Route automatic planning and designing method and system

Abstract

The application relates to a route automatic planning design method and system, which comprises the following steps: constructing a dynamic constraint map and an energy consumption feasibility gear set; based on the dynamic constraint map and the energy consumption feasibility gear set, automatically dividing a task to be executed by a UAV into multiple strips, constructing a task time graph according to the endpoints of the strips, take-off and landing points and a task substitution relationship; generating multiple path segments, and combining the energy consumption feasibility gear set to assign a recommended flight gear and a passing time period to each path segment to form an initial route plan; based on the initial route plan, outputting an enhanced pre-flight plan; in the flight process of the UAV, collecting positioning, weather, obstacle avoidance, power and link quality data in real time, and reporting the data to an online event bus; when the online event bus triggers a preset event, updating the pre-flight plan to obtain an incremental update plan; evaluating and improving the incremental update plan to generate a final planned target route. The application can improve the accuracy of UAV route planning.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to automatic route planning and design methods, systems, electronic devices, and non-transitory computer-readable storage media. Background Technology

[0002] Currently, drone flight path planning primarily relies on a combination of manual pre-setting and semi-automatic path generation. A common practice is for operators to manually set key waypoints, flight altitude, speed, and mission triggering conditions in ground station software based on mission requirements, and then draw the path using Geographic Information System (GIS) maps or satellite imagery data. Some more advanced systems also incorporate raster map-based automatic obstacle avoidance algorithms or rule-based shortest path planning methods, thus taking into account terrain undulations, no-fly zones, and safe flight distances during the planning phase.

[0003] However, manually setting waypoints relies on the operator's experience, making it difficult to respond promptly to sudden weather changes, temporary no-fly zone adjustments, or the appearance of real-time obstacles. Rule-based planning methods, on the other hand, often only optimize a single objective (such as the shortest distance) and lack a comprehensive consideration of multiple factors, including energy consumption distribution, task priority, and aerial overlap. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing an automatic route planning and design method, system, electronic device, and non-transitory computer-readable storage medium that can improve the accuracy of UAV route planning and design.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: This invention provides an automatic route planning and design method, the method comprising: Collect and integrate multi-source heterogeneous data related to drones to construct a dynamic constraint map and a set of energy consumption feasibility levels; Based on the dynamic constraint map and the energy consumption feasibility level set, the unmanned aerial vehicle's tasks to be executed are automatically divided into multiple strips, and a task time map is constructed using the endpoints, take-off and landing points and task substitution relationships of the strips. Based on the task time map and the dynamic constraint map, a spatiotemporal flight corridor that meets the safety constraints is extracted. Multi-objective topology routing and task sorting are performed on the spatiotemporal flight corridor to generate multiple path segments. The recommended flight level and passage time are assigned to each path segment in combination with the energy consumption feasibility level set to form an initial route plan. Based on the initial route plan, multiple disturbance scenarios are applied for batch simulation verification to identify vulnerable sections in the initial route plan and generate alternative sub-plans and emergency replacement sections, outputting an enhanced pre-flight plan. During the flight of the UAV, real-time data on positioning, weather, obstacle avoidance, power consumption, and link quality are collected and reported to the online event bus. When the online event bus triggers a preset event, the dynamic constraint map, the mission time map, the spatiotemporal flight corridor, and the energy consumption feasibility level set are invoked within a local window of the current flight segment to perform a rapid multi-objective trade-off and update the pre-flight plan to obtain an incremental update plan. After the UAV completes its flight, the incremental update plan is evaluated and improved based on the UAV's execution trajectory, trigger event records, actual energy consumption, and coverage quality, generating the final planned target route.

[0006] Optionally, the construction of the dynamic constraint map and energy consumption feasibility level set includes: Based on the multi-source heterogeneous data, a geographically constrained base layer with spatiotemporal index is generated; Based on the aforementioned geographic constraint base layer and historical and short-term weather forecast data, a dynamic constraint map is generated, which includes time-period effective labels and confidence levels. Based on the dynamic constraint map, the UAV's model parameters, and historical flight logs, the energy consumption feasibility level set is generated by discretization.

[0007] Optionally, based on the dynamic constraint map and the energy consumption feasibility level set, the step of automatically dividing the unmanned aerial vehicle's (UAV) tasks into multiple strips, and constructing a task time map using the endpoints, take-off and landing points, and task substitution relationships of the strips, includes: Based on the dynamic constraint map and the energy consumption feasibility level set, the flyable range and suitable flight level for each mission area are determined. The flightable range automatically divides the mission area into multiple strips that meet the requirements of overlap rate and resolution, and marks the strip priority and time window; The task time map is generated based on the endpoints, start and end points, and substitution relationships between the multiple strips.

[0008] Optionally, based on the task time map and the dynamic constraint map, a spatiotemporal flight corridor satisfying safety constraints is extracted. Multi-objective topology routing and task sorting are performed on the spatiotemporal flight corridor to generate multiple path segments. Then, combined with the energy consumption feasibility level set, recommended flight levels and passage times are assigned to each path segment to form an initial flight plan, including: Based on the mission timeline and dynamic constraint map, feasible flight segments are selected and the spatiotemporal flight corridor that meets safety constraints is constructed. Within the spatiotemporal flight corridor, multi-objective topology routing and task sorting are performed based on task priority, flight safety, and energy consumption distribution to generate multiple path segments; Based on the energy consumption feasibility gear set, recommended flight gears and passage times are assigned to each of the path segments to form the initial route plan.

[0009] Optionally, the output of the enhanced pre-flight plan includes: Based on the initial flight plan, a set of disturbance scenarios is constructed, which includes weather changes, temporary flight restrictions, communication interruptions, and newly appearing obstacles; Based on the set of disturbance scenarios, the initial route plan is batch simulated and verified to identify the vulnerable road sections that are susceptible to the disturbance. For the vulnerable road sections, generate alternative sub-plans and emergency replacement sections, and output the enhanced pre-flight plan.

[0010] Optionally, updating the pre-flight plan to obtain an incremental update plan includes: During flight, positioning, weather, obstacle avoidance, battery power, and link quality data are collected in real time, and the data is input into the online event bus for status analysis. When the online event bus triggers a preset event, the dynamic constraint map, mission time map, spatiotemporal flight corridor and energy consumption feasibility level set are called in the local window of the current flight segment to perform a rapid multi-objective trade-off and obtain the trade-off result. The incremental update plan is generated based on the trade-off results, and the changed segments are sent to the drone for execution.

[0011] Optionally, evaluating and improving the incremental update plan to generate the final planned target route includes: The actual execution effect of the incremental update plan is analyzed based on the execution trajectory and mission completion record of the UAV. By combining trigger event records, actual energy consumption, and coverage quality, the accuracy, safety, and mission completion rate of route planning are comprehensively evaluated to obtain post-flight evaluation results. The post-flight evaluation results are used as the basis for improving the incremental update plan to generate the final planned target route.

[0012] Optionally, the method further includes: Based on the post-flight assessment results, update the confidence level and time-period effective parameters of the dynamic constraint map; Based on the post-flight assessment results, optimize the priority and flight parameter range of the energy consumption feasibility level set; Based on the post-flight assessment results, the strip substitution cost and priority rules of the mission timeline are adjusted.

[0013] Optionally, the multi-source heterogeneous data includes static terrain data, regulations and flight restriction data, historical and short-term weather forecasts, communication coverage data and obstacle information; the dynamic constraint map includes time period effective labels and confidence levels; and the energy consumption feasibility level set is generated based on the discretization of aircraft parameters and historical flight logs.

[0014] The present invention also provides an automatic route planning and design system, the system comprising: The first construction module is used to collect and fuse multi-source heterogeneous data related to UAVs to build a dynamic constraint map and a set of energy consumption feasibility levels; The second construction module is used to automatically divide the unmanned aerial vehicle's tasks into multiple strips based on the dynamic constraint map and the energy consumption feasibility level set, and to construct a task time map based on the endpoints, take-off and landing points and task substitution relationships of the strips. The initial route module is used to extract spatiotemporal flight corridors that meet safety constraints based on the mission time map and the dynamic constraint map, perform multi-objective topology routing and mission sorting on the spatiotemporal flight corridors, generate multiple path segments, and assign recommended flight levels and passage time periods to each path segment in combination with the energy consumption feasibility level set to form an initial route plan. The pre-flight planning module is used to perform batch simulation verification based on the initial route plan using multiple disturbance scenarios, identify vulnerable sections in the initial route plan, generate alternative sub-plans and emergency replacement sections, and output an enhanced pre-flight plan. The incremental update module is used to collect positioning, weather, obstacle avoidance, power consumption and link quality data in real time during the flight of the UAV and report them to the online event bus. When the online event bus triggers a preset event, it calls the dynamic constraint map, the mission time map, the spatiotemporal flight corridor and the energy consumption feasibility level set in the local window of the current flight segment to perform multi-objective rapid trade-off and update the pre-flight plan to obtain the incremental update plan. The target route module is used to evaluate and improve the incremental update plan based on the UAV's execution trajectory, trigger event records, actual energy consumption, and coverage quality after the UAV has finished flying, and generate the final planned target route.

[0015] In addition, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the automatic route planning and design method as described above.

[0016] In addition, to achieve the above objectives, the present invention also proposes a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the automatic route planning and design method described above.

[0017] The beneficial effects of this invention are: (1) This invention integrates static terrain, regulations and flight restrictions, weather forecasts, communication coverage and obstacle information to construct a dynamic constraint map (DCM), and establishes an energy consumption feasibility set (EFL) based on aircraft parameters and historical flight logs. This allows for full consideration of various environmental factors and flight constraints during the planning stage, thereby improving the authenticity and operability of route planning.

[0018] (2) This invention divides the task area into strips and constructs a task time map (TTG), which can model task priority, time window and substitution relationship while meeting the requirements of image overlap rate and resolution, thereby realizing the deep integration of task constraints and route planning. (3) This invention performs multi-target topology routing and task sequencing within the spacetime flight corridor (SFC), and combines EFL allocation with recommended flight gear and passage time periods, which not only ensures flight safety, but also achieves energy consumption distribution optimization and reasonable arrangement of task priorities, overcoming the shortcomings of existing technologies that can only optimize for a single target (such as the shortest path).

[0019] In summary, this invention can achieve multi-source constraint fusion, comprehensive optimization of task priority and energy consumption, pre-flight robustness verification, in-flight dynamic replanning, and post-flight adaptive improvement, thereby significantly improving the intelligence, safety, and mission completion rate of UAV route planning. Attached Figure Description

[0020] Figure 1 A flowchart of the automatic route planning and design method provided by the present invention; Figure 2 This is a schematic diagram of the structure of an automatic route planning and design system provided by the present invention; Figure 3 A schematic diagram of a possible hardware structure of an electronic device provided by the present invention; Figure 4 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0023] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0024] Please see Figure 1 The present invention provides a flowchart of the automatic route planning and design method, including the following steps: Step 201: Collect and integrate multi-source heterogeneous data related to UAVs to construct a dynamic constraint map and a set of energy consumption feasibility levels.

[0025] The multi-source heterogeneous data includes static terrain data, regulations and flight restriction data, historical and short-term weather forecasts, communication coverage data and obstacle information. The dynamic constraint map includes time period effective labels and confidence levels. The energy consumption feasibility level set is generated by discretization based on aircraft parameters and historical flight logs.

[0026] In this embodiment, multi-source heterogeneous data related to the UAV are first collected, including static terrain data, regulations and flight restriction data, historical and short-term weather forecasts, communication coverage data, and obstacle information. By unifying the format and fusion the semantics of the above multi-source data, a dynamic constraint map (DCM) that reflects the dynamic constraints of the flight environment is constructed. Combined with UAV model parameters and historical flight logs, energy consumption performance is discretized to form an energy consumption feasibility level set (EFL) for subsequent route planning.

[0027] In some embodiments, step 201 may include: Generate a geographically constrained base layer with spatiotemporal index based on multi-source heterogeneous data; Based on the geographic constraint base layer and historical and short-term weather forecast data, a dynamic constraint map is generated, which includes time period effective labels and confidence levels. Based on the dynamic constraint map, UAV model parameters, and historical flight logs, a set of energy consumption feasibility levels is generated by discretization.

[0028] Specifically, firstly, based on the collected multi-source heterogeneous data, including terrain data, regulations and flight restriction data, obstacle information, and communication coverage, various types of data are formatted and spatially registered. On this basis, a geographic constraint base layer with spatiotemporal index is established. This layer can support fast retrieval of any geographic location and time dimension, providing a unified data foundation for subsequent constraint derivation.

[0029] Secondly, based on the geographic constraint base layer and combined with historical meteorological data and short-term weather forecast data, time-based modeling of flight feasibility in different regions is performed. In specific implementation, thresholds can be set for key meteorological elements such as wind speed, precipitation, and visibility, and time annotations can be added to the layer. Simultaneously, confidence levels are assigned to the validity of each time period, thereby generating a dynamic constraint map that includes time period validity annotations and confidence levels. This dynamic constraint map can dynamically reflect the flight feasibility and safety boundaries within different time periods.

[0030] Furthermore, based on the dynamic constraint map and combined with the UAV's model parameters (such as maximum flight time, payload, battery capacity, etc.) and energy consumption curves in historical flight logs, the energy consumption characteristics of the UAV under different mission conditions are statistically analyzed. The energy consumption performance is further discretized to form a set of energy consumption feasibility levels. This set can describe the energy consumption range and feasible mission duration of the UAV under different flight levels, thereby providing a basis for energy consumption constraints for subsequent route planning.

[0031] Step 202: Based on the dynamic constraint map and the energy consumption feasibility level set, the unmanned aerial vehicle's tasks to be performed are automatically divided into multiple strips, and a task time map is constructed using the endpoints, take-off and landing points and task substitution relationships of the strips.

[0032] In this embodiment, based on the Dynamic Constraint Map (DCM) and the Energy Feasibility Level Set (EFL), the feasible flight range and suitable flight level of the UAV within the mission area are first determined. Then, according to the mission requirements for image overlap rate, resolution, etc., the mission area is automatically divided into multiple strips. Furthermore, based on the endpoint positions of the strips, the UAV take-off and landing points, and the substitution relationships between different strips, a mission time map (TTG) is constructed, thereby introducing time windows and substitution constraints at the mission level.

[0033] In some embodiments, step 202 may include: Based on the dynamic constraint map and the energy consumption feasibility level set, the flyable range and suitable flight level of each mission area are determined. The flightable range automatically divides the mission area into multiple strips that meet the requirements of overlap rate and resolution, and marks the strip priority and time window; A task timeline is generated based on the endpoints, take-off and landing points, and substitution relationships between multiple stripes.

[0034] Specifically, firstly, based on the dynamic constraint map and the set of energy consumption feasibility levels, the flyable range of each mission area within different time periods can be determined. Then, combined with the UAV model parameters, the appropriate flight level within that range can be selected. Through this process, it can be ensured that the flight path in the mission area not only meets the dynamic safety constraints but also matches the energy consumption capacity of the UAV.

[0035] Secondly, within the flightable range, the mission area is automatically divided into multiple strips based on the mission's requirements for aerial overlap rate and image resolution. While dividing the area, the system assigns priorities to each strip and marks the executable time window for each strip based on the time period effective information of the dynamic constraint map, thereby establishing a set of strips with mission attributes and time constraints.

[0036] Next, a mission timeline is generated based on the endpoint positions of the strip set, the take-off and landing points of the UAVs, and the substitution relationships between the strips. The mission timeline can describe the sequence, substitution relationships, and time window constraints between the mission strips, thus providing a temporal mission model for subsequent route planning and path sequencing.

[0037] Step 203: Based on the mission time map and dynamic constraint map, extract the spatiotemporal flight corridor that meets the safety constraints, perform multi-objective topology routing and mission sorting on the spatiotemporal flight corridor, generate multiple path segments, and assign recommended flight levels and passage time periods to each path segment in combination with the energy consumption feasibility level set to form an initial route plan.

[0038] In this embodiment, based on the mission time map (TTG) and dynamic constraint map (DCM), a spatiotemporal flight corridor (SFC) that meets the requirements of flight safety distance and regulations is extracted. Within this corridor, topology routing and sorting are performed according to multiple objectives such as mission priority, energy consumption balance and flight safety to generate multiple feasible path segments. Then, combined with the energy consumption feasibility set (EFL), recommended flight levels and passage time periods are assigned to each path segment, and finally the initial route plan (Plan 0) is formed.

[0039] In some embodiments, step 203 may include: Based on the mission timeline and dynamic constraint map, feasible flight segments are selected and spatiotemporal flight corridors that meet safety constraints are constructed. Within the spatiotemporal flight corridor, multi-objective topology routing and task sequencing are performed based on mission priority, flight safety, and energy consumption distribution to generate multiple path segments. Based on the set of energy consumption feasibility levels, recommended flight levels and passage times are assigned to each route segment to form an initial route plan.

[0040] Specifically, firstly, based on the mission timeline and dynamic constraint map, combined with the time window of the mission strip, substitution relationships, and dynamic no-fly information, possible route candidates are screened. During the screening process, flight segments with no-fly conflicts, unfavorable weather, or insufficient safe distance are eliminated, while feasible flight segments that meet the constraints are retained. On this basis, a spatiotemporal flight corridor covering the take-off and landing points and the mission area is constructed. This corridor can dynamically describe the set of spatial paths that are allowed to fly within a given time range.

[0041] Secondly, within the aforementioned spatiotemporal flight corridor, considering multiple objective factors such as mission priority, flight safety, and energy consumption distribution, a topology modeling method is employed for path selection and mission sequencing. Through optimization of strip order, path connection methods, and mission substitution relationships, multiple path segments satisfying the constraints are generated. Each path segment includes a corresponding start point, end point, and associated mission strips, forming a preliminary path structure.

[0042] Next, based on the set of energy consumption feasibility levels, energy consumption matching and flight level allocation are performed on each of the path segments. During this process, the system recommends the optimal flight level for each path segment and determines a reasonable passage time period in conjunction with mission time window information, thereby ensuring that the UAV can meet both range and energy consumption constraints and follow mission timing requirements during mission execution. Finally, a complete initial route plan is formed.

[0043] Step 204: Based on the initial route plan, perform batch simulation verification using multiple disturbance scenarios, identify vulnerable sections in the initial route plan, generate alternative sub-plans and emergency replacement sections, and output the enhanced pre-flight plan.

[0044] In this embodiment, based on the initial route plan Plan0, multiple simulation scenarios are constructed, including disturbance factors such as sudden weather changes, temporary no-fly zones, communication link anomalies, and the appearance of dynamic obstacles. The initial route plan is batch simulated and verified under these scenarios to identify vulnerable road sections with significant risks under disturbance conditions. Alternative sub-plans and emergency alternative sections are generated for these vulnerable road sections, thereby outputting the enhanced pre-flight plan (Plan1).

[0045] In some embodiments, step 204 may include: Based on the initial flight plan, a set of disturbance scenarios is constructed, which includes weather changes, temporary flight restrictions, communication interruptions, and newly emerging obstacles. The initial route plan is batch simulated and verified based on the disturbance scenario set to identify vulnerable road sections that are easily affected; For vulnerable road sections, generate alternative sub-plans and emergency replacement sections, and output an enhanced pre-flight plan.

[0046] Specifically, firstly, a set of disturbance scenarios is constructed based on the initial flight path plan. This set includes sudden changes in weather conditions (such as increased wind speed, precipitation, or decreased visibility), the addition or adjustment of temporary no-fly zones, communication link interruptions during flight, and newly appearing dynamic obstacles. By constructing these disturbance scenarios, various external uncertainties that the UAV may encounter during operation can be simulated.

[0047] Secondly, based on the set of disturbance scenarios, the initial flight plan is subjected to batch simulation verification. During this process, the system replays the flight execution process in multiple virtual environments, analyzes the execution results of the flight under different disturbance conditions, and thus identifies vulnerable segments that are susceptible to external changes. These vulnerable segments are typically those that are sensitive to weather conditions, are adjacent to no-fly zones, depend on communication link quality, or have obstacle conflicts.

[0048] Furthermore, for identified vulnerable road segments, the system generates corresponding alternative sub-plans and emergency replacement segments. In practice, by locally recalculating the mission timeline and spatiotemporal flight corridor, one or more feasible alternative paths can be provided for the vulnerable road segment. These alternative solutions are then integrated with the original flight path to form a redundant, enhanced flight path structure. Finally, the system outputs a pre-flight plan containing the main plan and multiple sets of alternative plans, providing reliable support for the UAV to respond to emergencies during mission execution.

[0049] Step 205: During the flight of the UAV, real-time data on positioning, weather, obstacle avoidance, power consumption, and link quality are collected and reported to the online event bus. When the online event bus triggers a preset event, the dynamic constraint map, mission time map, spatiotemporal flight corridor, and energy consumption feasibility level set are invoked within the local window of the current flight segment to quickly weigh multiple objectives, update the pre-flight plan, and obtain an incremental update plan.

[0050] In this embodiment, during the actual flight of the UAV, positioning data, meteorological data, obstacle avoidance perception data, power information, and communication link quality are continuously collected and aggregated into the Online Event Bus (OEB). When the Online Event Bus detects that a preset event has been triggered, it calls the Dynamic Constraint Map (DCM), Mission Time Map (TTG), Spatiotemporal Flight Corridor (SFC), and Energy Consumption Feasibility Level Set (EFL) within the local window of the current flight segment to perform a rapid trade-off. Based on the trade-off results, the pre-flight plan Plan1 is adjusted to obtain an incremental update plan (Plan1.x), which is then sent to the UAV for execution in real time.

[0051] In some embodiments, step 205 may include: During flight, the system collects real-time data on positioning, weather, obstacle avoidance, battery power, and link quality, and inputs the data into the online event bus for status analysis. When the online event bus triggers a preset event, it calls the dynamic constraint map, mission time map, spatiotemporal flight corridor and energy consumption feasibility level set in the local window of the current flight segment to perform a rapid trade-off between multiple objectives and obtain the trade-off result. An incremental update plan is generated based on the trade-offs, and the changed segments are sent to the drones for execution.

[0052] Specifically, firstly, during the drone's flight, its positioning information, meteorological data, obstacle avoidance sensor data, remaining battery power, and communication link quality are collected in real time, and this data is input into an online event bus. The online event bus continuously analyzes and monitors the collected data to determine whether the current flight is within a normal range or whether a preset event requiring dynamic adjustment has been triggered.

[0053] Secondly, when the online event bus triggers a preset event, such as flight deviation, localized weather deterioration, insufficient power, or unstable link, the system, within a local window of the current flight segment, invokes the dynamic constraint map, mission time map, spatiotemporal flight corridor, and energy consumption feasibility level set for joint calculation. During this process, the system rapidly generates a trade-off result based on multiple objective factors such as mission completion, flight safety, and energy consumption balance, to determine alternative paths and mission execution sequences.

[0054] Furthermore, based on the aforementioned trade-offs, the system automatically generates an incremental update plan and only modifies the flight path for the segments requiring adjustment, then issues the plan to the UAV for execution. This incremental update approach ensures the overall stability of the original pre-flight plan while enabling the UAV to respond quickly to unforeseen localized situations, thereby improving the continuity and robustness of flight missions.

[0055] Step 206: After the UAV flight is completed, the incremental update plan is evaluated and improved based on the UAV's execution trajectory, trigger event records, actual energy consumption and coverage quality, and the final planned target route is generated.

[0056] In this embodiment, after the UAV mission is completed and the flight ends, the execution effect of the incremental update plan Plan1.x is evaluated based on the actual execution trajectory, mission trigger event records, energy consumption, and image coverage quality. Based on the evaluation results, the dynamic constraint map DCM, energy consumption feasibility set EFL, and mission time map TTG are corrected and improved to generate the final planned target route and provide optimization basis for subsequent missions.

[0057] In some embodiments, step 206 may include: The actual execution effect of the incremental update plan is analyzed based on the execution trajectory and mission completion record of the UAV. By combining trigger event records, actual energy consumption, and coverage quality, the accuracy, safety, and mission completion rate of route planning are comprehensively evaluated to obtain post-flight evaluation results. The post-flight evaluation results are used as the basis for improving the incremental update plan to generate the final planned target route.

[0058] Specifically, firstly, the actual execution effect of the incremental update plan is analyzed based on the UAV's execution trajectory and mission completion record. During this process, the system compares the UAV's actual flight path with the preset incremental update plan to identify segment deviations, execution delays, and mission coverage deviations, thereby determining the UAV's plan compliance during mission execution.

[0059] Secondly, the accuracy, safety, and mission completion rate of the flight route planning can be comprehensively evaluated by combining the triggering events recorded during the flight, the actual energy consumption of the UAV, and the coverage quality of the mission area. Through cross-validation of data from different dimensions, the system can generate a comprehensive post-flight evaluation result. This result not only reflects the UAV's adaptability to dynamic events during the mission but also reveals any shortcomings in the planning.

[0060] Furthermore, the post-flight evaluation results can be used as a basis for improvement to optimize and revise the incremental update plan, generating the final planned target route. This target route incorporates reflections and improvements on historical execution and can also serve as a reference for subsequent tasks, thereby enhancing the accuracy and reliability of route planning in similar future scenarios.

[0061] In some embodiments, the present invention may further include: Based on the post-flight assessment results, update the confidence level and time-period effective parameters of the dynamic constraint map; Based on the post-flight assessment results, optimize the priority of the energy consumption feasibility set and the range of flight parameters; Based on the post-flight assessment results, the strip substitution cost and priority rules of the mission timeline were adjusted.

[0062] Specifically, firstly, based on the post-flight assessment results, the confidence level and effective time period parameters of the dynamic constraint map are updated. During this process, the system uses the actual events triggered during the UAV's operation and their impact on flight safety as the basis for correction. Information such as no-fly zone boundaries, weather-affected areas, and communication blind spots in the dynamic constraint map are recalibrated, and the confidence level and effective time period of each constraint condition are adjusted accordingly to make the dynamic constraint map more consistent with the characteristics of the real flight environment.

[0063] Secondly, based on the post-flight assessment results, the priority and flight parameter range of the energy consumption feasibility set are optimized. By comparing the difference between actual and estimated energy consumption, the system can identify the deviations in energy efficiency and mission performance of different flight levels, and revise the priority ranking and parameter range of energy consumption levels based on these deviations, thereby achieving a better balance between energy consumption distribution and flight performance in subsequent route planning.

[0064] Furthermore, based on the post-flight evaluation results, the strip substitution costs and priority rules in the task timeline are adjusted. In practice, the system combines task coverage quality and execution delays to correct the substitution weights between different strips and dynamically adjusts the strip sorting rules, enabling the task timeline to more accurately reflect task completion rates and timeliness requirements. Through this optimization process, the constraint expression capability of the task timeline and the rationality of task allocation are continuously improved.

[0065] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of an automatic route planning and design system provided by the present invention.

[0066] like Figure 2 As shown, an automatic route planning and design system proposed in this embodiment of the invention includes: The first construction module 301 is used to collect and fuse multi-source heterogeneous data related to UAVs to construct a dynamic constraint map and a set of energy consumption feasibility levels. The second construction module 302 is used to automatically divide the unmanned aerial vehicle's tasks to be executed into multiple strips based on the dynamic constraint map and the energy consumption feasibility level set, and to construct a task time map based on the endpoints, take-off and landing points and task substitution relationships of the strips. The initial route module 303 is used to extract a spatiotemporal flight corridor that meets safety constraints based on the mission time map and the dynamic constraint map, perform multi-objective topology routing and mission sorting on the spatiotemporal flight corridor, generate multiple path segments, and assign recommended flight levels and passage time periods to each path segment in combination with the energy consumption feasibility level set to form an initial route plan. The pre-flight planning module 304 is used to perform batch simulation verification based on the initial route plan using multiple disturbance scenarios, identify vulnerable sections in the initial route plan and generate alternative sub-plans and emergency replacement sections, and output the enhanced pre-flight plan. The incremental update module 305 is used to collect positioning, weather, obstacle avoidance, power consumption and link quality data in real time during the flight of the UAV and report them to the online event bus. When the online event bus triggers a preset event, it calls the dynamic constraint map, the mission time map, the spatiotemporal flight corridor and the energy consumption feasibility level set in the local window of the current flight segment to perform multi-objective rapid trade-off and update the pre-flight plan to obtain the incremental update plan. The target route module 306 is used to evaluate and improve the incremental update plan based on the UAV's execution trajectory, trigger event records, actual energy consumption and coverage quality after the UAV finishes its flight, and generate the final planned target route.

[0067] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 3 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it performs the following steps: Collect and integrate multi-source heterogeneous data related to drones to construct a dynamic constraint map and a set of energy consumption feasibility levels; Based on the dynamic constraint map and the energy consumption feasibility level set, the unmanned aerial vehicle's tasks to be executed are automatically divided into multiple strips, and a task time map is constructed using the endpoints, take-off and landing points and task substitution relationships of the strips. Based on the task time map and the dynamic constraint map, a spatiotemporal flight corridor that meets the safety constraints is extracted. Multi-objective topology routing and task sorting are performed on the spatiotemporal flight corridor to generate multiple path segments. The recommended flight level and passage time are assigned to each path segment in combination with the energy consumption feasibility level set to form an initial route plan. Based on the initial route plan, multiple disturbance scenarios are applied for batch simulation verification to identify vulnerable sections in the initial route plan and generate alternative sub-plans and emergency replacement sections, outputting an enhanced pre-flight plan. During the flight of the UAV, real-time data on positioning, weather, obstacle avoidance, power consumption, and link quality are collected and reported to the online event bus. When the online event bus triggers a preset event, the dynamic constraint map, the mission time map, the spatiotemporal flight corridor, and the energy consumption feasibility level set are invoked within a local window of the current flight segment to perform a rapid multi-objective trade-off and update the pre-flight plan to obtain an incremental update plan. After the UAV completes its flight, the incremental update plan is evaluated and improved based on the UAV's execution trajectory, trigger event records, actual energy consumption, and coverage quality, generating the final planned target route.

[0068] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 4 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, it performs the following steps: Collect and integrate multi-source heterogeneous data related to drones to construct a dynamic constraint map and a set of energy consumption feasibility levels; Based on the dynamic constraint map and the energy consumption feasibility level set, the unmanned aerial vehicle's tasks to be executed are automatically divided into multiple strips, and a task time map is constructed using the endpoints, take-off and landing points and task substitution relationships of the strips. Based on the task time map and the dynamic constraint map, a spatiotemporal flight corridor that meets the safety constraints is extracted. Multi-objective topology routing and task sorting are performed on the spatiotemporal flight corridor to generate multiple path segments. The recommended flight level and passage time are assigned to each path segment in combination with the energy consumption feasibility level set to form an initial route plan. Based on the initial route plan, multiple disturbance scenarios are applied for batch simulation verification to identify vulnerable sections in the initial route plan and generate alternative sub-plans and emergency replacement sections, outputting an enhanced pre-flight plan. During the flight of the UAV, real-time data on positioning, weather, obstacle avoidance, power consumption, and link quality are collected and reported to the online event bus. When the online event bus triggers a preset event, the dynamic constraint map, the mission time map, the spatiotemporal flight corridor, and the energy consumption feasibility level set are invoked within a local window of the current flight segment to perform a rapid multi-objective trade-off and update the pre-flight plan to obtain an incremental update plan. After the UAV completes its flight, the incremental update plan is evaluated and improved based on the UAV's execution trajectory, trigger event records, actual energy consumption, and coverage quality, generating the final planned target route.

[0069] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0070] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0075] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An automatic route planning and design method, characterized in that, The method includes: Collect and integrate multi-source heterogeneous data related to drones to construct a dynamic constraint map and a set of energy consumption feasibility levels; Based on the dynamic constraint map and the energy consumption feasibility level set, the unmanned aerial vehicle's tasks to be executed are automatically divided into multiple strips, and a task time map is constructed using the endpoints, take-off and landing points and task substitution relationships of the strips. Based on the task time map and the dynamic constraint map, a spatiotemporal flight corridor that meets the safety constraints is extracted. Multi-objective topology routing and task sorting are performed on the spatiotemporal flight corridor to generate multiple path segments. The recommended flight level and passage time are assigned to each path segment in combination with the energy consumption feasibility level set to form an initial route plan. Based on the initial route plan, multiple disturbance scenarios are applied for batch simulation verification to identify vulnerable sections in the initial route plan and generate alternative sub-plans and emergency replacement sections, outputting an enhanced pre-flight plan. During the flight of the UAV, real-time data on positioning, weather, obstacle avoidance, power consumption, and link quality are collected and reported to the online event bus. When the online event bus triggers a preset event, the dynamic constraint map, the mission time map, the spatiotemporal flight corridor, and the energy consumption feasibility level set are invoked within a local window of the current flight segment to perform a rapid multi-objective trade-off and update the pre-flight plan to obtain an incremental update plan. After the UAV completes its flight, the incremental update plan is evaluated and improved based on the UAV's execution trajectory, trigger event records, actual energy consumption, and coverage quality, generating the final planned target route.

2. The automatic route planning and design method according to claim 1, characterized in that, The construction of the dynamic constraint map and energy consumption feasibility level set includes: Based on the multi-source heterogeneous data, a geographically constrained base layer with spatiotemporal index is generated; Based on the aforementioned geographic constraint base layer and historical and short-term weather forecast data, a dynamic constraint map is generated, which includes time-period effective labels and confidence levels. Based on the dynamic constraint map, the UAV's model parameters, and historical flight logs, the energy consumption feasibility level set is generated by discretization.

3. The automatic route planning and design method according to claim 2, characterized in that, Based on the dynamic constraint map and the energy consumption feasibility level set, the unmanned aerial vehicle's (UAV) tasks are automatically divided into multiple strips. A task time map is constructed using the endpoints, take-off and landing points, and task substitution relationships of the strips, including: Based on the dynamic constraint map and the energy consumption feasibility level set, the flyable range and suitable flight level for each mission area are determined. The flightable range automatically divides the mission area into multiple strips that meet the requirements of overlap rate and resolution, and marks the strip priority and time window; The task time map is generated based on the endpoints, start and end points, and substitution relationships between the multiple strips.

4. The automatic route planning and design method according to claim 3, characterized in that, Based on the task timeline and the dynamic constraint map, a spatiotemporal flight corridor satisfying safety constraints is extracted. Multi-objective topology routing and task sequencing are performed on these corridors to generate multiple path segments. Combined with the energy consumption feasibility set, recommended flight levels and passage times are assigned to each path segment to form an initial flight plan, including: Based on the mission timeline and dynamic constraint map, feasible flight segments are selected and the spatiotemporal flight corridor that meets safety constraints is constructed. Within the spatiotemporal flight corridor, multi-objective topology routing and task sorting are performed based on task priority, flight safety, and energy consumption distribution to generate multiple path segments; Based on the energy consumption feasibility gear set, recommended flight gears and passage times are assigned to each of the path segments to form the initial route plan.

5. The automatic route planning and design method according to claim 4, characterized in that, The output of the enhanced pre-flight plan includes: Based on the initial flight plan, a set of disturbance scenarios is constructed, which includes weather changes, temporary flight restrictions, communication interruptions, and newly appearing obstacles; Based on the set of disturbance scenarios, the initial route plan is batch simulated and verified to identify the vulnerable road sections that are susceptible to the disturbance. For the vulnerable road sections, generate alternative sub-plans and emergency replacement sections, and output the enhanced pre-flight plan.

6. The automatic route planning and design method according to claim 5, characterized in that, The update of the pre-flight plan, resulting in an incremental update plan, includes: During flight, positioning, weather, obstacle avoidance, battery power, and link quality data are collected in real time, and the data is input into the online event bus for status analysis. When the online event bus triggers a preset event, the dynamic constraint map, mission time map, spatiotemporal flight corridor and energy consumption feasibility level set are called in the local window of the current flight segment to perform a rapid multi-objective trade-off and obtain the trade-off result. The incremental update plan is generated based on the trade-off results, and the changed segments are sent to the drone for execution.

7. The automatic route planning and design method according to claim 6, characterized in that, The process of evaluating and improving the incremental update plan to generate the final planned target route includes: The actual execution effect of the incremental update plan is analyzed based on the execution trajectory and mission completion record of the UAV. By combining trigger event records, actual energy consumption, and coverage quality, the accuracy, safety, and mission completion rate of route planning are comprehensively evaluated to obtain post-flight evaluation results. The post-flight evaluation results are used as the basis for improving the incremental update plan to generate the final planned target route.

8. The automatic route planning and design method according to claim 7, characterized in that, The method further includes: Based on the post-flight assessment results, update the confidence level and time-period effective parameters of the dynamic constraint map; Based on the post-flight assessment results, optimize the priority and flight parameter range of the energy consumption feasibility level set; Based on the post-flight assessment results, the strip substitution cost and priority rules of the mission timeline are adjusted.

9. The automatic route planning and design method according to claim 8, characterized in that, The multi-source heterogeneous data includes static terrain data, regulations and flight restriction data, historical and short-term weather forecasts, communication coverage data and obstacle information. The dynamic constraint map includes time period effective labels and confidence levels. The energy consumption feasibility level set is generated based on aircraft parameters and historical flight logs through discretization.

10. An automatic route planning and design system, characterized in that, The system includes: The first construction module is used to collect and fuse multi-source heterogeneous data related to UAVs to build a dynamic constraint map and a set of energy consumption feasibility levels; The second construction module is used to automatically divide the unmanned aerial vehicle's tasks into multiple strips based on the dynamic constraint map and the energy consumption feasibility level set, and to construct a task time map based on the endpoints, take-off and landing points and task substitution relationships of the strips. The initial route module is used to extract spatiotemporal flight corridors that meet safety constraints based on the mission time map and the dynamic constraint map, perform multi-objective topology routing and mission sorting on the spatiotemporal flight corridors, generate multiple path segments, and assign recommended flight levels and passage time periods to each path segment in combination with the energy consumption feasibility level set to form an initial route plan. The pre-flight planning module is used to perform batch simulation verification based on the initial route plan using multiple disturbance scenarios, identify vulnerable sections in the initial route plan, generate alternative sub-plans and emergency replacement sections, and output an enhanced pre-flight plan. The incremental update module is used to collect positioning, weather, obstacle avoidance, power consumption and link quality data in real time during the flight of the UAV and report them to the online event bus. When the online event bus triggers a preset event, it calls the dynamic constraint map, the mission time map, the spatiotemporal flight corridor and the energy consumption feasibility level set in the local window of the current flight segment to perform multi-objective rapid trade-off and update the pre-flight plan to obtain the incremental update plan. The target route module is used to evaluate and improve the incremental update plan based on the UAV's execution trajectory, trigger event records, actual energy consumption, and coverage quality after the UAV has finished flying, and generate the final planned target route.

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