Synchronous data sensing method, device and equipment based on edge multipoint cooperation

Abstract

The invention provides a synchronous data sensing method, device and equipment based on edge multipoint cooperation, and the method comprises the steps: obtaining a first sensing parameter and a second sensing parameter when an unmanned plane and ground physical equipment carry out the data sensing of a sensing target; determining a synchronous data utility evaluation model during data perception according to the first perception parameter and the second perception parameter; according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, determining an optimal flight trajectory set of the unmanned aerial vehicle; updating the optimal flight path according to a preset diffusion model and a preset depth gradient model to obtain a target flight path; and synchronously sensing data information of the target according to the ground physical equipment and the unmanned aerial vehicle based on the target flight path and feeding back the data information to the edge server. According to the scheme of the invention, through the cooperative effect of the unmanned aerial vehicle and the ground physical equipment, the sensing target which changes in real time can be accurately sensed, so that the accuracy and coverage range of data sensing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer information processing, and particularly to a synchronous data sensing method, device and equipment based on edge multi-point cooperation. Background Art

[0002] Under the background of the rapid development of current digital twin technology, the importance of synchronous data sensing has become increasingly prominent. With the wide application of edge computing, the Internet of Things and unmanned aerial vehicle (UAV)-assisted communication, real-time, accurate and efficient sensing of environmental data has become the key to improving the intelligent level of the system. Only by ensuring the timeliness and consistency of data can a digital twin model be accurately constructed to achieve accurate mapping and dynamic optimization of the physical world. During the task execution process, if there are delays or inconsistencies in data sensing, it will not only affect the accuracy of computing offloading and resource scheduling, but may also lead to decision-making errors and reduce the overall system performance. Therefore, at present, constructing an accurate, efficient and low-latency synchronous data sensing mechanism has become a research topic that has received much attention.

[0003] However, the sensing schemes in existing synchronous data sensing technologies adopt the method of single base station or Internet of Things device sensing, which is difficult to meet the sensing requirements of the system for time-varying sensing targets and achieve true synchronization. In particular, ground sensing devices at fixed positions are greatly affected by line-of-sight blocking and environmental factors and cannot comprehensively sense the targets. Especially when facing time-varying sensing targets, the synchronous data sensing schemes in the existing technologies cannot effectively sense the sensing targets, cannot dynamically allocate resources well, and are extremely likely to lead to poor overall system performance. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a synchronous data sensing method, device and equipment based on edge multi-point cooperation, which can accurately sense time-varying sensing targets through the collaborative action of UAVs and ground physical devices, so as to improve the accuracy and coverage of data sensing.

[0005] To solve the above technical problem, an embodiment of the present invention provides a synchronous data sensing method based on edge multi-point cooperation, including: Obtaining a first sensing parameter when a UAV performs data sensing on a sensing target during flight and a second sensing parameter when a ground physical device performs data sensing on the sensing target; Determining a synchronous data utility evaluation model when the UAV and the ground physical device perform data sensing according to the first sensing parameter and the second sensing parameter; Determining an optimal flight trajectory set of the UAV according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, where the optimal flight trajectory set includes multiple different optimal flight trajectories; Update the optimal flight trajectories in the optimal flight trajectory set according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory; Synchronously sense the data information of the sensed target by the UAV according to the ground physical device and based on the target flight trajectory, and feed it back to the edge server.

[0006] In one embodiment, according to the first sensing parameter and the second sensing parameter, determining a synchronous data utility evaluation model for data sensing by the UAV and the ground physical device includes: Determine the index values for data sensing by the UAV and the ground physical device according to the first sensing parameter and the second sensing parameter; Determine the synchronous data utility evaluation model according to the index values.

[0007] In one embodiment, the synchronous data utility evaluation model is constructed by the following formula: ; where, represents the sensing completeness index value, represents the sensing accuracy index value, represents the sensing timeliness index value, represents the sensing energy efficiency index value, where, respectively represent the weight coefficients of the corresponding index values, and .

[0008] In one embodiment, according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, determining an optimal flight trajectory set of the UAV includes: Generate an initial flight trajectory set of the UAV according to a preset random generation algorithm; Update the initial flight trajectory set according to the preset initialization trajectory algorithm to obtain an updated flight trajectory set, and the updated flight trajectories in the updated flight trajectory set correspond one-to-one with the initial flight trajectories in the initial flight trajectory set; Determine the optimal flight trajectory set according to the synchronous data utility evaluation model, the initial flight trajectory set, and the updated flight trajectory set.

[0009] In one embodiment, updating the initial flight trajectory set according to the preset initialization trajectory algorithm to obtain an updated flight trajectory set includes: According to the formula: Perform an update process on each initial flight trajectory in the initial flight trajectory set to obtain a first flight trajectory corresponding one-to-one to the initial flight trajectory; where, Represents the initial flight coordinates; Represents the first flight coordinates; , respectively represent , the weight coefficients of, and are all random numbers between 0 and 1; is the weight coefficient of fel; Represents a normal distribution with a mean of 0 and a variance of 1; Represents the coordinate vector difference between the optimal initial flight coordinates in the initial flight trajectory set and the initial flight coordinates at the current moment; Represents the coordinate vector difference between a randomly selected coordinate from the first three optimal initial flight coordinates, the mean of all initial flight coordinates, and the median of the initial flight coordinates in the initial flight trajectory set and the initial flight coordinates at the current moment; Represents the coordinate vector difference between the UAV at a random position and the initial flight coordinates at the current moment; According to the formula: Perform a secondary update process on the first flight trajectory to obtain an updated flight trajectory set composed of updated flight trajectories corresponding one-to-one to the first flight trajectory; where, Represents the updated flight coordinates; Represents the spiral flight parameters of the UAV; Represents the Levy flight parameters of the UAV; M represents the adaptive parameter; Represents a random factor, randomly taking values between 0 and 1, and is used to select the position update formula.

[0010] In one embodiment, according to the synchronous data utility evaluation model, the initial flight trajectory set, and the updated flight trajectory set, determining the optimal flight trajectory set includes: According to the synchronous data utility evaluation model, determine the initial utility value corresponding to each initial flight trajectory in the initial flight trajectory set and the updated utility value corresponding to each updated flight trajectory in the updated flight trajectory set; According to the initial utility value and the updated utility value, perform a screening process on the initial flight trajectory set and the updated flight trajectory set to obtain the optimal flight trajectory set.

[0011] In one embodiment, update the optimal flight trajectory in the optimal flight trajectory set according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory, including: According to the preset diffusion model, perform forward diffusion noise addition and reverse diffusion noise reduction processes on the optimal flight trajectory in the optimal flight trajectory set in sequence to obtain the flight trajectory after diffusion processing; Determine a trajectory adjustment action for the flight trajectory after diffusion processing according to the preset depth gradient model; Adjust the flight trajectory after diffusion processing according to the trajectory adjustment action to obtain the target flight trajectory.

[0012] An embodiment of the present invention further provides a synchronous data perception device based on edge multi-point collaboration, which is characterized by including: An acquisition module, configured to acquire a first perception parameter when a drone performs data perception on a perception target during flight and a second perception parameter when a ground physical device performs data perception on the perception target; A processing module, configured to determine a synchronous data utility evaluation model for the drone and the ground physical device to perform data perception according to the first perception parameter and the second perception parameter; determine an optimal flight trajectory set of the drone according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, where the optimal flight trajectory set includes multiple different optimal flight trajectories; update the optimal flight trajectories in the optimal flight trajectory set according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory; synchronously sense data information of the perception target by the ground physical device and the drone based on the target flight trajectory and feedback it to the edge server.

[0013] An embodiment of the present invention further provides a computing device, including: A memory, configured to store one or more programs; One or more processors, configured to execute the one or more programs to implement the method as described above.

[0014] An embodiment of the present invention further provides a computer-readable storage medium, in which a program is stored, and when the program is executed by a processor, the method as described above is implemented.

[0015] The above solution of the present invention has at least the following beneficial effects: The method, apparatus, and device for synchronous data perception based on edge multi-point cooperation provided by the above solution of the present invention, wherein the method includes: obtaining a first perception parameter when the unmanned aerial vehicle (UAV) performs data perception on a perception target during flight and a second perception parameter when a ground physical device performs data perception on the perception target; determining a synchronous data utility evaluation model for the UAV and the ground physical device to perform data perception according to the first perception parameter and the second perception parameter; determining an optimal flight trajectory set of the UAV according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, where the optimal flight trajectory set includes multiple different optimal flight trajectories; updating the optimal flight trajectories in the optimal flight trajectory set according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory; synchronously perceiving data information of the perception target by the ground physical device and the UAV based on the target flight trajectory and feeding it back to the edge server. The solution provided by the above embodiment of the present invention can accurately perceive a perception target with real-time changes through the collaborative action of the UAV and the ground physical device, so as to improve the accuracy and coverage of data perception. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of the method for synchronous data perception based on edge multi-point cooperation provided by an embodiment of the present invention; Figure 2 is a schematic diagram of a perception system of a UAV-assisted edge network provided by an optional embodiment of the present invention; Figure 3 is a schematic block diagram of the modules of the device for synchronous data perception based on edge multi-point cooperation provided by an embodiment of the present invention; Figure 4 is a schematic block diagram of an electronic device provided by an embodiment of the present invention; Figure 5 is a schematic block diagram of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0018] In the following description, for the purpose of illustrating various disclosed embodiments, certain specific details are set forth to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the embodiments can be practiced without one or more of these specific details. In other instances, well-known devices, structures, and techniques associated with the present application may not be shown or described in detail so as not to unnecessarily obscure the description of the embodiments.

[0019] References to "one embodiment" or "an embodiment" throughout the specification mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily all referring to the same embodiment. Additionally, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0020] In the following description, for the purpose of clearly showing the structure and working mode of the present invention, many directional terms will be used for description. However, terms such as "front", "rear", "left", "right", "outer", "inner", "outward", "inward", "up", "down", etc. should be understood as convenient terms and not as limiting terms.

[0021] As Figure 1 shown, an embodiment of the present invention provides a synchronous data perception method based on edge multi-point collaboration, including: Step 11, obtaining a first perception parameter when the unmanned aerial vehicle (UAV) performs data perception on a perception target during flight and a second perception parameter when a ground physical device performs data perception on the perception target; Step 12, determining a synchronous data utility evaluation model for the UAV and the ground physical device to perform data perception according to the first perception parameter and the second perception parameter; Step 13, determining an optimal flight trajectory set of the UAV according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, where the optimal flight trajectory set includes multiple different optimal flight trajectories; Step 14, updating the optimal flight trajectories in the optimal flight trajectory set according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory; Step 15, synchronously perceiving the data information of the target according to the ground physical device and the UAV based on the target flight trajectory and feeding it back to the edge server.

[0022] In this embodiment, the mobility of the drone is utilized to quickly move to the target area, achieving effective perception of the target to be perceived in dead corners; through the collaborative perception of the drone and ground physical devices, data can be collected from different angles and heights, which can improve the accuracy and coverage of data perception. By the collaborative effect of the drone and ground physical devices, data perception is performed on the target to be perceived, effectively balancing the resources, latency, and energy consumption of the perception system, avoiding the problems that in the existing synchronous data perception solutions, the method of using a single base station or Internet of Things device for perception is difficult to meet the perception requirements of the system for the target to be perceived with real-time changes and cannot comprehensively perceive the target to be perceived. Here, considering indicators such as perception accuracy, perception completeness, perception timeliness, and perception energy efficiency during data perception to ensure data quality and network efficiency, the optimal flight trajectory set of the drone in a static environment can be determined through a preset initialization trajectory algorithm and a synchronous data utility evaluation model constructed based on the first perception parameter and the second perception parameter, making the flight trajectory of the drone as optimal as possible in terms of perception completeness, integrity, timeliness, and energy efficiency. Here, the first perception parameter may include the perception frequency of the drone for the target to be perceived at any moment, the first distance between the drone and the target to be perceived, the first perception factor (the influencing factor of the external environment on the perception quality), the maximum perception range of the drone (depending on the performance of the drone itself, when the target to be perceived exceeds the maximum perception range of the drone, the drone cannot perceive the target to be perceived at this time), and the first time required for the drone to perform a perception operation; the second perception parameter may include the second distance between the ground physical device and the target to be perceived, the second perception factor (a positive parameter for evaluating the perception detection quality according to environmental conditions), and the second time required for the ground physical device to perform a perception operation.

[0023] The optimal flight trajectory set contains multiple different ideal optimal flight trajectories; since the energy, perception resources, etc. of the drone are dynamically changing during flight, in this embodiment, the optimal flight trajectories in the optimal flight trajectory set are updated through a preset diffusion model and a preset depth gradient model to obtain a high-quality target flight trajectory that can meet real-time dynamic changes, while avoiding problems such as limitations brought by the existing technology usually using a single optimization strategy for trajectory update. Further, the drone flies according to the target flight trajectory and continuously perceives the target to be perceived in collaboration with the ground physical device during flight, and sends the synchronous perception data to the edge server, so that the edge server can process the collected data status and generate a digital twin of the target to be perceived, thereby reflecting the real-time status of the target to be perceived.

[0024] The synchronous data perception method provided in the above embodiment of the present invention can be applied to the perception system of a drone-assisted edge network; such as Figure 2As shown, the perception system may include a number of drones, a number of ground physical devices, an edge server, and a perception target. When the system works specifically, the ground physical devices and drones continuously perceive the perception target and send the synchronized perception data to the edge server; the edge server processes the collected data status and generates a digital twin of the perception target to reflect the real-time status of the perception target. In the perception system, is used to represent the index set of time.

[0025] In an optional embodiment of the present invention, the above step 12 may include: Step 121, determining the index values of the drones and ground physical devices when performing data perception according to the first perception parameter and the second perception parameter; Step 122, determining the synchronous data utility evaluation model according to the index values.

[0026] Here, the index values of the drones and ground physical devices when performing data perception may include average perception completeness, average perception accuracy, perception timeliness, and perception energy efficiency. Among them, perception completeness directly affects the completeness and reliability of the synchronous data in the perception process of the drone-assisted edge network, and determines whether the perception system can comprehensively capture environmental information; perception completeness refers to the perception mapping ratio of each element attribute of the perception target by the system, reflecting the overall coverage rate of the perception target by the system. Perception accuracy refers to the perception accuracy of the attributes of the perception target (such as position, shape, category, etc.) in the perception process of the perception system. Perception timeliness is used to characterize the effectiveness of the perception data. Since the state of the perception target changes with time, the effectiveness of the perception data will rapidly decline over time. Obsolete information not only cannot provide effective support for the perception system, but may even lead to incorrect decisions. Perception energy efficiency represents the energy utilization rate of the entire perception system in the process of perceiving the perception target (that is, the quantity and quality of perception completed under unit energy consumption).

[0027] Based on the average perception completeness, average perception accuracy, perception timeliness, and perception energy efficiency, comprehensively evaluate the quality of the drone perception data from four dimensions; at the same time, construct a synchronous data utility evaluation model based on the index values determined by the first perception parameter and the second perception parameter, and use the maximum value corresponding to the synchronous data utility evaluation model as the target value for subsequent drone flight trajectory update and adjustment to ensure the accuracy and effectiveness of the perception data when flying based on the finally updated and adjusted target flight trajectory.

[0028] In an alternative embodiment of the present invention, when determining the metric value based on the first sensing data and the second sensing data, first, a sensing model during sensing data construction is built using the first sensing data and the second sensing data. The sensing model includes a drone sensing model and a ground physical device sensing model. Among them, the drone sensing model is used to evaluate the sensing ability of the drone, and the ground physical device sensing model is used to evaluate the sensing ability of the ground physical device. Here, the sensing ability can be characterized by the sensing accuracy, sensing latency, sensing energy consumption, etc. of the drone or the ground physical device. Here, the specific process of building the sensing model is as follows: (1) Drone sensing model: Step 1211a, let represent the sensing frequency of the drone at time . Then, at time , the first sensing accuracy of the drone for the sensing target can be expressed as: ; Among them, represents the first sensing accuracy; represents the first sensing factor, and according to the changes in the environment, also changes accordingly; represents the maximum sensing range of the drone ; represents the first distance between the drone at time and the sensing target ; Step 1212a, let be the first time required for the drone to perform a sensing operation. Then, at time t, the first sensing latency of the drone can be expressed as: ; Step 1213a, according to the first time and the sensing frequency, the first sensing energy consumption of the drone at time t can be determined:

[0029] .

[0030] (2) Ground physical device sensing model: Step 1211b, let represent the size of the sensing data of the ground physical device at time t; use a probability model to evaluate the second sensing accuracy of the ground physical device. At time t, the ground physical device For the perceived target The successful perception probability Is expressed as: ;

[0031] Wherein, Represents the second perception factor; Represents the distance between the ground physical device And the perceived target At time t; Since the ground physical device can perform multiple perception operations simultaneously during a perception process (to increase the likelihood of successful perception), at time t, the ground physical device For the perceived target The successful perception probability after multiple perception operations is expressed as: ;

[0032] Wherein, Represents the number of perception times performed by the ground physical device For the perceived target At time t, with As the maximum number of perception times of a ground physical device at a moment, the constraint condition that the number of perception times should satisfy is: ;

[0033] Then, according to the successful perception probability after multiple perception operations, at Time, the second perception accuracy of the ground physical device For the perceived target Can be expressed as: ; Step 1212b, with Represents the second time required for the ground physical device To perform one perception operation, then at Time, the second perception delay Of the ground physical device Can be expressed as: ;

[0034] Step 1213b, according to the perception data size , at Time, the second perception energy consumption Of the ground physical device Is expressed as: ;

[0035] Wherein, Represents the energy consumption of the ground physical device For each bit of synchronization data.

[0036] Here, to ensure the quality of the perceived target, the probability of successful perception of the ground physical device should not be lower than a lower limit. Let represent the minimum probability of successful perception required by the ground physical device , then the perception probability should satisfy the following constraint conditions: ; where N represents the total number of ground physical devices in the perception system.

[0037] In an alternative embodiment of the present invention, after the UAV and the ground physical device perceive the perceived target, they need to transmit the synchronous perception data to the ground edge server respectively; here, a corresponding communication model needs to be constructed and data transmission is carried out through the communication model; the specific process of constructing the communication model is as follows: Step 1214a, construct an air-to-ground channel model between the UAV and the ground edge server: Since the UAV is flying in the air during the mission execution, the possibility of line-of-sight link communication is greatly increased during the communication with the ground edge server; at the same time, the existence of obstacles such as buildings also leads to the generation of non-line-of-sight links; Here, according to the air-to-ground probability model, the expected path loss for determining the communication between the UAV u and the ground edge server k can be expressed as: ;

[0038] where , respectively represent the line-of-sight link communication probability and the non-line-of-sight link communication probability between the UAV and the edge server at time ; represents the path loss under line-of-sight link communication between the UAV and the edge server at time ; represents the path loss under non-line-of-sight link communication between the UAV and the edge server at time ; the line-of-sight link probability can be expressed as:

[0039] where , both represent environmental parameters and change with the environment; represents the UAV at time The included angle with the edge server ; ; and can be respectively expressed as: ; ;

[0040] wherein, represents the carrier frequency of the air-to-ground channel; represents the time when the UAV and the edge server the distance between; is the speed of light; and respectively represent the average additional path loss of the line-of-sight link and the non-line-of-sight link.

[0041] At the time, the UAV uses the air-to-ground channel to transmit the sensed data to the edge server The first transmission rate can be expressed as: ;

[0042] wherein, represents the bandwidth of the communication channel, represents at the time when the UAV and the edge server the signal-to-noise ratio between, which can be specifically expressed as: ; wherein, represents Gaussian white noise with a mean of 0, is the edge server at the time when the received UAV the first transmission power, which can be expressed as: ; wherein, is the second transmission power of the UAV, represents the edge server antenna gain.

[0043] Therefore, the first transmission delay of the UAV at the time can be expressed as: ;

[0044] wherein, represents the data size sensed by the UAV for all potential sensing targets.

[0045] Step 1214b, constructing the ground-to-ground channel model: After a round of sensing of the sensing target is completed by the ground physical device, the ground physical device will transmit the data to the edge server to update the target status information, etc. Here, the transmission rate of the th ground physical device transmitting the sensing data to the edge server can be expressed as: Among them, represents the bandwidth of the transmission channel, represents the transmission power of the ground physical device at the moment, is the channel gain between the ground physical device and the edge server.

[0046] Furthermore, the second transmission delay of the ground physical device at the moment can be expressed as: ;

[0047] Among them, represents the size of the data sensed by the ground physical device .

[0048] When the UAV and the ground physical device transmit the sensed data to the edge server, the edge server needs to process the sensed data. The computing delay of the edge server at time t can be expressed as: ;

[0049] Among them, represents the size of the data transmitted by all ground physical devices and UAVs to the edge server at the moment, represents the computing power of the edge server. Since the edge server is on the ground and has an infinite energy supply, the energy consumption problem of the edge server is not considered.

[0050] In an optional embodiment of the present invention, based on the above constructed models, the index values when the UAV and the ground physical device perform data sensing can be specifically expressed as: At the moment, for the sensing target , the sensing completeness can be expressed as: ; Among them, represents the number of element attributes of the th sensing target sensed by the UAV or the ground sensing device at the moment; Indicates at time the total number of element attributes of the th sensed target. When is reached, it means that all the element attributes of the sensed target have been successfully sensed, and the sensing completeness is the highest; conversely, when is reached, it means that the sensing has completely failed; ;

[0051] At time, the accuracy of the th sensed target can be expressed as: ;

[0052] where and are the accuracy weights sensed by the UAV and the ground sensing device respectively, and satisfy the constraint: ; Indicates the sensing accuracy of the UAV at time for the th sensed target; Indicates the sensing accuracy of the ground sensing device at time for the th sensed target; can be expressed as: ; where indicates the sensing success rate of the ground sensing device for the th sensed target at time slot t; Furthermore, the average sensing accuracy of the entire sensing system can be expressed as: .

[0053] At time, the sensing timeliness of the entire sensing system can be expressed as: ; where indicates the size of the synchronized sensing data sensed by the entire system at time; Indicates the transmission and calculation delay, which can be specifically expressed by the following formula: .

[0054] At time, the sensing energy efficiency of the entire sensing system can be expressed as: ; where, represents the data size sensed by the UAV and the ground sensing device at moment; represents the total energy consumed by the entire synchronous data sensing, transmission and calculation at moment.

[0055] In an alternative embodiment of the present invention, based on the above various index values, a synchronous data utility evaluation model can be constructed , specifically can be expressed as: ;

[0056] where, represents the average sensing completeness, represents the average sensing accuracy, represents the sensing timeliness, represents the sensing energy efficiency, respectively represent the weight coefficients of the corresponding index values, and .

[0057] For all potential sensing targets, the optimization goal is to maximize the completeness and accuracy of the sensing targets and minimize the delay and energy consumption to the greatest extent, and improve the system timeliness and energy efficiency, specifically can be expressed as: .

[0058] Quantify the quality and value of the synchronous sensing data of the UAV-assisted edge network through the synchronous data utility evaluation model to better and more comprehensively sense the sensing targets; comprehensively consider the target completeness, target accuracy, target timeliness and target energy efficiency factors, optimize the system resource allocation, and improve the sensing efficiency and network synchronization performance.

[0059] In an alternative embodiment of the present invention, step 13 above may include: Step 131, generate an initial flight trajectory set of the UAV according to a preset random generation algorithm.

[0060] Here, the initial flight trajectory generated based on the preset random generation algorithm can be specifically expressed as: ; assuming that each UAV flies 100 steps (corresponding to 100 moments), and each moment corresponds to a flight coordinate, then an initial flight trajectory corresponds to 100 flight coordinates, that is, an initial flight trajectory can be obtained by connecting 100 flight coordinates in time sequence; Here, the initial flight coordinates of the UAV at each moment can be randomly generated through the following preset random generation algorithm: ;

[0061] Among them, respectively represent the initial coordinate values of the UAV on the X-axis, Y-axis, and Z-axis at time t, and can be specifically expressed as: ;

[0062] ;

[0063] ;

[0064] Among them, , , respectively represent the boundaries in the three directions of the X-axis, Y-axis, and Z-axis to prevent the generated initial solution from exceeding the boundaries; represents a random number between 0 and 1, which is used to randomize the initial coordinates; since represents a random number, and multiple different coordinate values may be generated at the corresponding time, so multiple different initial flight trajectories can be obtained.

[0065] In an optional embodiment of the present invention, step 13 further includes: Step 132, according to the preset initialization trajectory algorithm, perform an update process on the initial flight trajectory set to obtain an updated flight trajectory set, and the updated flight trajectories in the updated flight trajectory set correspond one-to-one with the initial flight trajectories in the initial flight trajectory set.

[0066] Here, based on the preset initialization trajectory algorithm, the initial flight trajectories in the initial flight trajectory set are updated. Specifically, the initial flight coordinates in each initial flight trajectory are updated to obtain the optimal flight coordinates, so as to ensure that the optimal flight trajectory can be obtained subsequently. Here, the update process of the preset initialization trajectory algorithm mainly includes two parts: the exploration stage and the exploitation stage. Among them, the exploration stage of the algorithm mainly reflects its powerful global search ability, fast convergence, and flexibility. Through extensive search, the exploration stage can effectively avoid local optima and ensure finding multiple potential good solutions (that is, the optimal flight coordinates), especially outstanding in dealing with complex, multi-objective optimization problems. The exploitation stage of the algorithm is mainly to further refine and optimize the solutions obtained in the exploration stage to improve the quality and accuracy of the solutions. In the exploitation stage, the algorithm converges to the global optimal solution through local search and fine-tuning, and usually uses heuristic methods or more strict constraint conditions to improve the performance of the solutions. Its advantage lies in being able to deeply optimize the key parts by concentrating resources, narrowing the search range, and improving the accuracy of the solutions. In addition, the exploitation stage can effectively avoid unnecessary calculations, reduce the search space, thereby accelerating the convergence process and improving the optimization efficiency.

[0067] In an alternative embodiment of the present invention, step 132 may include: Step 1321, in the exploration phase, according to the formula: Perform an update process on each initial flight trajectory in the initial flight trajectory set once to obtain a first flight trajectory corresponding one-to-one to the initial flight trajectory; where represents the initial flight trajectory; represents the first flight trajectory; , respectively represent , weight coefficients of , and are all random numbers between 0 and 1; represents the coordinate vector difference between the optimal initial flight coordinate in the initial flight trajectory set and the initial flight coordinate at the current moment; is the weight coefficient of represents a normal distribution with a mean of 0 and a variance of 1; represents the coordinate vector difference between the UAV at a random position and the initial flight coordinate at the current moment; here , and can be expressed as: is the weight coefficient of fel; represents a normal distribution with a mean of 0 and a variance of 1; fel represents the difference in distance between the UAV at a random position and the current position; here , and fel can be expressed as: ;

[0068] ;

[0069] ;

[0070] where represents a coordinate randomly selected from the first three optimal initial flight coordinates in the initial flight trajectory set, the mean of all initial flight coordinates, and the median of the initial flight coordinates, represents a random position randomly selected in the initial flight trajectory set, Represents the optimal initial flight coordinates in the set of initial flight trajectories, and the optimal initial flight coordinates can be obtained by calculating the value of each initial flight coordinate, and determining the coordinate corresponding to the maximum value as the optimal initial flight coordinates.

[0071] Step 1322, in the development stage, according to the formula: The first flight trajectory is processed for secondary update to obtain a set of updated flight trajectories composed of updated flight trajectories corresponding one by one to the first flight trajectory; where Represents the updated flight coordinates; Represents the spiral flight parameter of the UAV; Represents the Levy flight parameter of the UAV; M represents the adaptive parameter; Represents a random factor, randomly taking values between 0 and 1, and is used to select the position update formula.

[0072] Here, the spiral flight parameter of the UAV represents that in order to obtain the optimal solution faster, the UAV updates its position in a spiral flight mode, and the spiral flight parameter can be specifically expressed as: ;

[0073] Among them, L represents the spiral contraction factor, which determines the convergence speed during spiral flight, and Z represents the spiral angle parameter, which determines the updated position during spiral flight; and can be specifically expressed as: ;

[0074] ;

[0075] Among them, is the current iteration number, is the maximum iteration number. Represents the adaptive parameter and can be expressed as: ;

[0076] The Levy flight parameter of the UAV can be expressed as: ;

[0077] Among them, is the constant 1.5, u represents the random scale of the Levy flight step, that is, the basic size of the step, v represents the heavy-tailed characteristic of the Levy distribution, which controls the possibility of long-distance jumps of the step, and both obey specific normal distributions, can be specifically expressed as: ;

[0078] ;

[0079] Among them, represents a normal distribution with a mean of 0 and a variance of 1.

[0080] In an alternative embodiment of the present invention, the above step 13 further includes: Step 133, determining an optimal flight trajectory set according to the synchronous data utility evaluation model, the initial flight trajectory set, and the updated flight trajectory set.

[0081] Specifically, the above step 133 may include: Step 1331, determining the initial utility value corresponding to each initial flight trajectory in the initial flight trajectory set and the updated utility value corresponding to each updated flight trajectory in the updated flight trajectory set according to the synchronous data utility evaluation model; Step 1332, performing a screening process on the initial flight trajectory set and the updated flight trajectory set according to the initial utility value and the updated utility value to obtain an optimal flight trajectory set.

[0082] In this embodiment, for the initial flight trajectory before update and the updated flight trajectory after update, the initial utility value corresponding to the initial flight trajectory before update and the updated utility value corresponding to the updated flight trajectory after update are respectively determined through the synchronous data utility evaluation model; if the updated utility value corresponding to the updated flight trajectory after update is greater than the initial utility value corresponding to the initial flight trajectory before update, then the updated flight trajectory is determined as the optimal flight trajectory; otherwise, the initial flight trajectory is determined as the optimal flight trajectory; the above process can be expressed as: ;

[0083] Among them, represents the optimal flight trajectory; represents the updated utility value corresponding to the updated flight trajectory after update; represents the initial utility value corresponding to the initial flight trajectory before update.

[0084] In an alternative embodiment of the present invention, the above step 14 may include: Step 141, successively performing forward diffusion noise addition and reverse diffusion noise reduction processing on the optimal flight trajectories in the optimal flight trajectory set according to a preset diffusion model to obtain a flight trajectory after diffusion processing; Step 142, determining a trajectory adjustment action for the flight trajectory after diffusion processing according to a preset depth gradient model; Step 143, adjusting the flight trajectory after diffusion processing according to the trajectory adjustment action to obtain a target flight trajectory.

[0085] In this embodiment, the data corresponding to the optimal flight trajectory in the optimal flight trajectory set is modeled through a preset diffusion model, so as to generate a more optimized, smooth and constraint-compliant flight trajectory. The diffusion process perturbs the optimal flight trajectory with noise, and reconstructs and optimizes the flight trajectory through the reverse process, enabling the UAV to generate a more efficient and adaptable flight trajectory in future tasks. Its role is to enhance the generalization ability of flight trajectory planning, reduce the dependence on single optimization, and generate a better flight trajectory by combining historical experience; thereby improving the perception accuracy, reducing the energy consumption, and enhancing the adaptability of the UAV in a dynamic environment.

[0086] Here, the optimal flight trajectory is represented by the original trajectory data , and noise is gradually added to the original trajectory data , and finally it is transformed into a standard Gaussian distribution. For the given original trajectory data , the forward diffusion process can be expressed as: ;

[0087] where represents the trajectory data after noise addition in the -th round; The factor controlling the attenuation of the original trajectory data can be expressed as , is the preset noise increase parameter; represents the noise intensity; represents the identity matrix, indicating that the noise in each dimension is independently and identically distributed; represents a multi-dimensional normal distribution, indicating the state transition at each step in the diffusion process.

[0088] After rounds of noise addition, the noise version trajectory at any time can be expressed as: ;

[0089] where ;

[0090] After forward diffusion and noise addition, it is necessary to gradually denoise from the standard Gaussian noise trajectory data to restore the original trajectory data . The key to the reverse denoising process is to train a neural network to predict the denoised trajectory, which can be specifically expressed as:

[0091] where represents the predicted denoised mean, represents the predicted denoised variance, represents the neural network parameters (which can be obtained through training). Here, can be set as a fixed value, so the denoising network only needs to predict ; using Bayesian derivation, we can obtain: ;

[0092] wherein, represents the trajectory data corresponding to the original trajectory after denoising; represents the noise estimation value output by the neural network; represents the standard deviation of the random noise added during the reverse denoising process, which is used to ensure the randomness of denoising and make the sampling process diverse. This formula indicates that at each step of denoising, the noise is subtracted from the current standard Gaussian noise trajectory data to gradually recover the trajectory data corresponding to the original trajectory.

[0093] In an alternative embodiment of the present invention, in order to train the neural network , the mean square loss can be minimized for training, which can be specifically expressed as: ;

[0094] wherein, represents the noise added during the forward diffusion process.

[0095] The specific training steps are explained as follows: Step 1411, randomly sample the original trajectory data from the historical trajectories obtained by the intelligent optimization algorithm ;

[0096] Step 1412, randomly select a diffusion time step ;

[0097] Step 1413, generate noise , and calculate ;

[0098] Step 1414, use the neural network to predict the noise ;

[0099] Step 1415, calculate the loss and update the neural network parameters .

[0100] In an alternative embodiment of the present invention, based on the preset diffusion model, the optimal flight trajectories in the set of optimal flight trajectories are successively subjected to forward diffusion noise addition and reverse diffusion noise reduction processing, and can be adjusted by a preset deep gradient model to enable the drone to adapt to changes in a dynamic environment and optimize the synchronous data utility evaluation model. Preferably, the preset deep gradient model can be a Deep Deterministic Policy Gradient (DDPG) network model. The specific adjustment process is as follows: Step 1421: Sample the trajectory data corresponding to the original trajectory after denoising and convert it into a state sequence: ;

[0101] Step 1422: Initialize the DDPG network model; Step 14221: Initialize the Actor network ( ): Input the state , and output the action , which is used for drone trajectory adjustment.

[0102] Step 14222: Initialize the Critic network ( ): Input the state and the action , and output the Q value, which is used to evaluate the utility.

[0103] Step 14223: Initialize the experience replay buffer to store past trajectory data.

[0104] Step 14224: Initialize the target Actor and Critic networks for delayed update to stabilize the training.

[0105] Step 1423: Interaction and data collection: First, select the initial state from the trajectories generated by the diffusion model, and then the Actor network generates a trajectory adjustment action, which can be specifically expressed as: ;

[0106] where is the exploration noise to ensure the diversity of the policy. Execute the action , and update the drone trajectory, which can be expressed as: .

[0107] Step 1424: Calculate the reward (which is also the synchronous data utility evaluation model ), and the calculation method can be expressed as: ;

[0108] Step 1425, store the data into the experience pool .

[0109] Step 1426, update the Actor network and the Critic network, and sample from the experience replay pool to obtain a batch of data .

[0110] Step 1427, calculate the target Q value, which can be expressed as:

[0111] where is the discount factor, and are the target networks for delayed update.

[0112] Step 1428, update the Critic network: update the Critic network through training with mean squared error loss, which can be specifically expressed as: ;

[0113] Update the Actor network by maximizing the Q value of the trajectory adjustment strategy through policy gradient training, which can be specifically expressed as: ;

[0114] Update the target network, which can be specifically expressed as: ;

[0115] where takes a value of 0.005, and after the DDPG training converges, the final optimized target flight trajectory is obtained.

[0116] The synchronous data perception method based on edge multi-point cooperation provided by the above embodiments of the present invention addresses the perception requirements of drones in an edge computing environment. By obtaining the first perception parameters when the drone and ground physical devices perform data perception on the perception target, and the second perception parameters; according to the first perception parameters and the second perception parameters, determining a synchronous data utility evaluation model for data perception; according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, determining an optimal flight trajectory set of the drone; updating the optimal flight trajectory according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory; synchronously perceiving the data information of the target by the ground physical device and the drone based on the target flight trajectory and feeding it back to the edge server; based on the optimization strategy of the synchronous data utility evaluation model, comprehensively evaluating the quality of the drone's perceived data from four dimensions: perception accuracy, perception completeness, perception timeliness, and perception energy efficiency. Compared with the prior art that only focuses on the optimization goals of the shortest path or the lowest energy consumption, the solution of the present invention takes into account both data quality and computing resource utilization, enabling the drone to dynamically adjust its trajectory to maximize data synchronization utility when performing perception tasks, and improving the availability and decision-making value of data in the edge computing environment. Through a multi-stage optimization strategy, accurate perception of real-time changing perception targets can be achieved, improving the accuracy and coverage of data perception; in addition, this solution can achieve efficient and low-energy drone trajectory planning in a complex dynamic environment, providing better technical support for the application of drones in scenarios such as disaster monitoring, smart cities, and industrial inspection.

[0117] As Figure 3 shown, an embodiment of the present invention also provides a synchronous data perception 30 based on edge multi-point cooperation, including: An acquisition module 31, configured to acquire the first perception parameters when the drone performs data perception on the perception target during flight and the second perception parameters when the ground physical device performs data perception on the perception target; A processing module 32, configured to determine a synchronous data utility evaluation model for the drone and the ground physical device to perform data perception according to the first perception parameters and the second perception parameters; determine an optimal flight trajectory set of the drone according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, where the optimal flight trajectory set includes multiple different optimal flight trajectories; update the optimal flight trajectories in the optimal flight trajectory set according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory; synchronously perceive the data information of the target by the ground physical device and the drone based on the target flight trajectory and feed it back to the edge server.

[0118] It should be noted that this device corresponds to the method for synchronously perceiving data based on edge multi-point collaboration of the above data. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0119] As Figure 4 shown in the figure, an embodiment of the present invention further provides an electronic device 50, including: a memory 51 for storing one or more computer programs; one or more processors 52 for executing one or more computer programs. When the computer program is run by the processor, it executes the method 10 for synchronously perceiving data based on edge multi-point collaboration as described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects. The electronic device 50 is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed in the present invention.

[0120] As Figure 5 shown in the figure, the electronic device 50 is shown as a computing device, or a computer system, which may include a CPU 501 (computing unit), which can execute various appropriate actions and processes according to the computer program stored in the ROM 502 (read-only memory) or the computer program loaded from the storage unit 508 into the random access RAM 503 (memory). In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. The I / O interface 505 (input / output interface) is also connected to the bus 504.

[0121] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disc, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0122] The CPU 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the CPU 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The CPU 501 executes the various methods and processes described above. For example, in some embodiments, the edge multi-point collaboration-based synchronous data perception method 10 can be implemented as a computer software program tangibly embodied in a computer-readable storage medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the CPU 501, one or more steps of the edge multi-point collaboration-based synchronous data perception method 10 described above can be executed. Alternatively, in other embodiments, the CPU 501 can be configured to execute the edge multi-point collaboration-based synchronous data perception method 10 in any other suitable manner (e.g., by means of firmware).

[0123] Embodiments of the present invention also provide a computer-readable storage medium that stores instructions which, when run on a computer, cause the computer to execute the edge multi-point collaboration-based synchronous data perception method 10 described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0124] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0125] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0126] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0127] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0129] 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0130] In addition, it should be noted that in the device and method of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above series of processes can naturally be executed in chronological order according to the described order, but it is not necessary to be executed in chronological order. Some steps can be executed in parallel or independently of each other. For those of ordinary skill in the art, it is understandable that all or any steps or components of the method and device of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in the form of hardware, firmware, software, or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0131] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a well-known general-purpose device. Thus, the object of the present invention can also be achieved only by providing a program product containing program code for implementing the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be noted that in the device and method of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above series of processes can naturally be executed in chronological order according to the described order, but it is not necessary to be executed in chronological order. Some steps can be executed in parallel or independently of each other.

[0132] The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle described in the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A synchronous data perception method based on edge multi-point collaboration, characterized in that: include: Acquire a first perception parameter when the UAV perceives data of a perception target during flight and a second perception parameter when the ground physical device perceives data of the perception target; Determine, according to the first perception parameter and the second perception parameter, a synchronous data utility evaluation model when the UAV and the ground physical equipment perform data perception; Determine an optimal flight trajectory set of the UAV according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, wherein the optimal flight trajectory set includes a plurality of different optimal flight trajectories; Updating the optimal flight trajectory in the optimal flight trajectory set according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory; The data information of the sensed target is synchronously sensed according to the ground physical equipment and the drone based on the target flight trajectory and fed back to the edge server.

2. The synchronous data perception method based on edge multi-point collaboration according to claim 1 is characterized in that: Determining a synchronous data utility evaluation model when the UAV and the ground physical device perform data perception according to the first perception parameter and the second perception parameter includes: Determine, according to the first perception parameter and the second perception parameter, an index value when the drone and the ground physical equipment perform data perception; The synchronization data utility evaluation model is determined according to the indicator value.

3. The synchronous data perception method based on edge multi-point collaboration according to claim 2 is characterized in that: The synchronization data utility evaluation model is constructed by the following formula: ;in, represents the perceived completeness index value, represents the perceived accuracy index value, represents the perceived timeliness index value, represents the perceived energy efficiency index value, where Respectively represent the weight coefficients of the corresponding index values, and .

4. The synchronous data perception method based on edge multi-point collaboration according to claim 1 is characterized in that: According to the preset initialization trajectory algorithm and the synchronous data utility evaluation model, the optimal flight trajectory set of the UAV is determined, including: Generate an initial flight trajectory set of the UAV according to a preset random generation algorithm; According to the preset initialization trajectory algorithm, the initial flight trajectory set is updated to obtain an updated flight trajectory set, wherein the updated flight trajectories in the updated flight trajectory set correspond one-to-one to the initial flight trajectories in the initial flight trajectory set; The optimal flight trajectory set is determined according to the synchronous data utility evaluation model, the initial flight trajectory set and the updated flight trajectory set.

5. The synchronous data perception method based on edge multi-point collaboration according to claim 4 is characterized in that: According to the preset initialization trajectory algorithm, the initial flight trajectory set is updated to obtain an updated flight trajectory set, including: According to the formula: Performing an update process on each initial flight trajectory in the initial flight trajectory set to obtain a first flight trajectory corresponding to the initial flight trajectory one by one; wherein, represents the initial flight coordinates; represents the first flight coordinate; , Respectively , The weight coefficients are all random numbers between 0 and 1; is the weight coefficient of fel; represents a normal distribution with a mean of 0 and a variance of 1; Represents the coordinate vector difference between the optimal initial flight coordinates in the initial flight trajectory set and the initial flight coordinates at the current moment; represents the coordinate vector difference between a coordinate randomly selected from the first three optimal initial flight coordinates in the initial flight trajectory set, the mean of all initial flight coordinates, and the median of the initial flight coordinates and the initial flight coordinate at the current moment; Represents the coordinate vector difference between the drone's initial flight coordinates at a random position and the current moment; According to the formula: The first flight trajectory is subjected to a secondary updating process to obtain an updated flight trajectory set consisting of updated flight trajectories corresponding one to one with the first flight trajectory; wherein, Indicates updating flight coordinates; Indicates the spiral flight parameters of the drone; represents the Levy flight parameters of the UAV; M represents the adaptive parameters; Represents a random factor, which takes a random value between 0 and 1 and is used to select the position update formula.

6. The synchronous data perception method based on edge multi-point collaboration according to claim 4 is characterized in that: Determining the optimal flight trajectory set according to the synchronization data utility evaluation model, the initial flight trajectory set, and the updated flight trajectory set includes: Determining, according to the synchronous data utility evaluation model, an initial utility value corresponding to each initial flight trajectory in the initial flight trajectory set and an updated utility value corresponding to each updated flight trajectory in the updated flight trajectory set; The initial flight trajectory set and the updated flight trajectory set are screened according to the initial utility value and the updated utility value to obtain the optimal flight trajectory set.

7. The synchronous data perception method based on edge multi-point collaboration according to claim 5 is characterized in that: The optimal flight trajectory in the optimal flight trajectory set is updated according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory, including: According to the preset diffusion model, the optimal flight trajectory in the optimal flight trajectory set is subjected to forward diffusion noise enhancement and reverse diffusion noise reduction processing in sequence to obtain a flight trajectory after diffusion processing; Determining a trajectory adjustment action of the flight trajectory after the diffusion process according to the preset depth gradient model; The flight trajectory after the diffusion process is adjusted according to the trajectory adjustment action to obtain the target flight trajectory.

8. A synchronous data sensing device based on edge multi-point collaboration, characterized in that: include: An acquisition module, used to acquire a first perception parameter when the UAV performs data perception on a perception target during flight and a second perception parameter when the ground physical device performs data perception on the perception target; A processing module is used to determine a synchronous data utility evaluation model when a UAV and a ground physical device perform data perception according to the first perception parameter and the second perception parameter; determine an optimal flight trajectory set of the UAV according to a preset initialization trajectory algorithm and the synchronous data utility evaluation model, wherein the optimal flight trajectory set includes multiple different optimal flight trajectories; update the optimal flight trajectory in the optimal flight trajectory set according to a preset diffusion model and a preset depth gradient model to obtain a target flight trajectory; and synchronously perceive the data information of the perceived target according to the ground physical device and the UAV based on the target flight trajectory and feed it back to the edge server.

9. A computing device, characterized in that include: A memory for storing one or more programs; One or more processors, configured to execute the one or more programs to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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