission grid exploration value information; each drone mission grid exploration value information corresponds to one exploration mission grid information;

[0006] Based on the exploration mission area information and the drone group information, target drone exploration information is determined; the target drone exploration information includes the M target drone exploration value information;

[0007] Based on the target UAV exploration information, target area exploration result information is determined; the target area exploration result information includes the M target task grid exploration result information.

[0008] A second aspect of an embodiment of the present invention discloses a data processing device, the device comprising:

[0009] An acquisition module is used to acquire exploration mission area information and drone group information; the exploration mission area information includes M exploration mission grid information; the drone group information includes N drone information; the exploration mission grid information includes a first exploration time exploration value, grid coordinate information, and grid type; the drone information includes drone exploration value information, drone coordinate information, and drone exploration radius; the drone exploration value information includes the M drone mission grid exploration value information; each drone mission grid exploration value information corresponds to one exploration mission grid information;

[0010] A first determination module is used to determine target drone exploration information based on the exploration mission area information and the drone group information; the target drone exploration information includes the M target drone exploration value information;

[0011] The second determination module is used to determine the target area exploration result information based on the target UAV exploration information; the target area exploration result information includes the M target task grid exploration result information.

[0012] A third aspect of the present invention discloses another data processing device, the device comprising:

[0013] A memory storing executable program code;

[0014] a processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory to execute part or all of the steps in the data processing method disclosed in the first aspect of the embodiment of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the data processing method disclosed in the first aspect of the embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. 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.

[0018] Figure 1 is a schematic diagram of a scenario of a data processing system provided by an embodiment of the present invention;

[0019] Figure 2It is a flowchart of a data processing method disclosed in an embodiment of the present invention;

[0020] Figure 3 is a structural schematic diagram of a data processing device disclosed in an embodiment of the present invention;

[0021] Figure 4 It is a structural schematic diagram of another data processing device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in 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 creative work are within the scope of protection of the present invention.

[0023] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0024] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person 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 will not be 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 the present application.

[0026] 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, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0027] 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 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.

[0028] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0029] Computer Vision (CV) is a science that studies how to make machines "see". To put it more specifically, it refers to machine vision such as using cameras and computers to replace human eyes to identify and measure targets, and further perform graphic processing so that computer processing becomes an image that is more suitable for human eye observation or transmission to instrument detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and map construction, and other technologies, as well as common biometric recognition technologies such as face recognition and fingerprint recognition.

[0030] Unimodal information is data of only one type, such as text, image, audio, video, electromagnetic signal, etc. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved on the task.

[0031] 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 often 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 an embodiment of the present application, the large model may 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, and Wenxin Yiyan scale language models, which are not limited in the embodiments of the present application.

[0032] The embodiments of the present application provide a data processing method, apparatus, computer device, and computer-readable storage medium, which are described in detail below.

[0033] See also Figure 1 , Figure 1Schematic diagram of a data processing system provided in an embodiment of the present application. The data processing system may include a computer device 100, in which a data processing device is integrated, such as Figure 1 Computer equipment in.

[0034] In the embodiment of the present application, the computer device 100 is mainly used to obtain exploration task area information and drone group information; the exploration task area information includes M exploration task grid information; the drone group information includes N drone information; the exploration task grid information includes the first exploration time exploration value, grid coordinate information, and grid type; the drone information includes drone exploration value information, drone coordinate information, and drone exploration radius; the drone exploration value information includes M drone task grid exploration value information; each drone task grid exploration value information corresponds to one exploration task grid information;

[0035] Based on the exploration mission area information and the drone group information, the target drone exploration information is determined; the target drone exploration information includes M target drone exploration value information;

[0036] Based on the target UAV exploration information, the target area exploration result information is determined; the target area exploration result information includes M target task grid exploration result information.

[0037] It can improve the autonomous decision-making ability of drone swarms in complex working environments and improve the accuracy and efficiency of rapid exploration of unknown spaces.

[0038] In the embodiment 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 embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0039] It is understandable that the computer device 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0040] Those skilled in the art will understand that Figure 1The 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 less computer equipment as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the data processing system may also include one or more other services, which are not specifically limited here.

[0041] In addition, if Figure 1 As shown, the data processing system may further include a memory 200 for storing data, such as image data, location information, and the like.

[0042] It should be noted that Figure 1 The scenario diagram of the data processing system shown is merely an example. The data processing system and scenario described in the embodiments of the present application are intended to more clearly illustrate the technical solution of the embodiments of the present application, and do not constitute a limitation on the technical solution provided in the embodiments of the present application. A person of ordinary skill in the art will appreciate that with the evolution of the data processing system and the emergence of new business scenarios, the technical solution provided in the embodiments of the present application is equally applicable to similar technical problems.

[0043] The present invention discloses a data processing method and device based on local information interaction, which is beneficial to improving the autonomous decision-making ability of drone groups in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces. The following are detailed descriptions.

[0044] Embodiment 1

[0045] See also Figure 2 , Figure 2 : is a flow chart of a data processing method disclosed in an embodiment of the present invention. Figure 2 The data processing method described is applied to a management system, such as a local server or a cloud server for management, and the embodiments of the present invention do not limit this. Figure 2 As shown, the data processing method may include the following operations:

[0046] 101. Obtain exploration mission area information and drone group information.

[0047] In an embodiment of the present invention, the exploration mission area information includes M exploration mission grid information; the drone group information includes N drone information; the exploration mission grid information includes the first exploration time exploration value, grid coordinate information, and grid type; the drone information includes drone exploration value information, drone coordinate information, and drone exploration radius; the drone exploration value information includes M drone mission grid exploration value information; each drone mission grid exploration value information corresponds to one exploration mission grid information.

[0048] 102. Based on the exploration mission area information and the drone group information, the target drone exploration information is determined.

[0049] In an embodiment of the present invention, the target drone exploration information includes M target drone exploration value information.

[0050] 103. Based on the target UAV exploration information, determine the target area exploration result information.

[0051] In the embodiment of the present invention, the target area exploration result information includes M target task grid exploration result information.

[0052] It should be noted that the above-mentioned grid types include exploration target types (the task grid has a target object) and non-exploration target types (the task grid does not have a target object), which is not limited in the embodiment of the present invention.

[0053] It should be noted that the above exploration task grid information represents the data information corresponding to each task grid in the exploration task area, which is not limited in the embodiment of the present invention. Furthermore, the above first exploration time exploration value represents the target drone exploration value in the target drone exploration value information corresponding to the previous moment, which is not limited in the embodiment of the present invention. Furthermore, the above grid coordinate information represents the two-dimensional coordinates of the task grid, which is not limited in the embodiment of the present invention.

[0054] It should be noted that the above-mentioned drone information represents the data information of the drone at the current moment, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned drone exploration value information represents the probability of exploring the target object of the task grid in each task grid, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned drone coordinate information represents the two-dimensional coordinates of the task grid where the drone is located at the current moment, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned drone exploration radius represents the maximum radius range that the drone may explore, which is set according to the exploration capability of each drone, which is not limited in the embodiment of the present invention.

[0055] It should be noted that the above-mentioned determination of the target area exploration result information is to determine the grid type of the task grid at the current moment. At the next moment, the task type of the task grid will be adjusted to the grid type corresponding to the target area exploration result information at the current moment, thereby realizing dynamic adjustment of the grid type of the task grid, that is, utilizing the information explored by the drone to realize local information interaction, thereby improving the exploration efficiency of unknown task grids, which is not limited in the embodiments of the present invention.

[0056] It should be noted that the above-mentioned drone coordinate information is the coordinates of the previous moment, and according to the speed and heading of the drone, etc., in the process of transitioning from the previous moment to the current moment, the coordinates of the current moment to which the drone flies are not limited in the embodiment of the present invention. Furthermore, the position, speed and acceleration of the above-mentioned drone can be characterized in the following way:

[0057] The motion equation of the UAV can be expressed as:

[0058]

[0059] make

[0060]

[0061] The dynamic equation of the UAV cluster can be expressed as:

[0062]

[0063] From the equation of motion, we can know that U can be set as the input of the UAV cluster motion control. The speed and acceleration of each individual are limited to a certain range:

[0064]

[0065]

[0066] Among them: U max and V max are the maximum acceleration and maximum speed of the drone respectively.

[0067] When establishing the UAV swarm motion model, the control method based on the biological swarm cognitive model, the individual control quantity can be divided into two parts: individual motion control within the swarm and control to avoid external threats, as shown in the following formula:

[0068] u i (t) = u iα (t)+u iβ (t)

[0069] In the formula, u iα (t) represents the individual motion control quantity within the cluster, which is mainly based on collision avoidance rules to prevent collision with other drones; u iβ (t) represents the control amount to avoid external threats.

[0070]

[0071] Among them, r ij represents the distance from UAV i to UAV j, d r Indicates the maximum safe distance between drones, x i and x jRepresents the position vector of the drone.

[0072] For the control quantity to avoid the threat, let O t is the threat center, r is the threat radius (untouchable), R is the threat impact radius (accessible but starting to be threatened), r it For individual i to obstacle O t The distance, x it Point to obstacle O for individual i t Vector.

[0073] When individual i enters threat O t When the threat impact radius is r<r it <R, threat O t The amount u that starts to repel individual i iβ , so that the individual stays away from obstacles. But when v i The direction of x it When the same, that is, v i / ||v i ||=x it / ||x it ||, the cluster may oscillate in front of the obstacle and be unable to move normally. Therefore, in this case, an action perpendicular to the direction of movement needs to be applied. The threat avoidance control amount can be expressed as:

[0074]

[0075] In the process of collaborative search and positioning of the target, the drone nodes realize grouping, AOA / DOA positioning, pheromone map update and other operations through neighborhood interaction, and then perform subsequent movements based on their own pheromone maps as action guides. Due to the distributed structure and the lack of a central node, each drone cannot perform global pheromone perception and can only perceive partial information. Indirect coordinated perception of global information is achieved through local interaction of pheromones.

[0076] It can be seen that implementing the data processing method described in the embodiment of the present invention is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments, and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0077] In an optional embodiment, the target drone exploration information is determined based on the exploration mission area information and the drone group information, including:

[0078] For any exploration task grid information in the exploration task area information and any drone information in the drone group information, based on the exploration task grid information and the drone information, determine the target task grid exploration value information corresponding to the drone information;

[0079] The exploration value information of all target mission grids is weighted and summed to obtain the target UAV exploration value information corresponding to the exploration mission grid information.

[0080] In this optional embodiment, as an optional implementation, the weighted summation of all target task grid exploration value information is performed to obtain the target drone exploration value information corresponding to the exploration task grid information, including:

[0081] Look up the grid type and exploration value correction coefficient table corresponding to the exploration task grid information to obtain the correction coefficient corresponding to the exploration task grid information;

[0082] The correction model is used to perform weighted summation processing on the exploration value information of all target task grids with the correction coefficient to obtain the exploration value information of the target UAV corresponding to the exploration task grid information;

[0083] The modified model is:

[0084]

[0085] Where MUWRJ represents the exploration value information of the target UAV; MBTS1 i Represents the exploration value information of the i-th target task grid; xs represents the correction coefficient.

[0086] It should be noted that the above exploration value correction coefficient table represents the corresponding relationship between different grid types and correction coefficients. By setting the exploration value correction coefficient table, the correction work of the overall exploration value of the task grid can be dynamically adjusted, and the exploration strategy can be set dynamically and flexibly according to the actual requirements of the task, which is more conducive to improving the accuracy and efficiency of rapid exploration of unknown space. Further, the exploration value correction coefficient table can be:

[0087] Mesh Type Correction factor Explore target types 0.95 Non-exploration target type 1.02

[0088] It can be seen that implementing the data processing method described in the embodiment of the present invention is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments, and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0089] In another optional embodiment, based on the exploration task grid information and the drone information, determining the target task grid exploration value information corresponding to the drone information includes:

[0090] Obtain exploration parameter information corresponding to the exploration value threshold and the exploration task grid information;

[0091] The exploration value threshold, the exploration parameter information, the exploration task grid information and the UAV information are calculated and processed to obtain the target task grid exploration value information corresponding to the UAV information.

[0092] It should be noted that the above exploration parameter information may vary with the exploration task grid information, which is not limited in the embodiment of the present invention.

[0093] It can be seen that implementing the data processing method described in the embodiment of the present invention is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments, and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0094] In another optional embodiment, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information, including:

[0095] Perform distance calculation processing on the grid coordinate information corresponding to the exploration task grid information and the drone coordinate information corresponding to the drone information to obtain a distance value;

[0096] Determine whether the grid type corresponding to the exploration task grid information is the exploration target type, and obtain a type determination result;

[0097] When the type judgment result is yes, based on the size relationship between the drone exploration radius and the distance value corresponding to the drone information, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information;

[0098] When the type judgment result is no, based on the relationship between the drone task grid exploration value and the exploration value threshold in the drone task grid exploration value information corresponding to the drone information, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information.

[0099] It should be noted that the distance calculation process for the grid coordinate information corresponding to the exploration mission grid information and the drone coordinate information corresponding to the drone information is performed using two-dimensional coordinates, which is not limited in the embodiment of the present invention.

[0100] It can be seen that implementing the data processing method described in the embodiment of the present invention is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments, and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0101] In another optional embodiment, based on the size relationship between the drone exploration radius and the distance value corresponding to the drone information, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information, including:

[0102] Determine whether the drone exploration radius corresponding to the drone information is less than or equal to the distance value, and obtain a distance determination result;

[0103] When the distance judgment result is no, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed using the first exploration value calculation model to obtain the first backup task grid exploration value information corresponding to the drone information;

[0104] Among them, the first exploration value calculation model is:

[0105]

[0106] In the formula, MBTS1 represents the target task grid exploration value information; WRJTS1 represents the UAV task grid exploration value information; WRJR represents the UAV exploration radius; d represents the distance value; α represents the first exploration parameter in the exploration parameter information;

[0107] Determine whether the first standby task grid exploration value corresponding to the first standby task grid exploration value information is greater than or equal to an exploration value threshold, and obtain a first threshold determination result;

[0108] When the first threshold judgment result is yes, based on the exploration value threshold, the target task grid exploration value information corresponding to the drone information is determined;

[0109] When the first threshold judgment result is no, determining the first backup task grid exploration value information corresponding to the drone information as the target task grid exploration value information corresponding to the drone information;

[0110] When the distance judgment result is yes, the UAV mission grid exploration value information corresponding to the UAV information is determined as the target mission grid exploration value information corresponding to the UAV information.

[0111] It should be noted that the above exploration value threshold represents that the exploration positioning accuracy of a certain task grid reaches the exploration peak requirement. When the exploration value reaches the threshold, the exploration value of the task grid will no longer be increased to avoid attracting more drones to explore the adjacent area of ​​this task grid, resulting in a waste of drone exploration resources. Instead, the drone should be explored toward other task grids to discover more target objects, which is not limited in the embodiments of the present invention. Furthermore, the above exploration value threshold can be set by the user, or it can be a default value given by the system, or it can be determined by averaging the historical exploration value thresholds, which is not limited in the embodiments of the present invention.

[0112] It should be noted that the above-mentioned determination of the target task grid exploration value information corresponding to the drone information based on the exploration value threshold is to use the exploration value threshold as the target task grid exploration value corresponding to the target task grid exploration value information, which is not limited in the embodiment of the present invention.

[0113] It should be noted that the above-mentioned use of the drone exploration radius and the distance value for analysis and judgment to increase or decrease the exploration value of a certain task grid is to characterize the task grid that has been explored and located to have a target object before, reduce its exploration value (drone exploration radius is greater than the distance value), and transmit this information to other drones, thereby reducing the probability of other drones exploring the task grid, avoiding repeated searches by too many drones, and thus improving the efficiency of drone collaborative exploration. The embodiments of the present invention are not limited to this.

[0114] It can be seen that implementing the data processing method described in the embodiment of the present invention is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments, and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0115] In an optional embodiment, the above-mentioned relationship between the drone task grid exploration value and the exploration value threshold in the drone task grid exploration value information corresponding to the drone information is calculated and processed on the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information to obtain the target task grid exploration value information corresponding to the drone information, including:

[0116] Determine whether the drone task grid exploration value in the drone task grid exploration value information corresponding to the drone information is equal to the exploration value threshold, and obtain a second threshold judgment result;

[0117] When the second threshold judgment result is yes, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed using the second exploration value calculation model to obtain the target task grid exploration value information corresponding to the drone information;

[0118] Among them, the second exploration value calculation model is:

[0119]

[0120] In the formula, β 1 represents the second exploration parameter in the exploration parameter information; γ represents the third exploration parameter in the exploration parameter information;

[0121] When the second threshold judgment result is no, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed using the third exploration value calculation model to obtain the second backup task grid exploration value information corresponding to the drone information;

[0122] Among them, the third exploration value calculation model is:

[0123]

[0124] In the formula, β 2 Characterizing a fourth exploration parameter in the exploration parameter information;

[0125] Determine whether the second standby task grid exploration value corresponding to the second standby task grid exploration value information is greater than or equal to the exploration value threshold, and obtain a third threshold judgment result;

[0126] When the third threshold judgment result is yes, based on the exploration value threshold, the exploration value information of the target task grid corresponding to the drone information is determined;

[0127] When the result of the third threshold judgment is no, the second backup task grid exploration value information corresponding to the drone information is determined as the target task grid exploration value information corresponding to the drone information.

[0128] It should be noted that the above-mentioned second exploration parameter and fourth exploration coefficient represent the attenuation coefficient and the enhancement coefficient respectively, and their values ​​are both between 0 and 1, which are not limited in the embodiment of the present invention. Further, through the addition and subtraction of the attenuation coefficient and the enhancement coefficient, it can be characterized that the local information explored by the drone is used to guide other drones to explore in the opposite direction of a task grid that has already found a target (reaching the exploration accuracy, that is, a task grid equal to the exploration value threshold) or deviate from this task grid to increase the probability of exploring the task grids of other task grids, and at the same time, increase the attractiveness of a task grid that has not found a target, so as to increase the exploration intensity of the task grid, thereby increasing the probability that it may find the target object, and thus more conducive to the detection of the target object of the task grid, which is not limited in the embodiment of the present invention.

[0129] It should be noted that the third exploration parameter represents the diffusion coefficient, that is, the diffusion range of the information transmitted by the drone, and its value can be set by the user, or it can be a default value given by the system, or it can be determined by averaging the historical third exploration parameter, which is not limited in the embodiment of the present invention. Furthermore, the third exploration parameter is greater than 1, but less than the exploration radius of the drone, which is not limited in the embodiment of the present invention.

[0130] It can be seen that implementing the data processing method described in the embodiment of the present invention is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments, and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0131] In another optional embodiment, based on the target UAV exploration information, the target area exploration result information is determined, including:

[0132] For any target task grid exploration value information in the target UAV exploration information, determine whether the target task grid exploration value corresponding to the target task grid exploration value information is greater than or equal to the target exploration threshold, and obtain an exploration judgment result;

[0133] When the exploration judgment result is yes, determining that the task grid has a target is the target task grid exploration result information corresponding to the target task grid exploration value information;

[0134] When the exploration judgment result is no, it is determined that the task grid does not exist, and the target is the target task grid exploration result information corresponding to the target task grid exploration value information.

[0135] It should be noted that the above target task grid exploration result information represents whether the task grid corresponding to the current moment has found the target, which is not limited in the embodiment of the present invention. Further, at the next moment, the grid type of the task grid is adjusted according to the target task grid exploration result information. If the target task grid exploration result information at the current moment indicates that the task grid has a target, then at the next moment, the grid type of the task grid is adjusted to the exploration target type, otherwise it is adjusted to the non-exploration target type, which is not limited in the embodiment of the present invention.

[0136] It should be noted that the above target exploration threshold may be set by a user, may be a default value given by the system, or may be determined by averaging historical target exploration thresholds, which is not limited in the embodiment of the present invention.

[0137] It can be seen that implementing the data processing method described in the embodiment of the present invention is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments, and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0138] Embodiment 2

[0139] See also Figure 3 , Figure 3 is a schematic diagram of the structure of a data processing device disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 3 As shown, the device may include:

[0140] The acquisition module 201 is used to acquire exploration mission area information and drone group information; the exploration mission area information includes M exploration mission grid information; the drone group information includes N drone information; the exploration mission grid information includes the first exploration time exploration value, grid coordinate information, and grid type; the drone information includes drone exploration value information, drone coordinate information, and drone exploration radius; the drone exploration value information includes M drone mission grid exploration value information; each drone mission grid exploration value information corresponds to one exploration mission grid information;

[0141] The first determination module 202 is used to determine the target drone exploration information based on the exploration mission area information and the drone group information; the target drone exploration information includes M target drone exploration value information;

[0142] The second determination module 203 is used to determine the target area exploration result information based on the target UAV exploration information; the target area exploration result information includes M target task grid exploration result information.

[0143] It can be seen that implementation Figure 3 The described data processing device is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0144] In another optional embodiment, Figure 3 As shown, the first determination module 202 determines the target drone exploration information based on the exploration mission area information and the drone group information, including:

[0145] For any exploration task grid information in the exploration task area information and any drone information in the drone group information, based on the exploration task grid information and the drone information, determine the target task grid exploration value information corresponding to the drone information;

[0146] The exploration value information of all target mission grids is weighted and summed to obtain the target UAV exploration value information corresponding to the exploration mission grid information.

[0147] It can be seen that implementation Figure 3 The described data processing device is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0148] In yet another optional embodiment, Figure 3 As shown, the first determination module 202 determines the target task grid exploration value information corresponding to the drone information based on the exploration task grid information and the drone information, including:

[0149] Obtain exploration parameter information corresponding to the exploration value threshold and the exploration task grid information;

[0150] The exploration value threshold, the exploration parameter information, the exploration task grid information and the UAV information are calculated and processed to obtain the target task grid exploration value information corresponding to the UAV information.

[0151] It can be seen that implementation Figure 3 The described data processing device is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0152] In yet another optional embodiment, Figure 3 As shown, the first determination module 202 calculates and processes the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information to obtain the target task grid exploration value information corresponding to the drone information, including:

[0153] Perform distance calculation processing on the grid coordinate information corresponding to the exploration task grid information and the drone coordinate information corresponding to the drone information to obtain a distance value;

[0154] Determine whether the grid type corresponding to the exploration task grid information is the exploration target type, and obtain a type determination result;

[0155] When the type judgment result is yes, based on the size relationship between the drone exploration radius and the distance value corresponding to the drone information, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information;

[0156] When the type judgment result is no, based on the relationship between the drone task grid exploration value and the exploration value threshold in the drone task grid exploration value information corresponding to the drone information, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information.

[0157] It can be seen that implementation Figure 3 The described data processing device is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0158] In yet another optional embodiment, Figure 3As shown, the first determination module 202 calculates and processes the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information based on the size relationship between the drone exploration radius and the distance value corresponding to the drone information, and obtains the target task grid exploration value information corresponding to the drone information, including:

[0159] Determine whether the drone exploration radius corresponding to the drone information is less than or equal to the distance value, and obtain a distance determination result;

[0160] When the distance judgment result is no, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed using the first exploration value calculation model to obtain the first backup task grid exploration value information corresponding to the drone information;

[0161] Among them, the first exploration value calculation model is:

[0162]

[0163] In the formula, MBTS1 represents the target task grid exploration value information; WRJTS1 represents the UAV task grid exploration value information; R represents the UAV exploration radius; d represents the distance value; α represents the first exploration parameter in the exploration parameter information;

[0164] Determine whether the first standby task grid exploration value corresponding to the first standby task grid exploration value information is greater than or equal to an exploration value threshold, and obtain a first threshold determination result;

[0165] When the first threshold judgment result is yes, based on the exploration value threshold, the target task grid exploration value information corresponding to the drone information is determined;

[0166] When the first threshold judgment result is no, determining the first backup task grid exploration value information corresponding to the drone information as the target task grid exploration value information corresponding to the drone information;

[0167] When the distance judgment result is yes, the UAV mission grid exploration value information corresponding to the UAV information is determined as the target mission grid exploration value information corresponding to the UAV information.

[0168] It can be seen that implementation Figure 3 The described data processing device is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0169] In yet another optional embodiment, Figure 3As shown, the first determination module 202 calculates and processes the exploration value threshold, the exploration parameter information, the exploration task grid information, and the drone information based on the size relationship between the drone task grid exploration value information corresponding to the drone information and the exploration value threshold, and obtains the target task grid exploration value information corresponding to the drone information, including:

[0170] Determine whether the drone task grid exploration value in the drone task grid exploration value information corresponding to the drone information is equal to the exploration value threshold, and obtain a second threshold judgment result;

[0171] When the second threshold judgment result is yes, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed using the second exploration value calculation model to obtain the target task grid exploration value information corresponding to the drone information;

[0172] Among them, the second exploration value calculation model is:

[0173]

[0174] In the formula, β 1 represents the second exploration parameter in the exploration parameter information; γ represents the third exploration parameter in the exploration parameter information;

[0175] When the second threshold judgment result is no, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed using the third exploration value calculation model to obtain the second backup task grid exploration value information corresponding to the drone information;

[0176] Among them, the third exploration value calculation model is:

[0177]

[0178] In the formula, β 2 Characterizing a fourth exploration parameter in the exploration parameter information;

[0179] Determine whether the second standby task grid exploration value corresponding to the second standby task grid exploration value information is greater than or equal to the exploration value threshold, and obtain a third threshold judgment result;

[0180] When the third threshold judgment result is yes, based on the exploration value threshold, the exploration value information of the target task grid corresponding to the drone information is determined;

[0181] When the result of the third threshold judgment is no, the second backup task grid exploration value information corresponding to the drone information is determined as the target task grid exploration value information corresponding to the drone information.

[0182] It can be seen that implementation Figure 3The described data processing device is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0183] In yet another optional embodiment, Figure 3 As shown, the second determination module 203 determines the target area exploration result information based on the target UAV exploration information, including:

[0184] For any target task grid exploration value information in the target UAV exploration information, determine whether the target task grid exploration value corresponding to the target task grid exploration value information is greater than or equal to the target exploration threshold, and obtain an exploration judgment result;

[0185] When the exploration judgment result is yes, determining that the task grid has a target is the target task grid exploration result information corresponding to the target task grid exploration value information;

[0186] When the exploration judgment result is no, it is determined that the task grid does not exist, and the target is the target task grid exploration result information corresponding to the target task grid exploration value information.

[0187] It can be seen that implementation Figure 3 The described data processing device is conducive to improving the autonomous decision-making ability of drone swarms in complex working environments and improving the accuracy and efficiency of rapid exploration of unknown spaces.

[0188] Embodiment 3

[0189] See also Figure 4 , Figure 4 is a structural diagram of another data processing device disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 4 As shown, the device may include:

[0190] A memory 301 storing executable program codes;

[0191] a processor 302 coupled to the memory 301;

[0192] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the data processing method described in the first embodiment.

[0193] Embodiment 4

[0194] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the data processing method described in the first embodiment.

[0195] Embodiment 5

[0196] 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 data processing method described in the first embodiment.

[0197] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0198] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes 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 rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0199] Finally, it should be noted that the data processing method and device based on local information interaction disclosed in the embodiment of the present invention only discloses the preferred embodiments of the present invention, which are 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, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire exploration mission area information and drone group information; the exploration mission area information includes M exploration mission grid information; the drone group information includes N drone information; the exploration mission grid information includes a first exploration time exploration value, grid coordinate information, and grid type; the drone information includes drone exploration value information, drone coordinate information, and drone exploration radius; the drone exploration value information includes M drone mission grid exploration value information; each drone mission grid exploration value information corresponds to one exploration mission grid information; Based on the exploration mission area information and the drone group information, target drone exploration information is determined; the target drone exploration information includes M target drone exploration value information; Based on the target UAV exploration information, target area exploration result information is determined; the target area exploration result information includes the M target task grid exploration result information; Wherein, determining the target drone exploration information based on the exploration mission area information and the drone group information includes: For any exploration task grid information in the exploration task area information and for any drone information in the drone group information, based on the exploration task grid information and the drone information, determine the target task grid exploration value information corresponding to the drone information; Performing weighted summation processing on all the target task grid exploration value information to obtain the target UAV exploration value information corresponding to the exploration task grid information; Wherein, determining the target task grid exploration value information corresponding to the drone information based on the exploration task grid information and the drone information includes: Obtain exploration parameter information corresponding to the exploration value threshold and the exploration task grid information; the exploration value threshold represents that the exploration positioning accuracy of a certain task grid reaches the exploration peak requirement. When the exploration value reaches the threshold, the exploration value of the task grid is no longer increased to avoid attracting more drones to explore the adjacent area of ​​the task grid, resulting in a waste of drone exploration resources. Instead, the drone should be directed to explore other task grids to discover more target objects; The exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information.

2. The data processing method according to claim 1, characterized in that: The step of calculating and processing the exploration value threshold, the exploration parameter information, the exploration task grid information, and the drone information to obtain the target task grid exploration value information corresponding to the drone information includes: Perform distance calculation processing on the grid coordinate information corresponding to the exploration task grid information and the drone coordinate information corresponding to the drone information to obtain a distance value; Determine whether the grid type corresponding to the exploration task grid information is the exploration target type, and obtain a type determination result; When the type judgment result is yes, based on the relationship between the drone exploration radius corresponding to the drone information and the distance value, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information; When the type judgment result is no, based on the relationship between the drone task grid exploration value in the drone task grid exploration value information corresponding to the drone information and the exploration value threshold, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information.

3. The data processing method according to claim 2, characterized in that: The step of calculating and processing the exploration value threshold, the exploration parameter information, the exploration task grid information, and the drone information based on the relationship between the drone exploration radius corresponding to the drone information and the distance value to obtain the target task grid exploration value information corresponding to the drone information includes: Determine whether the drone exploration radius corresponding to the drone information is less than or equal to the distance value, and obtain a distance determination result; When the distance judgment result is no, the exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed using the first exploration value calculation model to obtain the first backup task grid exploration value information corresponding to the drone information; Among them, the first exploration value calculation model is: Wherein, MBTS1 represents the target task grid exploration value information; WRJTS1 represents the UAV task grid exploration value information; R represents the UAV exploration radius; d represents the distance value; α represents the first exploration parameter in the exploration parameter information; Determine whether the first standby task grid exploration value corresponding to the first standby task grid exploration value information is greater than or equal to the exploration value threshold, and obtain a first threshold judgment result; When the first threshold judgment result is yes, based on the exploration value threshold, determining the target task grid exploration value information corresponding to the drone information; When the first threshold judgment result is no, determining the first backup task grid exploration value information corresponding to the drone information as the target task grid exploration value information corresponding to the drone information; When the distance judgment result is yes, the drone mission grid exploration value information corresponding to the drone information is determined as the target mission grid exploration value information corresponding to the drone information.

4. The data processing method according to claim 2, characterized in that: The step of calculating and processing the exploration value threshold, the exploration parameter information, the exploration task grid information, and the drone information based on the relationship between the drone task grid exploration value information and the exploration value threshold in the drone task grid exploration value information corresponding to the drone information to obtain the target task grid exploration value information corresponding to the drone information includes: Determine whether the drone task grid exploration value in the drone task grid exploration value information corresponding to the drone information is equal to the exploration value threshold, and obtain a second threshold judgment result; When the second threshold judgment result is yes, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed using the second exploration value calculation model to obtain the target task grid exploration value information corresponding to the drone information; Among them, the second exploration value calculation model is: In the formula, β1 represents the second exploration parameter in the exploration parameter information; γ represents the third exploration parameter in the exploration parameter information; When the second threshold judgment result is no, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed by using a third exploration value calculation model to obtain the second backup task grid exploration value information corresponding to the drone information; Wherein, the third exploration value calculation model is: Wherein, β2 represents the fourth exploration parameter in the exploration parameter information; Determine whether the second standby task grid exploration value corresponding to the second standby task grid exploration value information is greater than or equal to the exploration value threshold, and obtain a third threshold judgment result; When the third threshold judgment result is yes, based on the exploration value threshold, determining the target task grid exploration value information corresponding to the drone information; When the third threshold judgment result is no, the second backup task grid exploration value information corresponding to the drone information is determined as the target task grid exploration value information corresponding to the drone information.

5. The data processing method according to claim 1, characterized in that: The step of determining the target area exploration result information based on the target UAV exploration information includes: For any of the target task grid exploration value information in the target UAV exploration information, determine whether the target task grid exploration value corresponding to the target task grid exploration value information is greater than or equal to the target exploration threshold, and obtain an exploration judgment result; When the exploration judgment result is yes, determining that the task grid has a target is the target task grid exploration result information corresponding to the target task grid exploration value information; When the exploration judgment result is no, it is determined that the task grid does not exist, and the target task grid exploration result information corresponding to the target task grid exploration value information is determined.

6. A data processing device, characterized in that: The device comprises: An acquisition module is used to acquire exploration mission area information and drone group information; the exploration mission area information includes M exploration mission grid information; the drone group information includes N drone information; the exploration mission grid information includes a first exploration time exploration value, grid coordinate information, and grid type; the drone information includes drone exploration value information, drone coordinate information, and drone exploration radius; the drone exploration value information includes M drone mission grid exploration value information; each drone mission grid exploration value information corresponds to one exploration mission grid information; A first determination module is used to determine target drone exploration information based on the exploration mission area information and the drone group information; the target drone exploration information includes M target drone exploration value information; A second determination module is used to determine target area exploration result information based on the target UAV exploration information; the target area exploration result information includes the M target task grid exploration result information; Wherein, determining the target drone exploration information based on the exploration mission area information and the drone group information includes: For any exploration task grid information in the exploration task area information and for any drone information in the drone group information, based on the exploration task grid information and the drone information, determine the target task grid exploration value information corresponding to the drone information; Performing weighted summation processing on all the target task grid exploration value information to obtain the target UAV exploration value information corresponding to the exploration task grid information; Wherein, determining the target task grid exploration value information corresponding to the drone information based on the exploration task grid information and the drone information includes: Obtain exploration parameter information corresponding to the exploration value threshold and the exploration task grid information; the exploration value threshold represents that the exploration positioning accuracy of a certain task grid reaches the exploration peak requirement. When the exploration value reaches the threshold, the exploration value of the task grid is no longer increased to avoid attracting more drones to explore the adjacent area of ​​the task grid, resulting in a waste of drone exploration resources. Instead, the drone should be directed to explore other task grids to discover more target objects; The exploration value threshold, the exploration parameter information, the exploration task grid information and the drone information are calculated and processed to obtain the target task grid exploration value information corresponding to the drone information.

7. A data processing 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 data processing method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the data processing method according to any one of claims 1 to 5.

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