Multi-target intelligent planning method and product based on space-time cognitive mechanism

Abstract

The invention provides a multi-target intelligent planning method and product based on a space-time cognitive mechanism, and relates to the technical field of intelligent planning. According to the method, the space-time weight is calculated by considering the time weight and the space weight, so that the time information (the time window of the task point) and the space information (the space position of the task point) are dynamically integrated, and the comprehensiveness of task planning is improved. By adopting the technical scheme provided by the embodiment of the invention, the unmanned aerial vehicle can be planned to efficiently access a plurality of task points within a given time in complex terrains (such as mountainous regions, cities or forests), energy consumption, path lengths and task priorities are considered at the same time, and comprehensive coverage and dynamic adjustment of tasks are realized. By adopting the technical scheme provided by the embodiment of the invention, the speed and success rate of target tracking can be remarkably improved under the conditions of limited resources and complex environment, and an efficient and accurate technical solution is provided for intelligent target tracking of the unmanned aerial vehicle in a high-speed motion scene.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent planning, and particularly to a multi-objective intelligent planning method and product based on a spatio-temporal cognitive mechanism. Background Art

[0002] In complex terrains (such as mountains, cities or forests), an unmanned aerial vehicle (UAV) needs to efficiently visit multiple task points within a given time, while taking into account energy consumption, path length and task priority to achieve comprehensive coverage and dynamic adjustment of tasks. However, due to the optimization complexity of time window constraints, the time windows of multiple objectives may overlap or conflict. Moreover, task points and obstacles in a complex environment may change over time, and the UAV has limited computing resources, making it difficult to complete task planning and execution under the constraints of limited computing power and energy consumption. Summary of the Invention

[0003] The present invention provides a multi-objective intelligent planning method and product based on a spatio-temporal cognitive mechanism to at least partially solve the above problems.

[0004] In a first aspect of the present invention, a multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism is provided, and the method includes: Modeling the characteristics of the time-dependent functions of the multiple task points changing with time according to the time windows of the multiple task points respectively, to obtain the time characteristic functions of the multiple task points respectively; Determining the time weights of the multiple task points respectively according to the time windows of the multiple task points and the time characteristic functions of the multiple task points respectively; Modeling the attraction relationship between a task point and other task points according to the spatial positions of the multiple task points, to determine the distribution intensity function of the task point, and the attraction relationship between task points is used to characterize the degree of being on the same route between task points; For each task point among the multiple task points, obtaining the spatial weight of the task point according to the distribution intensity function of the task point; Obtaining the spatio-temporal weight of the task point according to the time weights and spatial weights of the multiple task points respectively; Planning the execution order of the multiple task points with the highest task completion efficiency as the goal according to the spatio-temporal weights of the multiple task points respectively.

[0005] Optionally, modeling the characteristics of the time-dependent functions of the multiple task points changing with time according to the time windows of the multiple task points respectively, to obtain the time characteristic functions of the multiple task points respectively, includes: Determining the time-dependent functions of the multiple task points respectively according to the time windows of the multiple task points, and the time-dependent function of a task point is used to characterize whether it is within the time window of the task at any moment; Using the fractional differential equation and the time-dependent function of each of the multiple task points, model the characteristics of the time-dependent function of each of the multiple task points changing over time, and obtain the time characteristic function of each of the multiple task points.

[0006] Optionally, for each task point among the multiple task points, obtain the spatial weight of the task point according to the distribution intensity function of the task point, including: For each task point among the multiple task points, perform feature decomposition of the distribution intensity function of the task point at multiple scales to obtain the weight corresponding to each scale and the eigenbasis function of the task point at each scale; For each task point among the multiple task points, obtain the spatial weight of the task point according to the eigenbasis function of the task point at each scale and the distance between the task point and each task point at that scale.

[0007] Optionally, the method further includes: For any one of the multiple task points, after detecting that the spatial position of the task point is updated, determine the updated spatial weights and updated spatio-temporal weights of each of the multiple task points according to the updated spatial position of the task point; According to the updated spatio-temporal weights of each of the multiple task points, re-plan the execution order of the multiple task points with the goal of maximizing the task completion efficiency.

[0008] Optionally, the method further includes: After detecting a new task point, determine the spatio-temporal weight of the new task point, and determine the updated spatial weights and updated spatio-temporal weights of each of the multiple task points according to the spatial position of the new task point; According to the updated spatio-temporal weights of each of the multiple task points and the spatio-temporal weight of the new task point, re-plan the execution order of the multiple task points with the goal of maximizing the task completion efficiency.

[0009] Optionally, the method further includes: After detecting that any one of the multiple task points is deleted, determine the updated spatial weights and updated spatio-temporal weights of the remaining task points; According to the updated spatio-temporal weights of the remaining task points, re-plan the execution order of the remaining task points with the goal of maximizing the task completion efficiency.

[0010] Optionally, the method further includes: For any one of the multiple task points, after detecting that the time window of the task point is updated, according to the updated time window of the task point, determine the updated time weight and the updated spatio-temporal weight of the task point; According to the updated spatio-temporal weight of the task point and the spatio-temporal weights of other task points respectively, with the goal of maximizing the task completion efficiency, re-plan the execution order of the multiple task points.

[0011] Optionally, after planning the execution order of the multiple task points, the method further includes: According to the execution order of the multiple task points, determine every two adjacent task points in the execution order; According to multiple paths between every two adjacent task points in the execution order, plan the optimal path for traversing the multiple task points.

[0012] The second aspect of the present invention provides a multi-objective intelligent planning device based on a spatio-temporal cognitive mechanism, and the multi-objective intelligent planning device based on a spatio-temporal cognitive mechanism includes: A time modeling module, configured to model the characteristics of the time-dependent functions of the multiple task points changing with time according to the time windows of the multiple task points respectively, and obtain the time characteristic functions of the multiple task points respectively; A time weight determination module, configured to determine the time weights of the multiple task points respectively according to the time windows of the multiple task points and the time characteristic functions of the multiple task points respectively; A space modeling module, configured to model the attraction relationship between the task point and other task points according to the spatial positions of the multiple task points, and determine the distribution intensity function of the task point, where the attraction relationship between task points is used to characterize the degree of being on the way between task points; A space weight determination module, configured to obtain the space weight of each task point among the multiple task points according to the distribution intensity function of the task point; The spatio-temporal weight determination module obtains the spatio-temporal weight of the task point according to the time weights and space weights of the multiple task points respectively; A planning module, configured to plan the execution order of the multiple task points with the goal of maximizing the task completion efficiency according to the spatio-temporal weights of the multiple task points respectively.

[0013] The third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes, it implements the multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism as described in the first aspect of the present invention.

[0014] The fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the multi-objective intelligent planning method based on the spatio-temporal cognitive mechanism as described in the first aspect of the present invention.

[0015] The fifth aspect of the present invention provides a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, they implement the steps in the multi-objective intelligent planning method based on the spatio-temporal cognitive mechanism as described in the first aspect of the present invention.

[0016] The technical solution provided by the embodiments of the present invention can solve the problem of efficient task planning and execution of intelligent devices (such as drones) in complex dynamic environments, and is particularly suitable for multi-objective task allocation and path optimization with time window constraints. By adopting the technical solution provided by the embodiments of the present invention, it is possible to plan for a drone to efficiently visit multiple task points within a given time in complex terrains (such as mountains, cities or forests), while taking into account energy consumption, path length and task priority, so as to achieve comprehensive coverage and dynamic adjustment of tasks.

[0017] By adopting the technical solution provided by the embodiments of the present invention, it is possible to significantly improve the speed and success rate of target tracking under the conditions of resource limitation and complex environment, and provide an efficient and accurate technical solution for intelligent target tracking of drones in high-speed motion scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the present invention, the drawings required for the description of the present invention will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 is a flowchart of the steps of the multi-objective intelligent planning method based on the spatio-temporal cognitive mechanism provided by the present invention; Figure 2 is a schematic diagram of performing feature decomposition of the distribution intensity function of task points at multiple scales in the multi-objective intelligent planning method based on the spatio-temporal cognitive mechanism provided by the present invention; Figure 3 is a flowchart of the steps of the multi-objective intelligent planning method based on the spatio-temporal cognitive mechanism provided by the present invention; Figure 4 is a schematic flowchart of the multi-objective intelligent planning method based on the spatio-temporal cognitive mechanism provided by the present invention; Figure 5 is a structural block diagram of the multi-objective intelligent planning device based on the spatio-temporal cognitive mechanism provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] In the related art, multi-objective path planning based on dynamic programming has been proposed. This method obtains the optimal path for multi-objective access through the decomposition of tasks and the recursive calculation of path states. However, as the number of task points increases, the dimension of the state space will increase exponentially, resulting in a significant increase in computational complexity. At the same time, this method cannot effectively handle the dynamically changing time window constraints and lacks flexibility and real-time performance.

[0022] The intelligent planning methods proposed in the related art often model the time and space characteristics independently and lack the joint analysis of spatio-temporal information. For example, although the dynamic programming method can optimize the path, it is difficult to handle the time window constraints, resulting in insufficient accuracy of the planning results in complex dynamic scenarios. The change of the time window will trigger frequent recalculations, further increasing the computational overhead of the system.

[0023] Reinforcement learning and dynamic programming methods have high computational requirements in high-dimensional task scenarios. Especially when the number of task points is large or the path length is long, the calculation time will increase significantly. For an embedded UAV platform with limited resources, this computational bottleneck will lead to planning delays, thus reducing the real-time performance and reliability of the overall task.

[0024] In complex dynamic scenarios (such as changes in task point positions or dynamic adjustments of time windows), the related art lacks the ability to quickly update the spatio-temporal model, resulting in frequent recalculations for task planning. In this case, the UAV may miss the time window or select a suboptimal path, further reducing the task completion rate and efficiency.

[0025] The related art also proposes using reinforcement learning for multi-objective task scheduling. Through deep reinforcement learning, the UAV can learn the optimal task scheduling strategy in a dynamic environment, such as through the Proximal Policy Optimization (PPO) or Deep Q-Network (DQN) methods. Although the reinforcement learning method has good adaptability, when facing time window constraints and complex dynamic scenarios, the convergence speed is slow and a large amount of computational resources are required, making it difficult to meet the real-time requirements of embedded devices.

[0026] Based on this, the embodiments of the present invention provide a solution that can achieve efficient task planning under complex dynamic scenarios and time constraint conditions, and specifically propose a multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism. The embodiments of the present invention build a dynamic spatio-temporal cognitive model, and use fractional differential equations (FDE) and generalized Poisson equations to model and optimize the time characteristics and space characteristics of task points, so as to improve the efficiency and accuracy of task scheduling and path planning.

[0027] As Figure 1 shown, it shows a flowchart of the steps of a multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism provided by the embodiments of the present invention. Specifically, the method includes the following steps: S101, according to the time windows of multiple task points respectively, model the characteristics of the time-dependent functions of multiple task points changing with time, and obtain the time characteristic functions of multiple task points respectively.

[0028] In the embodiments of the present invention, the time window refers to the time interval during which each task point is allowed to be executed (for example, the task point is only valid within , where represents the starting point of the time interval during which the task point is allowed to be executed, and represents the ending point of the time interval during which the task point is allowed to be executed).

[0029] The time-dependent function is used to describe the function of the effectiveness of the task point changing with time. In the embodiments of the present invention, if the time t is not within the time window of the task point, the effectiveness of the task point is invalid and can be expressed as 0; if the time t is within the time window of the task point, the effectiveness of the task point is valid and can be expressed as 1.

[0030] The time characteristic function is a mathematical expression that is finally obtained through modeling and can quantify the time dependence of the effectiveness of the task point, and describes the non-linear dynamic characteristics of the task effectiveness changing with time. The time characteristic function characterizes the influence of the time window constraint of the task point on the priority of the task point (for example: the less remaining time of the task point, the more urgent the task, and the higher the task priority).

[0031] Specifically, the step S101 includes the following sub-steps: S1011, according to the time windows of multiple task points respectively, determine the time-dependent functions of multiple task points respectively. The time-dependent function of a task point is used to characterize whether any moment is within the time window of the task.

[0032] Specifically, the task point Time window Expressed as a time-dependent function :

[0033] Wherein: Represents a step function used to define the start and end ranges of the time window; Indicates whether the task point is within the valid time window at time t.

[0034] In the embodiments of the present invention, time window parsing converts time window constraints into mathematical expressions, providing a basis for subsequent time modeling.

[0035] S1012. Using the fractional differential equation and the time-dependent function of each of the multiple task points, model the characteristics of the time-dependent function of each of the multiple task points changing with time, and obtain the time characteristic function of each of the multiple task points.

[0036] In the embodiments of the present invention, the fractional differential equation is used to capture the time-varying characteristics of the task, and the memory and historical dependence on the time characteristics are controlled by the order of the fractional derivative.

[0037] In the embodiments of the present invention, the fractional differential model introduces historical memory and can better describe the dynamic changes of the task time window. Specifically, by combining the time-dependent function with the fractional differential equation, the time evolution characteristics of task effectiveness can be more finely characterized, especially the memory effect and the gradual change process during the transition inside and outside the time window.

[0038] S102. Determine the time weights of each of the multiple task points according to the time window of each of the multiple task points and the time characteristic function of each of the multiple task points.

[0039] In the embodiments of the present invention, the time characteristic functions of each of the multiple task points can be integrated according to the time window of each of the multiple task points to obtain the time weights of each task point. The time weights of each task point can be used for the optimization of subsequent task scheduling.

[0040] Taking "complex terrain" as an example of an urban scene with multiple building obstacles, assuming that the intelligent device currently performing the task is a drone, the task is package delivery, and the task points include task point A (customer A) and task point B (customer B). The package for customer A needs to be delivered between 10:00 and 11:00, and the package for customer B needs to be delivered between 10:30 and 12:00. Then, when the current time is 10:30, the time weight of task point A is higher than that of task point B.

[0041] In the embodiment of the present invention, time perception modeling is completed based on steps S101 to S102. Through time perception modeling, the distribution law of the time window of the task points is analyzed, the dependency relationship and dynamic characteristics of the time windows of each task point are captured, and task priorities are generated to ensure that the tasks are efficiently completed within the time window constraints of this task point.

[0042] S103. According to the spatial positions of the multiple task points, model the attraction relationship between this task point and other task points, and determine the distribution intensity function of this task point. The attraction relationship between task points is used to characterize the degree of being on the way between task points.

[0043] In the embodiment of the present invention, the spatial distribution position of the task points can be represented as a Poisson field with boundary conditions, and the attraction relationship between task points is described through the potential field of the task points. Based on the potential field of the task points, the density function of the task points can be determined to reflect the distribution intensity of the task points. In the embodiment of the present invention, the density function of the task points is used as the distribution intensity function of the task points.

[0044] In the embodiment of the present invention, the spatial positions of each task point can be represented by position vectors.

[0045] In the embodiment of the present invention, the boundary conditions can be determined according to the allowable flight boundary of the unmanned aerial vehicle.

[0046] In the embodiment of the present invention, after constructing the potential field model, the attraction relationship between task points can be modeled to provide support for path optimization.

[0047] In the embodiment of the present invention, the attraction relationship between task points is determined by the spatial position distribution of each task point, and characterizes the degree of being on the way between task points.

[0048] In the embodiment of the present invention, a potential field can be generated for each task point, and its intensity decays with distance, reflecting the range and intensity of its attraction. The superposition of the potential fields of all task points can form a total potential field. Based on the total potential field, the distribution intensity function of each task point can be obtained.

[0049] By modeling the attraction relationship between task points through the potential field, transforming the physical potential field idea into a mathematical tool, dynamic and adaptive task scheduling and path planning can be realized.

[0050] Modeling the spatial distribution of task points as a Poisson field with boundary conditions, the spatial constraints and the attraction relationship between task points can be encoded into the density function through partial differential equations.

[0051] S104. For each task point among the multiple task points, according to the distribution intensity function of this task point, obtain the spatial weight of this task point.

[0052] In the embodiments of the present invention, the distribution intensity function of each task point can be subjected to feature decomposition at multiple scales to obtain the feature basis functions of the task points that respectively consider different local features. Then, according to the feature basis functions at different scales and the distances between the task points at this scale, the spatial weight of this task point is obtained.

[0053] Specifically, the step S104 includes the following sub-steps: S1041, for each task point among the multiple task points, perform feature decomposition on the distribution intensity function of this task point at multiple scales to obtain the weight corresponding to each scale and the feature basis function of this task point at each scale.

[0054] In the embodiments of the present invention, the distribution intensity function of each task point takes into account the current global distribution features, and this global distribution feature not only considers the distances between task points but also considers influencing factors such as obstacles.

[0055] In the embodiments of the present invention, the Poisson field can be subjected to feature decomposition to extract the multi-scale spatial characteristics of the task points. Specifically, the distribution intensity function of the task points can be decomposed to obtain the feature intensity and feature basis functions of the task points at different scales, where the feature intensity represents the weight corresponding to each scale.

[0056] Each scale corresponds to a different spatial resolution. For example, a large scale may capture the overall distribution trend, while a small scale focuses on local details.

[0057] Specifically, the localization characteristics of wavelet basis functions can be utilized to decompose the distribution intensity function into components at different scales. By performing multi-scale wavelet decomposition on the distribution intensity function of the task points, the distribution features at different spatial resolutions can be quantified. The scale weights indicate the importance of each level, and the feature basis functions reveal the local structure, providing a data-driven decision basis for subsequent intelligent planning.

[0058] Specifically, such as Figure 2As shown, the ellipses of different sizes represent different scales. It can be seen that the features at different scales can capture different spatial feature information. Taking task point A as an example, it can be seen that the spatial feature information at the smallest scale (the first scale) can focus on the local details related to the spatial information of task point A itself. For example, the obstacle information around task point A. The spatial feature information at a scale larger than the smallest scale (the second scale) can focus on the mutual information of the spatial distribution between task point A and task point B. For example, the path information from task point A to task point B. The spatial feature information at an even larger scale (the third scale) can focus on the mutual relationship of the spatial distribution among task point A, task point B, and task point C. Assuming there are obstacles between A and B that seriously affect the traveling speed, the priority order for completing the task points can be determined as A→C→B. The spatial feature information at an even larger scale (the fourth scale) can focus on the mutual relationship of the spatial distribution among task point A, task point B, task point C, and task point D. Assuming the road between D and B is unobstructed and there are no obstacles, and the drone can maintain a relatively high traveling speed, then the priority order for completing the task points can be determined as A→C→D→B.

[0059] Thus, in the embodiment of the present invention, by decomposing the distribution intensity function of the task points, the characteristic basis functions of the task points at different scales can be obtained, and the mutual relationship of the spatial distribution between the task points can be considered from different scales.

[0060] S1042, for each of the multiple task points, according to the characteristic basis function of the task point at each scale and the distance between the task point and each task point at that scale, obtain the spatial weight of the task point.

[0061] In the embodiment of the present invention, extracting the multi-scale features of the spatial distribution of the task points can distinguish the local and global target distribution relationships and improve the robustness of the intelligent planning method.

[0062] In the embodiment of the present invention, the multi-scale features of each task point and the distances between the task point and each task point at the corresponding scale are summed to obtain the spatial weight of the task point.

[0063] In the embodiment of the present invention, the multi-scale feature extraction can take into account local features. For example, there are many obstacles between task point A and task point B, which will affect the flight speed. The straight-line distance between the two points is 1. After local feature extraction of the distance and considering the obstacle features, the distance can be determined as 2; task point A and task point C are originally far away, with a straight-line distance of 2, but the field of vision in the middle is open and the obstacles are relatively low, and the flight speed is relatively fast. After local feature extraction and considering the obstacle features, the distance can be determined as 1.

[0064] In the embodiment of the present invention, based on steps S103 to S104, spatial cognitive modeling is completed. The spatial cognitive modeling can analyze the spatial distribution law of task points through the generalized Poisson equation, optimize the path planning between targets, and reduce redundant paths.

[0065] S105. Obtain the spatio-temporal weight of the task point according to the time weight and spatial weight of each of the multiple task points.

[0066] In the embodiment of the present invention, by fusing the time weight and spatial weight, a dynamic spatio-temporal cognitive model is constructed to guide task scheduling and path optimization.

[0067] In the embodiment of the present invention, when calculating the spatio-temporal weight, the adjustment coefficients of the time weight and spatial weight can also be considered to balance the influence of time factors and spatial factors on intelligent planning, so as to dynamically integrate time information (time window of the task point) and spatial information (spatial position of the task point), and improve the comprehensiveness of task planning.

[0068] S106. Plan the execution order of the multiple task points with the goal of the highest task completion efficiency according to the spatio-temporal weights of each of the multiple task points.

[0069] In the embodiment of the present invention, a dynamic programming algorithm can be used to generate the optimal task execution order, determine the best task scheduling order, and maximize the task completion efficiency.

[0070] In the embodiment of the present invention, the spatio-temporal weight dynamically integrates time information and spatial information, and can achieve the global optimal solution of task scheduling and path optimization.

[0071] In the embodiment of the present invention, a multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism is also provided. Specifically, as Figure 3 shown, which shows the step flow chart of the multi-objective intelligent planning method based on the spatio-temporal cognitive mechanism. The method includes the following steps: S201. According to the time window of each of the multiple task points, model the characteristics of the time-dependent function of each of the multiple task points changing with time, and obtain the time characteristic function of each of the multiple task points.

[0072] S202. Determine the time weight of each of the multiple task points according to the time window of each of the multiple task points and the time characteristic function of each of the multiple task points.

[0073] S203. According to the spatial positions of the multiple task points, model the attraction relationship between the task point and other task points, and determine the distribution intensity function of the task point. The attraction relationship between task points is used to characterize the degree of being on the way between task points.

[0074] S204. For each of the multiple task points, obtain the spatial weight of the task point according to the distribution intensity function of the task point.

[0075] S205. Obtain the spatio-temporal weight of the task point according to the respective time weight and spatial weight of the multiple task points.

[0076] S206. According to the respective spatio-temporal weights of the multiple task points, plan the execution order of the multiple task points with the goal of the highest task completion efficiency.

[0077] Steps S201 - S206 are similar to the above steps S101 - S106 and will not be elaborated here.

[0078] S207. Determine every two adjacent task points in the execution order of the multiple task points.

[0079] S208. According to the multiple paths between every two adjacent task points in the execution order, plan the optimal path for traversing the multiple task points.

[0080] In the embodiment of the present invention, the ant colony algorithm can be combined with the spatio-temporal cognitive model to generate the optimal path according to the paths between every two adjacent task points in the execution order.

[0081] Specifically, in the embodiment of the present invention, after obtaining the execution order of the task points considering the time information and spatial information of each task point based on the above steps S201 - S206, based on this execution order, further combining the spatial positions of each task point and all spatial information within the task execution range (including road information, obstacle information, etc.), obtain the multiple paths between every two adjacent task points in the execution order, and finally plan the optimal path for traversing the multiple task points.

[0082] In the embodiment of the present invention, when the spatial position of any one task point is updated, the time weights and spatial weights of all task points in the whole can be dynamically updated to dynamically plan the execution order of the multiple task points in the whole.

[0083] Specifically, the method further includes the following steps: S11. For any one of the multiple task points, after detecting that the spatial position of the task point is updated, determine the updated spatial weights and updated spatio-temporal weights of the multiple task points according to the updated spatial position of the task point.

[0084] S12. According to the updated spatio-temporal weights of the multiple task points, re-plan the execution order of the multiple task points with the goal of the highest task completion efficiency.

[0085] In the embodiments of the present invention, when a new task point is detected, the time weights and space weights of all task points in the whole can be dynamically updated based on the time information and space information of the new task point, so as to dynamically plan the execution order of multiple task points in the whole, and further dynamically adjust the optimal path for traversing multiple task points in the whole.

[0086] Specifically, the method further includes the following steps: S21, after detecting a new task point, determine the space-time weight of the new task point, and determine the updated space weights and updated space-time weights of each of the multiple task points according to the spatial position of the new task point.

[0087] S22, according to the updated space-time weights of each of the multiple task points and the space-time weight of the new task point, with the goal of the highest task completion efficiency, re-plan the execution order of the multiple task points.

[0088] In the embodiments of the present invention, when it is detected that any task point is deleted, the time weights and space weights of all task points in the whole can be dynamically updated based on the time information and space information of deleting the task point, so as to dynamically plan the execution order of multiple task points in the whole, and further dynamically adjust the optimal path for traversing multiple task points in the whole.

[0089] Specifically, the method further includes the following steps: S31, after detecting that any one of the multiple task points is deleted, determine the updated space weights and updated space-time weights of the remaining task points respectively.

[0090] S32, according to the updated space-time weights of the remaining task points respectively, with the goal of the highest task completion efficiency, re-plan the execution order of the remaining task points.

[0091] In the embodiments of the present invention, when it is detected that the time window of any task point is updated, the time weights and space weights of all task points in the whole can be dynamically updated based on the time information and space information of updating the task point, so as to dynamically plan the execution order of multiple task points in the whole, and further dynamically adjust the optimal path for traversing multiple task points in the whole.

[0092] Specifically, the method further includes the following steps: S41, for any one of the multiple task points, after detecting that the time window of the task point is updated, according to the updated time window of the task point, determine the updated time weight and updated space-time weight of the task point.

[0093] S42. Re-plan the execution order of the multiple task points with the goal of maximizing the task completion efficiency according to the updated spatio-temporal weights of this task point and the spatio-temporal weights of other task points respectively, and further dynamically adjust the optimal path for traversing multiple task points globally.

[0094] For the sake of easy understanding, the following further explains Figure 4 the multi-objective intelligent planning method based on spatio-temporal cognitive mechanism provided by the embodiments of the present invention. As Figure 4 shown, in the embodiments of the present invention, the time window of each task point among the current multiple task points can be obtained as time information, and the spatial position of each task point among the current multiple task points can be obtained as spatial information. Then, time window parsing and spatial distribution identification are performed for all task points. Among them, for the time information, based on time window parsing, the time window constraint of each task point is converted into a mathematical expression, and further time characteristic modeling is performed to obtain the time characteristic function of each task point. Further, time-dependent weight optimization is completed to obtain the time weight of each task point. For the spatial information, based on the spatial distribution representation of all task points, multi-scale feature extraction is performed to obtain the feature basis function of each task point at each scale. Further, based on the path optimization weight, the spatial weight of each task point is obtained. Then, spatio-temporal weight integration is performed based on the time weight and spatial weight of each task point to obtain the spatio-temporal weight of each task point, and further intelligent planning of global task points considering time information and spatial information is realized. During the task execution process, path planning and real-time adjustment can also be performed based on dynamic task scheduling information (for example: adding task points, deleting task points, modifying the time information or spatial information of task points).

[0095] Based on the same inventive concept, the present invention also provides a multi-objective intelligent planning device based on spatio-temporal cognitive mechanism. As Figure 5 shown, it shows a structural block diagram of the multi-objective intelligent planning device based on spatio-temporal cognitive mechanism. The multi-objective intelligent planning device 500 based on spatio-temporal cognitive mechanism includes: A time modeling module 501, configured to model the characteristics of the time-dependent functions of the multiple task points changing with time according to the time windows of the multiple task points respectively, and obtain the time characteristic functions of the multiple task points respectively; A time weight determination module 502, configured to determine the time weights of the multiple task points respectively according to the time windows of the multiple task points and the time characteristic functions of the multiple task points respectively; A spatial modeling module 503, configured to model the attraction relationship between this task point and other task points according to the spatial positions of the multiple task points, and determine the distribution intensity function of this task point. The attraction relationship between task points is used to characterize the degree of being on the same route between task points; A spatial weight determination module 504, configured to obtain the spatial weight of each task point among the multiple task points according to the distribution intensity function of the task point; A spatio-temporal weight determination module 505, configured to obtain the spatio-temporal weight of each task point according to the respective time weights and spatial weights of the multiple task points; A planning module 506, configured to plan the execution order of the multiple task points with the goal of maximizing the task completion efficiency according to the respective spatio-temporal weights of the multiple task points.

[0096] Optionally, the time modeling module 501 is configured to: Determine the respective time dependence functions of the multiple task points according to the respective time windows of the multiple task points, where the time dependence function of a task point is used to characterize whether any moment is within the time window of the task; Model the characteristics of the respective time dependence functions of the multiple task points changing with time using fractional differential equations and the respective time dependence functions of the multiple task points, to obtain the respective time characteristic functions of the multiple task points.

[0097] Optionally, the spatial weight determination module 504 is configured to: For each task point among the multiple task points, perform feature decomposition of the distribution intensity function of the task point at multiple scales to obtain the weight corresponding to each scale and the feature basis function of the task point at each scale; For each task point among the multiple task points, obtain the spatial weight of the task point according to the feature basis function of the task point at each scale and the distance between the task point and each task point at the scale.

[0098] Optionally, the apparatus further includes: A first detection module, configured to, for any one of the multiple task points, after detecting that the spatial position of the task point is updated, determine the updated spatial weights and updated spatio-temporal weights of the multiple task points according to the updated spatial position of the task point; A first update module, configured to re-plan the execution order of the multiple task points with the goal of maximizing the task completion efficiency according to the respective updated spatio-temporal weights of the multiple task points.

[0099] Optionally, the apparatus further includes: A second detection module, configured to, after detecting a new task point, determine the spatio-temporal weight of the new task point, and determine the updated spatial weights and updated spatio-temporal weights of the multiple task points according to the spatial position of the new task point; A second update module, configured to re-plan the execution order of the multiple task points with the highest task completion efficiency according to the updated spatio-temporal weights of the multiple task points and the spatio-temporal weight of the newly added task point.

[0100] Optionally, the device further includes: A third detection module, configured to determine the updated spatial weight and the updated spatio-temporal weight of each of the remaining task points after detecting that any one of the multiple task points is deleted; A third update module, configured to re-plan the execution order of the remaining task points with the highest task completion efficiency according to the updated spatio-temporal weights of the remaining task points.

[0101] Optionally, the device further includes: A fourth detection module, configured to, for any one of the multiple task points, determine the updated time weight and the updated spatio-temporal weight of the task point according to the updated time window of the task point after detecting that the time window of the task point is updated; A fourth update module, configured to re-plan the execution order of the multiple task points with the highest task completion efficiency according to the updated spatio-temporal weight of the task point and the spatio-temporal weights of the other task points.

[0102] Optionally, the device further includes: A determination module, configured to determine every two adjacent task points in the execution order of the multiple task points; A traversal module, configured to plan an optimal path for traversing the multiple task points according to multiple paths between every two adjacent task points in the execution order.

[0103] Based on the same inventive concept, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the steps in the multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism as described in any one of the above embodiments are implemented when the processor executes.

[0104] Based on the same inventive concept, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the steps in the multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism as described in any one of the above embodiments are implemented when the program is executed by a processor.

[0105] Based on the same inventive concept, the present invention provides a computer program product, including a computer program / instructions, and the steps in the multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism as described in any one of the above embodiments are implemented when the computer program / instructions are executed by a processor.

[0106] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0107] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, devices, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to the flowcharts and / or block diagrams of methods, terminal devices (devices), and computer program products according to the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable terminal devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable terminal devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0109] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable terminal device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0110] These computer program instructions can also be loaded onto a computer or other programmable terminal device, such that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0111] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0112] Finally, it should also be noted that in the present invention, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the element.

[0113] The above has introduced in detail a multi-objective intelligent planning method based on a spatio-temporal cognitive mechanism provided by the present invention. Specific examples are used in the present invention to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A multi-objective intelligent planning method based on spatiotemporal cognitive mechanism, characterized in that: The method comprises: Modeling the time-dependent characteristics of the time-dependent functions of the multiple task points according to the respective time windows of the multiple task points to obtain the time characteristic functions of the multiple task points; Determining the time weights of the multiple task points according to the time windows of the multiple task points and the time characteristic functions of the multiple task points; According to the spatial positions of the multiple task points, the attraction relationship between the task point and other task points is modeled to determine the distribution intensity function of the task point, and the attraction relationship between the task points is used to characterize the degree of convenience between the task points; For each task point among the multiple task points, obtaining a spatial weight of the task point according to a distribution intensity function of the task point; According to the time weights and space weights of the multiple task points, the time and space weights of the task point are obtained; According to the respective spatiotemporal weights of the multiple task points, the execution order of the multiple task points is planned with the highest task completion efficiency as the goal.

2. The multi-objective intelligent planning method based on spatiotemporal cognitive mechanism according to claim 1 is characterized in that: Modeling the time-dependent characteristics of the time-dependent functions of the multiple task points according to the respective time windows of the multiple task points to obtain the time characteristic functions of the multiple task points, including: Determine the time dependency functions of the multiple task points according to their respective time windows, wherein the time dependency function of a task point is used to characterize whether any moment is within the time window of the task; Using fractional-order differential equations and the respective time-dependent functions of the plurality of task points, the characteristics of the respective time-dependent functions of the plurality of task points that vary with time are modeled to obtain the respective time characteristic functions of the plurality of task points.

3. The multi-objective intelligent planning method based on spatiotemporal cognitive mechanism according to claim 1 is characterized in that: For each task point among the multiple task points, a spatial weight of the task point is obtained according to the distribution intensity function of the task point, including: For each of the multiple task points, perform characteristic decomposition of the distribution intensity function of the task point at multiple scales to obtain a weight corresponding to each scale and a characteristic basis function of the task point at each scale; For each task point among the multiple task points, a spatial weight of the task point is obtained according to a characteristic basis function of the task point at each scale and a distance between the task point and each task point at the scale.

4. The multi-objective intelligent planning method based on spatiotemporal cognitive mechanism according to claim 1 is characterized in that: The method further comprises: For any task point among the multiple task points, after detecting that the spatial position of the task point is updated, determining the updated spatial weights and updated spatiotemporal weights of the multiple task points according to the updated spatial position of the task point; According to the updated spatiotemporal weights of each of the multiple task points, the execution order of the multiple task points is replanned with the goal of maximizing task completion efficiency.

5. The multi-objective intelligent planning method based on spatiotemporal cognitive mechanism according to claim 1 is characterized in that: The method further comprises: After detecting a newly added task point, determining the spatiotemporal weight of the newly added task point, and, according to the spatial position of the newly added task point, determining the updated spatial weight and the updated spatiotemporal weight of each of the plurality of task points; According to the updated spatiotemporal weights of the multiple task points and the spatiotemporal weights of the newly added task points, the execution order of the multiple task points is replanned with the goal of maximizing task completion efficiency.

6. The multi-objective intelligent planning method based on spatiotemporal cognitive mechanism according to claim 1 is characterized in that: The method further comprises: After detecting that any of the plurality of task points is deleted, determining updated spatial weights and updated spatiotemporal weights of the remaining task points; According to the updated spatiotemporal weights of the remaining task points, the execution order of the remaining task points is replanned with the goal of maximizing task completion efficiency.

7. The multi-objective intelligent planning method based on spatiotemporal cognitive mechanism according to claim 1 is characterized in that: The method further comprises: For any task point among the multiple task points, after detecting that the time window of the task point is updated, determining an updated time weight and an updated spatiotemporal weight of the task point according to the updated time window of the task point; According to the updated spatiotemporal weight of the task point and the spatiotemporal weights of other task points, the execution order of the multiple task points is replanned with the goal of maximizing task completion efficiency.

8. The multi-objective intelligent planning method based on spatiotemporal cognitive mechanism according to any one of claims 1 to 7, characterized in that: After planning the execution order of the multiple task points, the method further includes: According to the execution order of the multiple task points, determine every two task points whose execution order is adjacent to each other; According to a plurality of paths between every two task points that are adjacent in execution order, an optimal path that traverses the plurality of task points is planned.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the multi-objective intelligent planning method based on the spatiotemporal cognitive mechanism described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-objective intelligent planning method based on the spatiotemporal cognitive mechanism described in any one of claims 1 to 8 is implemented.

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