Unmanned aerial vehicle self-adaptive path planning method and system for road disaster site

Abstract

The invention discloses an unmanned aerial vehicle adaptive path planning method and system for a road disaster site. The method comprises the following steps: collecting road disaster site data in real time; performing region division on the road disaster site to obtain a plurality of sub-regions; wherein each sub-region corresponds to one group of data acquisition information set; based on the ratio of the number of the specific disaster feature road sections in each sub-region to the total number of the road sections in the corresponding sub-region, calculating a comprehensive information aggregation rate in the sub-region; determining the unmanned aerial vehicle path planning adjustment amount of each sub-region according to the comprehensive information aggregation rate of each sub-region; and according to the path planning adjustment amount of each sub-region, adjusting the path planning of the unmanned aerial vehicle in the geographic position range corresponding to the sub-region. Through the above mode, the planned unmanned aerial vehicle path can better adapt to a complex environment of a road disaster site, the flight safety of the unmanned aerial vehicle is effectively improved, and then the task execution efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicle (UAV) path planning, and in particular to a method and system for adaptively planning a UAV path at a highway disaster site. Background Art

[0002] In the emergency response of highway disaster sites, drones play an important role in on-site monitoring, material transportation, emergency search, etc. due to their flexibility and efficiency. However, the existing drone path planning technology has many shortcomings when dealing with the complex environment of highway sites. Traditional path planning algorithms often plan based on only a single data source, such as relying only on a single map data, resulting in the planned path being unable to adapt to the actual situation on site, and the drone flight safety is not high, which in turn affects the efficiency of task execution. Summary of the invention

[0003] The main technical problem solved by this application is to provide a method and system for adaptive path planning of unmanned aerial vehicles at highway disaster sites, which can effectively improve the safety of unmanned aerial vehicle flight and thus improve the efficiency of mission execution.

[0004] In order to solve the above technical problems, a technical solution adopted in the present application is: to provide a method for adaptive path planning of unmanned aerial vehicles at highway disaster sites, the method comprising: real-time collection of highway disaster site data; regionalization of the highway disaster site to obtain multiple sub-areas; wherein each sub-area corresponds to a set of data collection information sets; based on the ratio of the number of specific disaster characteristic sections in each sub-area to the total number of sections in the corresponding sub-area, calculating the comprehensive information aggregation rate in the sub-area; according to the comprehensive information aggregation rate of each sub-area, determining the adjustment amount of the unmanned aerial vehicle path planning for each sub-area; according to the path planning adjustment amount for each sub-area, adjusting the path planning of the unmanned aerial vehicle within the geographical location range corresponding to the sub-area.

[0005] Among them, based on the ratio of the number of sections with specific disaster characteristics in each sub-region to the total number of sections in the corresponding sub-region, the comprehensive information aggregation rate in the sub-region is calculated, including: counting the ratio of the number of sections containing specific disaster characteristics in each sub-region to the total number of sections in the corresponding sub-region as the information aggregation rate of the sub-region about the specific disaster characteristics; assigning weights to each specific disaster characteristic in each sub-region, and calculating the comprehensive information aggregation rate of each sub-region.

[0006] Among them, according to the comprehensive information aggregation rate of each sub-area, the UAV path planning adjustment amount of each sub-area is determined, including: through a first optimization algorithm, the path planning adjustment amount is determined by maximizing the sum of the first preset values ​​of all sub-areas, wherein the first preset value is calculated based on the estimated comprehensive information aggregation rate, path risk assessment value, task completion assessment value and path length after the sub-area newly added path planning adjustment amount, the first preset value is positively correlated with the estimated comprehensive information aggregation rate and the task completion assessment value, and the first preset value is negatively correlated with the path risk assessment value and the path length.

[0007] Among them, according to the comprehensive information aggregation rate of each sub-area, the UAV path planning adjustment amount of each sub-area is determined, including: through the second optimization algorithm, the path planning adjustment amount is determined by minimizing the sum of the second preset values ​​of all sub-areas, wherein the second preset value is calculated based on the estimated comprehensive information aggregation rate and the proportion of path planning adjustment after the new path planning adjustment of the sub-area, the second preset value is negatively correlated with the estimated comprehensive information aggregation rate, and the second preset value is positively correlated with the proportion of path planning adjustment.

[0008] Among them, according to the comprehensive information aggregation rate of each sub-area, the drone path planning adjustment amount of each sub-area is determined, including: comparing the comprehensive information aggregation rate of each sub-area with a preset threshold, in response to the comprehensive information aggregation rate being less than the preset threshold, using the preset number as the drone path planning adjustment amount of the sub-area, and in response to the comprehensive information aggregation rate being greater than or equal to the preset threshold, determining the path planning adjustment amount based on the difference between the preset threshold and the comprehensive information aggregation rate.

[0009] Among them, a weight is assigned to each specific disaster feature in each sub-region, and the comprehensive information aggregation rate of each sub-region is calculated, including: the sum of the products obtained by multiplying the information aggregation rate of each specific disaster feature in each sub-region by the corresponding weight is used as the comprehensive information aggregation rate of the sub-region; wherein the sum of the weight coefficients of the specific disaster features in each sub-region is 1.

[0010] Among them, based on the ratio of the number of each specific disaster characteristic section in each sub-area to the total number of sections in the corresponding sub-area, the comprehensive information aggregation rate in the sub-area is calculated, including: for each sub-area, the planned path of the drone in the sub-area is divided into a number of sections of preset lengths, the number of sections of the planned path containing each specific disaster characteristic in the sub-area is taken as the number of each specific disaster characteristic section in the sub-area, and the total number of sections of the planned path in the sub-area is taken as the total number of sections in the corresponding sub-area.

[0011] Among them, the first preset value is equal to the difference between the first product and the second product and the third product; the first product is equal to the product of the estimated comprehensive information aggregation rate and the first weight coefficient, the second product is equal to the product of the path risk assessment value and the second weight coefficient, the third product is equal to the product of the path length and the third weight coefficient, and the sum of the first weight coefficient, the second weight coefficient and the third weight coefficient is 1, and all are greater than 0.

[0012] Among them, the second preset value is equal to the fourth product minus the fifth product, wherein the fourth product is equal to the product of the path planning adjustment ratio and the fourth weight coefficient, and the fifth product is equal to the product of the estimated comprehensive information aggregation rate and the fifth weight coefficient.

[0013] In order to solve the above technical problems, another technical solution adopted in the present application is: to provide an adaptive path planning system for unmanned aerial vehicles at highway disaster sites, the system comprising: a collection module, a partitioning module, a calculation module, a determination module and an adjustment module, wherein the collection module is used to collect highway disaster site data in real time; the partitioning module is used to divide the highway disaster site into regions to obtain multiple sub-regions; wherein each sub-region corresponds to a set of data collection information sets; the calculation module is used to calculate the comprehensive information aggregation rate within each sub-region based on the ratio of the number of specific disaster characteristic sections in each sub-region to the total number of sections in the corresponding sub-region; the determination module is used to determine the unmanned aerial vehicle path planning adjustment amount for each sub-region according to the comprehensive information aggregation rate of each sub-region; the adjustment module is used to adjust the path planning of the unmanned aerial vehicle within the geographical location range corresponding to the sub-region according to the path planning adjustment amount of each sub-region.

[0014] Different from the existing technology, the beneficial effect of this application is: this application collects highway disaster site data in real time and divides the site into regions, calculates the comprehensive information aggregation rate based on the ratio of the specific disaster characteristic sections to the total number of sections in each sub-region, and then determines the adjustment amount of the drone path planning in each sub-region based on the aggregation rate, and finally adjusts the drone path planning within the corresponding geographical location range. Based on the data actually collected on site, combined with the characteristics of the sections in different sub-regions, and comprehensively considered from multiple dimensions, it overcomes the defect that traditional path planning only relies on a single data source, so that the planned drone path can better adapt to the complex environment of the highway disaster site, effectively improve the safety of drone flight, and thus improve the efficiency of task execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of an implementation method of an adaptive path planning method for unmanned aerial vehicles at a highway disaster site of the present application.

[0016] Figure 2 It is a structural schematic diagram of an implementation scheme of a UAV adaptive path planning system for a highway disaster site of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and effect of the present application clearer and more specific, the present application is further described in detail below with reference to the accompanying drawings and examples.

[0018] The terms "first" and "second" herein are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. Furthermore, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments may be combined with one or more other embodiments to form new embodiments.

[0019] In some related technologies, path planning algorithms are often based on a single data source, such as relying solely on map data, without fully considering the real-time changing environmental factors at the disaster site, such as smoke generated by fire, dynamic changes in the scope of road flooding caused by floods, etc., resulting in the planned path being unable to adapt to the actual situation at the site, and the drone flight safety and mission execution efficiency are low.

[0020] See also Figure 1 , Figure 1 It is a flow chart of an implementation method of an adaptive path planning method for unmanned aerial vehicles at a highway disaster site of the present application. The method comprises: Step S11: real-time collection of highway disaster site data.

[0021] When a highway disaster occurs, it is necessary to obtain various information on the scene in a timely and comprehensive manner. UAVs equipped with a variety of professional sensors can be used for data collection. For example, optical cameras are responsible for obtaining high-resolution highway images, which can clearly record the damage to the road, including the location and size of road cracks and collapses, as well as the distribution of vehicles and personnel; infrared thermal imagers play a key role in low-visibility environments such as at night or in smoky conditions, by detecting heat sources, accurately locating the location of the fire, and sensing the body temperature signals of people; lidar uses laser beams to build accurate three-dimensional terrain models, which can quickly measure the location, height and shape of obstacles; meteorological sensors monitor meteorological parameters such as wind speed, wind direction, precipitation and visibility in real time. The collected data will be transmitted in real time to the drone's own storage device or ground control station through wireless transmission technology.

[0022] Step S12: Divide the highway disaster site into regions to obtain multiple sub-regions; wherein each sub-region corresponds to a set of data collection information.

[0023] After obtaining the basic data of the highway disaster site, it is necessary to reasonably divide the entire disaster site into regions. Specifically, the highway disaster site can be divided into multiple sub-regions based on the geographical location information of the highway, such as longitude and latitude data, combined with the type of disaster and the actual impact range. Taking the mountain highway where the debris flow disaster occurred as an example, the road section directly impacted by the debris flow and the surrounding areas that may be affected by the secondary disaster can be divided into key sub-regions, while the road section that is far away but has a certain traffic impact can be divided into general sub-regions. Each sub-region forms an independent set of data collection information, which covers the disaster-related data in the area, such as the type of disaster, the severity of the disaster, etc., as well as environmental data, such as topography, meteorology, etc. Before dividing the area, it is necessary to obtain the basic geographic information of the highway disaster site in advance, including the total mileage, direction, surrounding mountains, rivers and other geographical environmental features of the highway, as well as the existing drone path planning information.

[0024] Step S13: Based on the ratio of the number of each specific disaster characteristic road section in each sub-region to the total number of road sections in the corresponding sub-region, the comprehensive information aggregation rate in the sub-region is calculated.

[0025] The comprehensive information aggregation rate is calculated by the ratio of the number of each specific disaster characteristic road section in the sub-region to the total number of road sections in the corresponding sub-region. This ratio can quantitatively represent the complexity of the disaster situation in the sub-region or the concentration of special situations. For example, if the comprehensive information aggregation rate of a sub-region is high, it means that there are relatively more specific disaster characteristic road sections in the sub-region, and there may be more road damage, obstacles or other factors that affect the flight of drones; conversely, a low aggregation rate means that the situation in the sub-region is relatively simple and the road conditions are relatively good. Among them, the sections in each sub-region can be divided according to certain rules, for example, they can be divided according to fixed lengths.

[0026] Step S14: Determine the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area.

[0027] Adjusting the path planning according to the comprehensive information aggregation rate of different sub-areas can enable drones to complete tasks more efficiently. In sub-areas with high comprehensive information aggregation rates, if sufficient path adjustments are not made, drones may have to frequently change routes or suspend missions due to frequent disasters, resulting in a significant extension of mission time. By reasonably increasing the amount of path planning adjustments and planning a more suitable path in advance, drones can fly more smoothly and quickly reach the target location to perform monitoring, transportation and other tasks, thereby improving the overall mission execution efficiency.

[0028] Step S15: According to the path planning adjustment amount of each sub-area, the path planning of the UAV is adjusted within the geographical location range corresponding to the sub-area.

[0029] Specifically, the path planning data visualization system can be used to assist in the adjustment. The system uses a map as the core to display the current location of the drone, the planned path, disaster site information, etc. Different marks on the map represent elements such as drones, disaster areas, obstacles, etc. Click on the mark to view detailed information.

[0030] The above scheme collects data from highway disaster sites in real time and divides the site into regions. It calculates the comprehensive information aggregation rate based on the ratio of the number of specific disaster characteristic sections to the total number of sections in each sub-region, and then determines the adjustment amount of the drone path planning in each sub-region based on the aggregation rate, and finally adjusts the drone path planning within the corresponding geographical location range. Based on the data actually collected on site, combined with the characteristics of the sections in different sub-regions, and comprehensively considered from multiple dimensions, it overcomes the defect that traditional path planning only relies on a single data source, making the planned drone path more adaptable to the complex environment of highway disaster sites, effectively improving the safety of drone flights, and thus improving the efficiency of mission execution.

[0031] In some embodiments, step S13: based on the ratio of the number of road sections with specific disaster characteristics in each sub-region to the total number of road sections in the corresponding sub-region, the comprehensive information aggregation rate in the sub-region is calculated. This includes: counting the ratio of the number of road sections containing specific disaster characteristics in each sub-region to the total number of road sections in the corresponding sub-region as the information aggregation rate of the sub-region about the specific disaster characteristics; assigning a weight to each specific disaster characteristic in each sub-region, and calculating the comprehensive information aggregation rate of each sub-region.

[0032] In some embodiments, a weight is assigned to each specific disaster feature in each sub-region, and the comprehensive information aggregation rate of each sub-region is calculated, including: taking the sum of the products obtained by multiplying the information aggregation rate of each specific disaster feature in each sub-region by the corresponding weight as the comprehensive information aggregation rate of the sub-region; wherein the sum of the weight coefficients of the specific disaster features in each sub-region is 1.

[0033] In some embodiments, based on the ratio of the number of sections with specific disaster characteristics in each sub-region to the total number of sections in the corresponding sub-region, the comprehensive information aggregation rate in the sub-region is calculated, including: for each sub-region, dividing the planned path of the drone in the sub-region into several sections with preset lengths, taking the number of sections of the planned path containing each specific disaster characteristic in the sub-region as the number of sections with specific disaster characteristics in the sub-region, and taking the total number of sections of the planned path in the sub-region as the total number of sections in the corresponding sub-region.

[0034] In some specific embodiments, a road section with specific disaster characteristics refers to a road section with specific disaster characteristics in a sub-region. For example, in a fire disaster, the road section affected by the fire is the road section with specific disaster characteristics; in a road collapse disaster, the collapsed road section is the road section with specific disaster characteristics.

[0035] For each sub-region, first determine the disaster characteristic associated areas corresponding to different disaster types. For example, for flood disasters, based on the boundaries of the flooded area, combined with the water flow speed and direction, determine the road areas that may be affected by the flood impact as associated areas; for earthquake disasters, according to the distribution of earthquake intensity, the areas where road sections with disaster characteristics such as road collapse and ground cracks may occur are designated as associated areas. Next, divide the drone planned path in the sub-region into several small sections of equal length, and the length of each section can be set according to actual conditions, such as 50 meters or 100 meters. Then, count the number of road sections containing specific disaster characteristics.

[0036] Assume that the total number of road sections in a sub-area is N total , the number of road sections covered by a certain disaster feature is N covered , then the information aggregation rate r of the sub-region about the disaster characteristics is calculated as: .

[0037] When there are multiple disaster characteristics, the information aggregation rate of each disaster characteristic can be assigned a corresponding disaster characteristic weight according to the severity of the impact of different disaster characteristics on the drone path planning. For example, in a sub-area with both fire and road collapse, the fire poses a greater threat to the flight safety of the drone, so the fire characteristic is assigned a higher weight; while if the road collapse has a relatively small impact on the drone flight path, it is assigned a lower weight. Assuming there are k disaster characteristics, the first The information aggregation rate of disaster characteristics is , the weight is ,in, , then the comprehensive information aggregation rate R of the sub-region is: .

[0038] The determination of disaster characteristic weights can be done through expert evaluation, statistical analysis of historical data, and other methods, combined with the degree of impact of different disasters on drone flights in actual situations.

[0039] In some embodiments, a variety of methods can be used to determine the path planning adjustment amount. For example, the adjustment amount can be determined by optimizing the algorithm to maximize or minimize the sum of certain preset values; or the comprehensive information aggregation rate can be compared with a preset threshold and the adjustment amount can be determined based on the comparison result.

[0040] In some application scenarios, step S14: determining the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area, including: determining the path planning adjustment amount by maximizing the sum of the first preset values ​​of all sub-areas through a first optimization algorithm, wherein the first preset value is calculated based on the estimated comprehensive information aggregation rate, path risk assessment value, task completion assessment value and path length after the sub-area newly added path planning adjustment amount, the first preset value is positively correlated with the estimated comprehensive information aggregation rate and the task completion assessment value, and the first preset value is negatively correlated with the path risk assessment value and the path length.

[0041] In some embodiments, the first preset value is equal to the difference between the first product and the second product and the third product; the first product is equal to the product of the estimated comprehensive information aggregation rate and the first weight coefficient, the second product is equal to the product of the path risk assessment value and the second weight coefficient, the third product is equal to the product of the path length and the third weight coefficient, and the sum of the first weight coefficient, the second weight coefficient and the third weight coefficient is 1, and all are greater than 0.

[0042] In some specific embodiments, the path planning adjustment amount is determined by maximizing the sum of preset values ​​of all sub-areas through an optimization algorithm. For the The path planning adjustment amount for each area, For the The number of existing route plans in the area, for The path risk assessment value of the region (the higher the risk, the larger the value, which can be determined according to factors such as disaster type, meteorological conditions, and terrain complexity. For example, near the fire area, the risk assessment value is higher; in areas with bad weather and high wind speed, the risk assessment value is also increased accordingly), For the The mission completion evaluation value of each area (the higher the completion, the greater the value. For example, if the drone’s mission is to conduct a comprehensive monitoring of the disaster site, the higher the proportion of the monitored area to the total target area, the greater the mission completion evaluation value). is the path length of the region, is the weight coefficient, and . Custom utility function It can be defined as: .

[0043] The first preset value of the sub-area It is equal to the difference between the first product and the second and third products, that is: .

[0044] Among them, the first product: .

[0045] This item reflects the impact of task completion on the first preset value. For the The current task completion evaluation value of each region, is the estimated task completion evaluation value after adding the path planning adjustment amount. The number of existing path plans, Adjustment amount for path planning. Indicates the contribution of existing path planning to task completion, It is the expected contribution of the newly added path planning adjustment to the task completion. is the total number of path plans after adjustment. The first product is positively correlated with the task completion degree. The higher the task completion degree, the larger the first product is, and the greater the positive contribution to the first preset value is.

[0046] Second product: .

[0047] This item is used to measure the impact of path risk on the first preset value. is the current path risk assessment value, It is the estimated path risk assessment value after adding the path planning adjustment amount. is the risk situation of the existing path, The risk condition of the newly added path, the larger the second product is, the greater the negative impact on the first preset value is.

[0048] The third product: .

[0049] This term takes into account the path length, is the current path length, is the estimated path length after the path planning adjustment amount is added. The larger the third product is, the greater the negative impact on the first preset value is.

[0050] Used to weigh the relative importance of task completion, path risk, and path length in the calculation of the first preset value. For example, in the target task, if more emphasis is placed on task completion, then If the risk of the disaster site is high, the value can be increased to ensure the safety of the drone. The weight of .

[0051] After calculating the first preset value of each sub-area After that, the utility function can be maximized by using common optimization algorithms such as gradient descent, genetic algorithm or simulated annealing algorithm, and finally the path planning adjustment amount for each sub-area can be obtained. In actual operation, the calculation process of the optimization algorithm can be realized by computer programming, and the path planning adjustment amount can be continuously adjusted through iterative calculation until a solution that maximizes the utility function is found.

[0052] In other application scenarios, step S14: determining the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area, including: determining the path planning adjustment amount by minimizing the sum of the second preset values ​​of all sub-areas through a second optimization algorithm, wherein the second preset value is calculated based on the estimated comprehensive information aggregation rate and the proportion of the path planning adjustment after the new path planning adjustment of the sub-area, the second preset value is negatively correlated with the estimated comprehensive information aggregation rate, and the second preset value is positively correlated with the proportion of the path planning adjustment.

[0053] In some embodiments, the second preset value is equal to the fourth product minus the fifth product, wherein the fourth product is equal to the product of the path planning adjustment ratio and the fourth weight coefficient, and the fifth product is equal to the product of the estimated comprehensive information aggregation rate and the fifth weight coefficient.

[0054] In some specific embodiments, the path planning adjustment amount is determined by minimizing the sum of the second preset values ​​of all sub-areas through an optimization algorithm. In the sub-areas, It represents the estimated comprehensive information aggregation rate after adding the path planning adjustment amount. Represents the proportion of path planning adjustment, is the fourth weight coefficient, is the fifth weight coefficient. , used to weigh the impact of the two factors, can be adjusted according to actual conditions. The second preset value of the sub-area is equal to the fourth product minus the fifth product, that is: ; Among them, the proportion of path planning adjustment The path planning adjustment amount can be adjusted by the sub-area The sum of all sub-area path planning adjustments The ratio is calculated, that is: .

[0055] Estimated comprehensive information aggregation rate The calculation considers the comprehensive information aggregation rate of the current sub-region 、Number of existing path planning , Path planning adjustment And other factors. The calculation method is: In this formula, is an adjustable coefficient that reflects the impact of the newly added path planning adjustment on the estimated comprehensive information aggregation rate. Through these formulas, the second preset value of each sub-area can be calculated , and then minimize the sum of the second preset values ​​of all sub-areas through optimization algorithms such as gradient descent and genetic algorithm , and finally determine the adjustment amount of the drone path planning in each sub-area. In practical applications, the appropriate , and value to improve the accuracy of determining the path planning adjustment amount.

[0056] For example, when performing an emergency task, more attention is paid to the impact of the path planning adjustment on the task completion. At this time, the In conventional monitoring tasks, in order to ensure the stability of path planning, it can be reasonably adjusted The size of is used to balance the relationship between the estimated comprehensive information aggregation rate and the proportion of path planning adjustment. At the same time, as the situation at the disaster site changes in real time, the two weight coefficients can be dynamically adjusted to ensure the accuracy and rationality of the path planning adjustment, so that the drone can better adapt to the complex environment of the disaster site.

[0057] In other application scenarios, step S14: determining the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area, including: comparing the comprehensive information aggregation rate of each sub-area with a preset threshold, in response to the comprehensive information aggregation rate being less than the preset threshold, using the preset number as the UAV path planning adjustment amount for the sub-area, and in response to the comprehensive information aggregation rate being greater than or equal to the preset threshold, determining the path planning adjustment amount based on the difference between the preset threshold and the comprehensive information aggregation rate.

[0058] Specifically, the comprehensive information aggregation rate of each sub-region is compared with a preset threshold. The setting of the preset threshold can be determined based on historical disaster data and experience, combined with different disaster types and drone performance. For example, for areas with severe fire disasters, a higher threshold is set; for disaster areas such as general road waterlogging, a relatively low threshold is set. If the comprehensive information aggregation rate is less than the threshold, it means that the path in the area is greatly affected by the disaster, and the path planning may need to be adjusted significantly. The preset number can be used as the path planning adjustment amount for the sub-region. The preset number can also be set according to actual conditions, such as the size of the sub-region, the severity of the disaster, and other factors. For sub-regions with large areas and severe disasters, the preset number can be appropriately increased. If the comprehensive information aggregation rate is greater than or equal to the threshold, it means that the path in the area is relatively less affected by the disaster, the path planning is relatively reasonable, and the path planning adjustment amount for the sub-region is 0. Alternatively, the path planning adjustment amount is determined based on the difference between the threshold and the comprehensive information aggregation rate. The larger the difference, the larger the path planning adjustment amount, and the two are positively correlated. For example, for every 0.1 increase in the difference, the path planning adjustment amount increases by 1. In actual operation, the automated process of threshold judgment and adjustment amount calculation can be realized through programming to improve the efficiency of path planning adjustment.

[0059] In some embodiments, step S15 adjusts the path planning of the drone within the geographical location range corresponding to the sub-area according to the path planning adjustment amount determined for each sub-area. The path planning data visualization system is used to assist the adjustment. The system uses the map as the core to display the current location of the drone, the planned path, the disaster site information, etc. Different marks are used on the map to represent drones, disaster areas, obstacles, etc., and detailed information can be viewed by clicking on the mark. The system includes a menu bar, providing functional options such as "path viewing", "optimization suggestions", and "parameter settings". The "path viewing" function responds to the path mark on the map being clicked, and displays detailed information of the path, such as path length, waypoints, estimated flight time, etc., and can also view path historical data, such as path adjustment records at different times. The "optimization suggestion" function automatically calculates and gives optimized path suggestions based on the path planning adjustment amount of the sub-area and the real-time disaster situation, including waypoints and flight altitudes of the adjusted path. The system provides an "optimization execution" button, which automatically adjusts the drone path planning and updates the map and information panel after clicking it. The "parameter setting" interface is used to manually set some parameters, such as weight values, number of regional divisions, etc. After the setting is completed, click the "apply settings" button, and the system recalculates and updates the map and information panel. In addition, the system also has search and data export functions. The search function can quickly locate relevant information by entering keywords, such as drone numbers, disaster area names, etc.; the export data function can export historical path planning data, monitoring data, etc., for further analysis. During the path planning adjustment process, adaptive adjustments are made based on real-time collected meteorological data. If strong winds are encountered, the flight altitude is appropriately lowered, the flight direction is adjusted according to the wind direction, and the path is adjusted to avoid strong wind areas; in low visibility conditions, the flight speed is reduced, the sensor scanning frequency is increased, and infrared thermal imagers are used to assist navigation. Task priorities and path planning strategies are dynamically adjusted according to the development of the disaster. For example, in a fire disaster, if the fire spreads and expands, priority is given to planning the path to the direction of the fire spread and monitoring is strengthened; if a new target location is found, the path is adjusted to go to the rescue.

[0060] The above scheme collects data from the highway disaster site in real time and divides the site into regions. It calculates the comprehensive information aggregation rate based on the ratio of the specific disaster characteristic sections to the total number of sections in each sub-region, and then determines the adjustment amount of the drone path planning in each sub-region based on the aggregation rate, and finally adjusts the drone path planning within the corresponding geographical location range. In this way, based on the data actually collected on site, combined with the characteristics of the sections in different sub-regions, and comprehensively considered from multiple dimensions, it overcomes the defect that traditional path planning only relies on a single data source, so that the planned drone path can better adapt to the complex environment of the highway disaster site, effectively improve the safety of drone flight, and thus improve the efficiency of task execution.

[0061] See also Figure 2 , Figure 2It is a structural schematic diagram of an implementation scheme of a UAV adaptive path planning system for a highway disaster site of the present application.

[0062] The UAV adaptive path planning system 20 for highway disaster sites includes: a collection module 21, a partition module 22, a calculation module 23, a determination module 24 and an adjustment module 25. The collection module 21 is used to collect highway disaster site data in real time; the partition module 22 is used to divide the highway disaster site into regions to obtain multiple sub-regions; each sub-region corresponds to a set of data collection information sets; the calculation module 23 is used to calculate the comprehensive information aggregation rate in each sub-region based on the ratio of the number of each specific disaster characteristic road section in each sub-region to the total number of road sections in the corresponding sub-region; the determination module 24 is used to determine the UAV path planning adjustment amount of each sub-region according to the comprehensive information aggregation rate of each sub-region; the adjustment module 25 is used to adjust the path planning of the UAV in the geographical location range corresponding to the sub-region according to the path planning adjustment amount of each sub-region.

[0063] In some embodiments, the calculation module 23 calculates the comprehensive information aggregation rate within the sub-region based on the ratio of the number of road sections with specific disaster characteristics in each sub-region to the total number of road sections in the corresponding sub-region, including: counting the ratio of the number of road sections containing specific disaster characteristics in each sub-region to the total number of road sections in the corresponding sub-region as the information aggregation rate of the sub-region regarding the specific disaster characteristics; assigning weights to each specific disaster characteristic in each sub-region, and calculating the comprehensive information aggregation rate of each sub-region.

[0064] In some embodiments, the determination module 24 determines the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area, including: determining the path planning adjustment amount by maximizing the sum of the first preset values ​​of all sub-areas through a first optimization algorithm, wherein the first preset value is calculated based on the estimated comprehensive information aggregation rate, path risk assessment value, task completion assessment value and path length after the sub-area newly added path planning adjustment amount, the first preset value is positively correlated with the estimated comprehensive information aggregation rate and the task completion assessment value, and the first preset value is negatively correlated with the path risk assessment value and the path length.

[0065] Among them, the first preset value is equal to the difference between the first product and the second product and the third product; the first product is equal to the product of the estimated comprehensive information aggregation rate and the first weight coefficient, the second product is equal to the product of the path risk assessment value and the second weight coefficient, the third product is equal to the product of the path length and the third weight coefficient, and the sum of the first weight coefficient, the second weight coefficient and the third weight coefficient is 1, and all are greater than 0.

[0066] In some embodiments, the determination module 24 determines the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area, including: determining the path planning adjustment amount by minimizing the sum of the second preset values ​​of all sub-areas through a second optimization algorithm, wherein the second preset value is calculated based on the estimated comprehensive information aggregation rate and the proportion of the path planning adjustment amount after the new path planning adjustment of the sub-area, the second preset value is negatively correlated with the estimated comprehensive information aggregation rate, and the second preset value is positively correlated with the proportion of the path planning adjustment amount.

[0067] Among them, the second preset value is equal to the fourth product minus the fifth product, wherein the fourth product is equal to the product of the path planning adjustment ratio and the fourth weight coefficient, and the fifth product is equal to the product of the estimated comprehensive information aggregation rate and the fifth weight coefficient.

[0068] In some embodiments, the determination module 24 determines the drone path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area, including: comparing the comprehensive information aggregation rate of each sub-area with a preset threshold, in response to the comprehensive information aggregation rate being less than the preset threshold, using the preset number as the drone path planning adjustment amount for the sub-area, and in response to the comprehensive information aggregation rate being greater than or equal to the preset threshold, determining the path planning adjustment amount based on the difference between the preset threshold and the comprehensive information aggregation rate.

[0069] In some embodiments, the calculation module 23 assigns a weight to each specific disaster feature in each sub-region and calculates the comprehensive information aggregation rate of each sub-region, including: taking the sum of the products obtained by multiplying the information aggregation rate of each specific disaster feature in each sub-region by the corresponding weight as the comprehensive information aggregation rate of the sub-region; wherein the sum of the weight coefficients of the specific disaster features in each sub-region is 1.

[0070] In some embodiments, the calculation module 23 calculates the comprehensive information aggregation rate within the sub-region based on the ratio of the number of each specific disaster characteristic section in each sub-region to the total number of sections in the corresponding sub-region, including: for each sub-region, dividing the planned path of the drone in the sub-region into a number of sections of preset lengths, taking the number of sections of the planned path containing each specific disaster characteristic in the sub-region as the number of each specific disaster characteristic section in the sub-region, and taking the total number of sections of the planned path in the sub-region as the total number of sections in the corresponding sub-region.

[0071] The above scheme collects data from the highway disaster site in real time and divides the site into regions. It calculates the comprehensive information aggregation rate based on the ratio of the specific disaster characteristic sections to the total number of sections in each sub-region, and then determines the adjustment amount of the drone path planning in each sub-region based on the aggregation rate, and finally adjusts the drone path planning within the corresponding geographical location range. In this way, based on the data actually collected on site, combined with the characteristics of the sections in different sub-regions, and comprehensively considered from multiple dimensions, it overcomes the defect that traditional path planning only relies on a single data source, so that the planned drone path can better adapt to the complex environment of the highway disaster site, effectively improve the safety of drone flight, and thus improve the efficiency of task execution.

[0072] The above are only implementation methods of the present application, and are not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the protection scope of the present application.

Claims

1. A method for adaptive path planning of unmanned aerial vehicles at a highway disaster site, characterized in that: The method comprises: collecting highway disaster site data in real time; dividing the highway disaster site into regions to obtain a plurality of sub-regions; wherein each sub-region corresponds to a set of data collection information; calculating the comprehensive information aggregation rate in each sub-region based on the ratio of the number of each specific disaster characteristic road section in each sub-region to the total number of road sections in the corresponding sub-region; determining the adjustment amount of the unmanned aerial vehicle path planning in each sub-region according to the comprehensive information aggregation rate of each sub-region; and adjusting the path planning of the unmanned aerial vehicle in the geographical location range corresponding to the sub-region according to the path planning adjustment amount of each sub-region.

2. The method according to claim 1, characterized in that The method calculates the comprehensive information aggregation rate in each sub-region based on the ratio of the number of sections with specific disaster characteristics in each sub-region to the total number of sections in the corresponding sub-region, including: counting the ratio of the number of sections containing specific disaster characteristics in each sub-region to the total number of sections in the corresponding sub-region as the information aggregation rate of the sub-region about the specific disaster characteristics; assigning weights to each specific disaster characteristic in each sub-region, and calculating the comprehensive information aggregation rate of each sub-region.

3. The method according to claim 1, characterized in that: Determining the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area includes: determining the path planning adjustment amount by maximizing the sum of first preset values ​​of all sub-areas through a first optimization algorithm, wherein the first preset value is calculated based on the estimated comprehensive information aggregation rate, path risk assessment value, task completion assessment value and path length after the sub-area newly added path planning adjustment amount, the first preset value is positively correlated with the estimated comprehensive information aggregation rate and the task completion assessment value, and the first preset value is negatively correlated with the path risk assessment value and the path length.

4. The method according to claim 1, characterized in that: Determining the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area includes: determining the path planning adjustment amount by minimizing the sum of second preset values ​​of all sub-areas through a second optimization algorithm, wherein the second preset value is calculated based on the estimated comprehensive information aggregation rate and the proportion of path planning adjustment after the new path planning adjustment of the sub-area, the second preset value is negatively correlated with the estimated comprehensive information aggregation rate, and the second preset value is positively correlated with the proportion of path planning adjustment.

5. The method according to claim 1, characterized in that: The method of determining the UAV path planning adjustment amount for each sub-area based on the comprehensive information aggregation rate of each sub-area includes: comparing the comprehensive information aggregation rate of each sub-area with a preset threshold value; in response to the comprehensive information aggregation rate being less than the preset threshold value, using a preset number as the UAV path planning adjustment amount for the sub-area; in response to the comprehensive information aggregation rate being greater than or equal to the preset threshold value, determining the path planning adjustment amount based on the difference between the preset threshold value and the comprehensive information aggregation rate.

6. The method according to claim 2, characterized in that The step of assigning a weight to each specific disaster feature in each sub-region and calculating the comprehensive information aggregation rate of each sub-region includes: taking the sum of the products obtained by multiplying the information aggregation rate of each specific disaster feature in each sub-region by the corresponding weight as the comprehensive information aggregation rate of the sub-region; wherein the sum of the weight coefficients of the specific disaster features in each sub-region is 1, and the weights of the specific disaster features are all greater than 0.

7. The method according to claim 1 or 2, characterized in that: The method calculates the comprehensive information aggregation rate in each sub-region based on the ratio of the number of sections with specific disaster characteristics in each sub-region to the total number of sections in the corresponding sub-region, including: for each sub-region, dividing the planned path of the drone in the sub-region into a number of sections with preset lengths, taking the number of sections of the planned path containing each specific disaster characteristic in the sub-region as the number of sections with each specific disaster characteristic in the sub-region, and taking the total number of sections of the planned path in the sub-region as the total number of sections in the corresponding sub-region.

8. The method according to claim 3, characterized in that The first preset value is equal to the difference between the first product and the second product and the third product; the first product is equal to the product of the estimated comprehensive information aggregation rate and the first weight coefficient, the second product is equal to the product of the path risk assessment value and the second weight coefficient, the third product is equal to the product of the path length and the third weight coefficient, and the sum of the first weight coefficient, the second weight coefficient and the third weight coefficient is 1, and all are greater than 0.

9. The method according to claim 4, characterized in that The second preset value is equal to the fourth product minus the fifth product, wherein the fourth product is equal to the product of the path planning adjustment ratio and the fourth weight coefficient, and the fifth product is equal to the product of the estimated comprehensive information aggregation rate and the fifth weight coefficient.

10. An adaptive path planning system for unmanned aerial vehicles at highway disaster sites, characterized in that: It includes: Collection module: used to collect highway disaster site data in real time; Partitioning module: used to divide the highway disaster site into regions to obtain multiple sub-regions; wherein each sub-region corresponds to a set of data collection information; calculation module: based on the ratio of the number of specific disaster characteristic sections in each sub-region to the total number of sections in the corresponding sub-region, calculate the comprehensive information aggregation rate in the sub-region; determination module: according to the comprehensive information aggregation rate of each sub-region, determine the UAV path planning adjustment amount of each sub-region; adjustment module: according to the path planning adjustment amount of each sub-region, adjust the path planning of the UAV in the geographical location range corresponding to the sub-region.

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