A path planning method and device, electronic equipment and storage medium

Abstract

The application discloses a path planning method and device, electronic equipment and storage medium, and relates to the field of aviation technology. The method comprises the following steps: determining the cruising condition parameters of an airplane, determining a target cruising height according to the cruising condition parameters, and constructing a fuel cost function and a time cost function based on the cruising condition parameters and the target cruising height; acquiring the starting point, the ending point of an air route and the boundary nodes of each passing region, constructing an air route node network, and determining the position information corresponding to each network node; constructing a navigation cost function and a heuristic function according to the position information and the cruising condition parameters, constructing a total cost function and a regional air segment cost function based on the fuel cost function, the time cost function and the navigation cost function; performing cross-region path search on the air route node network to obtain an initial path across the passing regions; performing internal path search on the passing regions to obtain the internal paths of the passing regions, and determining a target path according to the initial path and the internal paths.

Description

Technical Field

[0001] This invention relates to the field of aviation technology, and in particular to a path planning method, apparatus, electronic device, and storage medium. Background Technology

[0002] Cross-regional route planning in civil aviation is a crucial step in the pre-operation preparation for flight operations, and the planning results directly affect flight operating costs, flight compliance, and on-time efficiency. At present, most route planning schemes in the civil aviation field conduct optimization calculations with a single objective, commonly pursuing the lowest fuel consumption or the shortest flight time. Only a few schemes can take into account some operating costs when solving route problems, which is insufficient to fully meet the actual operational needs of cross-border flights.

[0003] In the actual operation of cross-border flights, route navigation fees need to be paid to various transit areas. The billing rules for navigation fees vary significantly from country to country, with most using linear billing based on the great circle distance between boundary nodes. However, most existing route planning technologies do not incorporate these differentiated route navigation fees into their core cost functions. They generally use a rough method of fixed-segment billing or a uniform conversion of total mileage to estimate navigation fees, which fails to match the actual billing rules of each country. This results in a significant discrepancy between the calculated navigation fees and actual operating expenses, leading to a distortion in the overall cost assessment. Furthermore, existing technologies based on the A-star Algorithm (A... Most of the route planning schemes of the algorithm directly perform integrated optimization calculations on the complete global trajectory, which can easily generate routes that do not comply with the airspace management regulations of various countries. The output planned trajectory cannot be executed in actual flight, and the practicality of the planning results is poor.

[0004] On the other hand, existing solutions mostly treat fuel costs and time costs as independent calculations, failing to establish a coupling relationship between them using a cost index model for aviation operations. This fails to accurately reflect the impact of flight duration on airline operational efficiency, flight connections, and delay losses, resulting in total cost optimization objectives detached from real-world airline operating scenarios. Furthermore, most route planning methods employ fixed cruising altitudes for horizontal route solving, neglecting to consider aircraft cruising weight, cruising speed, and other operational parameters to match the optimal cruising altitude for performance. This disconnect between fuel consumption calculations and actual flight conditions further limits the reference value of cost optimization results. Therefore, there is an urgent need in this field for a route planning solution that can cover multi-dimensional operating costs, adapt to airspace constraints in multiple countries, and closely reflect actual aircraft flight performance, thereby addressing the aforementioned technical deficiencies. Summary of the Invention

[0005] This invention provides a path planning method, apparatus, electronic device, and storage medium to address the incompatibility of existing path planning methods for cross-regional applications.

[0006] According to one aspect of the present invention, a path planning method is provided, wherein the method includes: Determine the aircraft's cruise operating parameters, determine the target cruise altitude based on the cruise operating parameters, and construct a fuel cost function and a time cost function based on the cruise operating parameters and the target cruise altitude; Obtain the aircraft's route start point, route end point, and boundary nodes of each area it passes through; construct a route node network using the route start point, route end point, and each boundary node as network nodes; and determine the location information corresponding to each network node. Based on the location information and the cruise operating parameters, a navigation cost function and a heuristic function are constructed. Based on the fuel cost function, the time cost function, and the navigation cost function, a total cost function and a regional segment cost function are constructed. A cross-regional path search is performed on the route node network. A first cost function is constructed using the total cost function and the heuristic function to perform a selective search and obtain an initial path across the transit regions. An internal path search is performed on the routed areas. The second cost function is constructed by combining the regional segment cost function and the heuristic function to perform a selective search, thereby obtaining the internal paths of each routed area. The target path is determined according to the initial path and the internal paths.

[0007] According to another aspect of the present invention, a path planning apparatus is provided, wherein the apparatus comprises: The altitude determination module is used to determine the aircraft's cruise operating parameters, determine the target cruise altitude based on the cruise operating parameters, and construct a fuel cost function and a time cost function based on the cruise operating parameters and the target cruise altitude. The network construction module is used to obtain the aircraft's route start point, route end point, and boundary nodes of each area it passes through, construct a route node network using the route start point, route end point, and each boundary node as network nodes, and determine the location information corresponding to each network node. The function construction module is used to construct a navigation cost function and a heuristic function based on the location information and the cruise operating parameters, and to construct a total cost function and a regional segment cost function based on the fuel cost function, the time cost function, and the navigation cost function; The path planning module is used to perform cross-regional path search on the route node network. It constructs a first cost function through the total cost function and the heuristic function to perform optimal search and obtain an initial path across the transit areas. The path generation module is used to perform internal path search on the transit areas, combine the regional segment cost function and the heuristic function to form a second cost function for optimal search, obtain the internal path of each transit area, and determine the target path according to the initial path and the internal path.

[0008] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the path planning method according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the path planning method according to any embodiment of the present invention.

[0010] According to another aspect of the present invention, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the path planning method of any embodiment of the present invention.

[0011] The technical solution of this invention involves determining the aircraft's cruise parameters, determining the target cruise altitude based on these parameters, constructing a fuel cost function and a time cost function based on the cruise parameters and the target cruise altitude, obtaining the aircraft's route start point, route end point, and boundary nodes of each transit area, constructing a route node network using the route start point, route end point, and boundary nodes as network nodes, determining the position information corresponding to each network node, constructing a navigation cost function and a heuristic function based on the position information and cruise parameters, and constructing a total cost function and a regional cost function based on the fuel cost function, time cost function, and navigation cost function. The domain segment cost function enables multi-dimensional collaborative optimization of operating costs, improving the accuracy of route cost accounting. By performing cross-regional path search on the route node network, a first cost function is constructed using the total cost function and heuristic function for optimal search, resulting in an initial path across the transit areas. An internal path search is then performed on the transit areas, and a second cost function is constructed using the regional segment cost function and heuristic function for optimal search, resulting in the internal paths of each transit area. The target path is determined according to the initial path and the internal paths, realizing two-layer path planning. This completely solves the problem of conflict between planned routes and airspace rules, improving the adaptability of the routes.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of a path planning method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a path planning method provided according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a path planning device according to Embodiment 4 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the path planning method of this invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] Example 1 Figure 1This is a flowchart of a route planning method according to Embodiment 1 of the present invention. This embodiment is applicable to the situation of route planning for aircraft crossing regions. The method can be executed by a route planning device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Determine the aircraft's cruise operating parameters, determine the target cruise altitude based on the cruise operating parameters, and construct the fuel cost function and time cost function based on the cruise operating parameters and the target cruise altitude.

[0018] Cruise operating parameters can be understood as the operational parameters of an aircraft during stable cruise flight. Generally, cruise operating parameters may include, but are not limited to, parameters such as initial cruise weight, set cruise speed, wind and temperature conditions at the initial cruise point, atmospheric temperature, and atmospheric pressure. The target cruise altitude is the optimal level flight cruise altitude obtained by matching the aircraft's real-time cruise operating parameters. In actual operation, the target cruise altitude is a fixed horizontal planning altitude and serves as the benchmark altitude for fuel consumption calculation and route search. The fuel cost function is a mathematical mapping model that uses the flight segment distance as the independent variable and outputs the corresponding flight segment's fuel consumption cost. The time cost function is a function model constructed based on fuel cost coupled with a cost index. Generally, the time cost function uses the flight segment distance as the independent variable and quantifies the flight operation loss cost corresponding to the flight duration.

[0019] In this embodiment, the initial cruise weight of the aircraft, the standard cruise speed set by the crew, and the wind, temperature, and meteorological parameters of the initial cruise airspace can be collected as initial cruise operation data. The collected cruise operating parameters are input into a preset aircraft performance database, and the altitude performance matching rules for the corresponding aircraft model are retrieved to select the optimal flight altitude under the current cruise operating parameters as the target cruise altitude for this route planning. Based on the target cruise altitude and cruise operating parameters, the fuel consumption per unit distance under the corresponding operating conditions can be matched in the preset aircraft performance database. Combined with the flight distance of the segment to be evaluated and the preset fuel unit price, a fuel cost function is established with flight distance as input and real-time fuel cost as output. The output of the fuel cost function is multiplied by a preset cost index to construct a time cost function with segment flight distance as input and time operating cost as output.

[0020] S120: Obtain the aircraft's route start point, route end point, and boundary nodes of each area it passes through; construct a route node network using the route start point, route end point, and each boundary node as network nodes; and determine the location information corresponding to each network node.

[0021] In this context, the origin of the route refers to the aircraft's starting point, and the destination of the route refers to the aircraft's landing point. The transit area is the airspace control area that the aircraft passes through during its cross-border flight. Generally, each transit area has independent airspace management rules and navigation billing standards. Boundary nodes refer to the compliant entry and exit points on the airspace boundaries of each transit area; all cross-regional flight segments must complete the area switch through boundary nodes. In one embodiment, there can be multiple boundary nodes, meaning that each transit area can include multiple entry and exit points. The route node network refers to a topological network constructed with the origin and destination of the route and the boundary nodes of each area as vertices, and airspace passage rules as constraints, used to determine the order of passage through transit areas. Location information refers to the geographical coordinates of each network node, mainly including longitude and latitude data.

[0022] In this embodiment, the flight's origin and destination routes can be determined based on the flight plan, while all airspace control areas traversed along the route are identified as transit areas. The civil aviation airspace regulations for each transit area are queried, and all compliant entry and exit points are selected as boundary nodes. The origin, destination, and boundary nodes of each transit area are then unified as network nodes. Based on cross-regional travel constraints in various countries, topological connections are established between directly connectable nodes, while connections to cross-regional nodes that do not comply with airspace rules are prohibited, thus completing the construction of the route node network. Finally, the precise geographical longitude and latitude information of each network node is read to obtain the corresponding location information for each network node.

[0023] S130. Construct navigation cost function and heuristic function based on location information and cruise operating parameters, and construct total cost function and regional segment cost function based on fuel cost function, time cost function and navigation cost function.

[0024] The navigation cost function is a cost model adapted to the differentiated billing rules of various transit areas. Generally, the navigation cost function takes the internal great circle distance of the entry and exit boundary nodes within the same area as input and outputs the navigation service fee for the corresponding regional segment. The heuristic function is for A The optimal search provides a guiding function for cost estimation, divided into a cross-regional path search heuristic function (used to estimate the global residual cost from the current node to the route endpoint) and an intra-regional path search heuristic function (used to estimate the local residual cost from the current node to the exit point within the region). The total cost function is a full-dimensional cost model for cross-regional path search, integrating fuel cost, time cost, and navigation cost. The regional segment cost function is a cost model for intra-regional path search, integrating only fuel cost and time cost.

[0025] In this embodiment, preset navigation fee coefficients for each transit area can be retrieved. The internal great circle distance is calculated based on the position information of boundary nodes within the same transit area. Using the internal great circle distance as input and the regional navigation fee coefficient as weight, a segment navigation cost function is constructed. Then, the unit baseline comprehensive cost per unit distance of the aircraft is obtained by querying cruise operating parameters and target cruise altitude. Cross-regional heuristic functions and intra-regional heuristic functions are constructed based on the unit baseline comprehensive cost. The cross-regional heuristic function calculates the external great circle distance using the position information of the current extended node and the route endpoint during cross-regional search, outputting a global estimated cost. The intra-regional heuristic function calculates the intra-regional great circle distance using the position information of the current extended node and the exit point within the region, outputting a local estimated cost. The output values ​​of the constructed fuel cost function, time cost function, and navigation cost function are linearly superimposed to obtain the total cost function. The output values ​​of the fuel cost function and the time cost function are superimposed to obtain the regional segment cost function adapted for intra-regional segment calculation.

[0026] S140. Perform cross-regional path search on the route node network. Construct a first cost function using the total cost function and heuristic function to perform optimal search and obtain the initial path across the transit regions.

[0027] Cross-regional path search refers to the process of selecting the best cross-regional path, used to determine the passage order of flights through different regions and the combination of boundary nodes for each region. The first cost function, generated from the total cost function and a heuristic function, is used to filter the globally optimal cross-regional node path. Generally, the total cost function outputs the actual cumulative cost, while the heuristic function outputs the estimated cost. The initial path refers to the skeleton path output by the cross-regional path search, initially formed by concatenating the route origin, the boundary nodes of each region, and the route destination.

[0028] In this embodiment, the actual cumulative cost value output by the total cost function can be added to the estimated cost value output by the cross-regional heuristic function to obtain the first cost function used for cross-regional optimal search. Using the first cost function as the evaluation criterion, through A... The algorithm traverses the route node network, selects the candidate path with the smallest first cost function, and connects the route start point, the boundary nodes of each region passed through and the route end point arranged in sequence on the candidate path to generate an initial path across the regions passed through.

[0029] S150. Perform internal path search on the transit area, combine the regional segment cost function and the heuristic function to form a second cost function for optimal search, obtain the internal path of each transit area, and determine the target path according to the initial path and the internal path.

[0030] Intra-regional path search refers to refining the internal trajectory between regional boundary nodes for each region traversed by the initial path, thereby achieving local path optimization. The second cost function refers to the selection criterion for intra-regional search, which can be composed of the regional segment cost function and the heuristic function. The target path is the final output of the complete optimal route, formed by concatenating the initial path and the internal paths. In one embodiment, both cross-regional path search and intra-regional path search are performed at the target cruising altitude, using horizontal path planning.

[0031] In this embodiment, the cumulative actual cost value of the region output by the regional segment cost function can be added to the estimated cost value output by the heuristic function within the region to obtain a second cost function adapted for single-region path optimization. Using this second cost function as the evaluation criterion, through A... The algorithm traverses the route node network, selects the internal path with the minimum second cost function, connects the route start point, the boundary nodes of each region passed through, and the route end point arranged in sequence on the candidate path, and splices the initial path and the regional path together in the flight order to obtain the target path.

[0032] In this embodiment of the invention, the aircraft's cruise operating parameters are determined, and the target cruise altitude is determined based on these parameters. A fuel cost function and a time cost function are constructed based on the cruise operating parameters and the target cruise altitude. The aircraft's route start point, route end point, and boundary nodes of each transit area are obtained. A route node network is constructed using the route start point, route end point, and boundary nodes as network nodes. The position information corresponding to each network node is determined. A navigation cost function and a heuristic function are constructed based on the position information and cruise operating parameters. Finally, a total cost function and a regional cost function are constructed based on the fuel cost function, time cost function, and navigation cost function. The segment cost function enables multi-dimensional collaborative optimization of operating costs, improving the accuracy of route cost accounting. By performing cross-regional path search on the route node network, a first cost function is constructed using the total cost function and heuristic function for optimal search, resulting in an initial path across the transit areas. An internal path search is then performed on the transit areas, and a second cost function is constructed using the regional segment cost function and heuristic function for optimal search, resulting in the internal paths of each transit area. The target path is determined according to the initial path and the internal paths, realizing two-layer path planning. This completely solves the problem of conflict between planned routes and airspace rules, improving the adaptability of the routes.

[0033] Example 2 Figure 2 This is a flowchart of a path planning method according to Embodiment 2 of the present invention. This embodiment is a further optimization and extension based on the above embodiments, and can be combined with various optional technical solutions in the above embodiments. Figure 2 As shown, the method includes: S201. Collect the aircraft's initial cruise weight, set cruise speed, and wind and temperature conditions at the initial cruise point as cruise operating parameters, and match the target cruise altitude in the preset aircraft performance database according to the initial cruise weight, set cruise speed, and wind and temperature conditions at the initial cruise point.

[0034] The initial cruise weight refers to the total weight of the aircraft in its initial stable cruise phase, including the fuselage, fuel, passengers, and cargo. The set cruise speed is the standard cruise flight speed pre-set in the flight operation plan. The initial cruise point wind and temperature conditions refer to parameters such as wind speed, temperature, and weather conditions in the initial airspace where the aircraft enters cruise mode. The pre-set aircraft performance database is a professional database that pre-stores altitude performance, fuel consumption performance, and operating condition matching rules for the corresponding aircraft model, including calibration data such as optimal cruise altitude and fuel consumption per unit distance under different weight, speed, and weather conditions.

[0035] In this embodiment, the aircraft's initial cruise weight, the standard cruise speed set by the crew, and wind, temperature, and meteorological data at the initial cruise point can be collected and summarized as cruise operating condition parameters. A preset aircraft performance database is retrieved, and the collected cruise operating condition parameters are used as a matching index to find the flight altitude with the lowest flight drag and highest fuel efficiency for the corresponding aircraft type under the current operating conditions. This flight altitude is then determined as the target cruise altitude for this route planning.

[0036] S202. Based on the cruise operating parameters and target cruise altitude, the fuel consumption per unit distance is obtained by matching in the preset aircraft performance database. Based on the fuel consumption per unit distance, the flight distance of the flight segment to be evaluated, and the preset fuel unit price, a fuel cost function is constructed with flight distance as input and fuel cost as output.

[0037] Here, fuel consumption per unit distance refers to the fuel consumption per unit flight distance of the aircraft under the cruise conditions corresponding to the target cruise altitude. The flight segment to be evaluated refers to the flight segment between any two nodes in the airway network to be calculated. The preset fuel price is the preset market price of aviation fuel for flight operations, and is a fixed value.

[0038] In this embodiment, defined cruise parameters and target cruise altitude can be used as search criteria to match and query the corresponding fuel consumption parameters per unit distance for the aircraft in a preset aircraft performance database. For any segment to be evaluated within the route, the flight distance of that segment is obtained. Combined with a preset fuel price, a standardized fuel cost function is constructed using the formula: Fuel Cost = Fuel Consumption per Unit Distance × Flight Distance of Segment × Fuel Price per Unit. The fuel cost function takes the flight distance of the segment as the input variable and outputs the fuel cost corresponding to any segment. In one embodiment, the fuel cost function can be Cfuel = Qtotal × Pfuel; where Qtotal is the total fuel consumption of the segment, calculated from the initial cruise weight, cruise speed, target cruise altitude, and wind and temperature conditions combined with the preset aircraft performance database; Pfuel is the preset fuel price per unit. Generally, Qtotal can be calculated by retrieving the performance manual of the corresponding aircraft model, inputting the current cruise altitude (target cruise altitude), initial cruise weight, and set cruise speed, and correcting aerodynamic parameters based on real-time wind and temperature conditions, calculating the fuel consumption per unit flight distance, and then multiplying it by the flight distance to obtain the total fuel consumption. The total fuel consumption is obtained by summing the fuel consumption of each segment.

[0039] S203. Multiply the output of the fuel cost function with the preset cost index to obtain the time cost function with the flight distance of the flight segment as input and the time cost as output.

[0040] Among them, the preset cost index refers to the weight that represents the comprehensive operational loss value corresponding to the flight duration.

[0041] In one embodiment, the output of the fuel cost function can be coupled and multiplied with a preset cost index to calculate the time cost function. In one embodiment, the time cost function can be C_time = C_fuel × CI; where CI is a preset cost index (which can be a parameter set by the airline based on operational efficiency and flight connection requirements), reflecting the weight of time cost relative to fuel cost.

[0042] S204. Determine the aircraft's route start point, route end point, and transit areas, and extract the departure and entry points of each transit area as boundary nodes.

[0043] The entry point refers to the compliant boundary node where the aircraft enters the corresponding airspace of the transit area, and is the starting node for path searching within the area. The departure point is the compliant boundary node where the aircraft leaves the corresponding airspace of the transit area, and is the ending node for path searching within a single area.

[0044] In this embodiment, the flight path origin and destination can be determined based on the flight plan, and all airspace control areas traversed throughout the flight can be identified. For each traversed area, all compliant airspace entry and exit points are selected and used as boundary nodes of the traversed area.

[0045] S205. Take the starting point, ending point, and boundary nodes of each area along the route as network nodes, and establish connection edges between each network node according to the preset route passage constraints between each network node to obtain the route node network.

[0046] Among them, the preset flight segment passage constraints can be understood as the airspace cross-regional direct flight control rules of various countries, used to constrain the passage relationships between boundary nodes of different regions. Connecting edges refer to the topological relationship between two network nodes that allow direct passage.

[0047] In this embodiment, the route start point, route end point, and boundary nodes of all traversed areas can be uniformly considered as network nodes. Based on preset route passage constraints, it is determined whether any two network nodes in different areas are eligible for direct flight passage. For network node pairs that meet the preset route passage constraints, topological connection edges are established to form a route node network.

[0048] S206. Read the geographic coordinates corresponding to each network node as the location information of each network node.

[0049] The location information includes the longitude and latitude information of the network nodes.

[0050] In this embodiment, the longitude and latitude information of all network nodes can be retrieved and used as the location information of the network nodes.

[0051] S207. Combining the preset navigation fee coefficients of each route area, construct a navigation cost function with the internal great circle distance as input and the route segment navigation cost as output.

[0052] The internal great circle distance is determined by the location information of boundary nodes belonging to the same route area. A preset navigation fee coefficient is used, specifying the navigation fee coefficient for each route area.

[0053] In this embodiment, preset navigation fee coefficients for each transit area can be retrieved. For each transit area, the great circle distance is calculated using the latitude and longitude location information of the entry and exit points within that area, and this great circle distance is used as the great circle distance to the interior of the area. A navigation cost function is constructed using the interior great circle distance as the input variable and the area navigation fee coefficient as the weight. In one embodiment, the navigation cost function can be... Where K_i is the preset navigation fee coefficient (i.e., the navigation fee linear coefficient, which can be published by the civil aviation authorities of each country) for the i-th transit area, and D_i is the great circle distance between the entry point and the exit point for the i-th transit area. This is a summation operation performed while traversing the regions traversed by the flight path.

[0054] S208. By querying the corresponding unit distance benchmark comprehensive cost through cruise operating parameters and target cruise altitude, and combining the unit distance benchmark comprehensive cost, construct a function with the great circle distance outside the region as input and the estimated cost as output as a heuristic function for performing cross-regional path search.

[0055] The outer great circle distance is calculated from the position information of the current extended node and the route endpoint during cross-regional path search. The benchmark comprehensive cost per unit distance is the benchmark comprehensive operating cost per unit flight distance of the aircraft under the current cruise conditions and target cruise altitude; it serves as the benchmark parameter for heuristic cost estimation. The current extended node refers to the node selected in each iteration of the cross-regional path search for expanding downstream successor nodes. The cross-regional path search heuristic function is the cost estimation function used in the cross-regional path optimization process to estimate the remaining comprehensive cost from the current cross-regional node to the route endpoint.

[0056] In this embodiment, the corresponding unit distance baseline comprehensive cost can be obtained by matching and querying a preset aircraft performance database based on cruise operating parameters and target cruise altitude. During the cross-regional path search process, the latitude and longitude position information of the current extended node and the global route endpoint in each iteration is obtained in real time, and the outer great circle distance between the two is calculated. Using the outer great circle distance as input and the unit distance baseline comprehensive cost as the conversion weight, a cross-regional heuristic function is constructed.

[0057] S209. Combining the unit distance benchmark comprehensive cost, construct a function with the great circle distance within the region as input and the estimated cost as output as a heuristic function for performing path search within the region.

[0058] The great circle distance within the region is calculated using the position information of the current processing node and the departure point of the current route region during the internal path search. The current processing node is the node used to expand the downstream nodes within the region in each iteration of the internal path search. The heuristic function of the internal path search refers to the estimated cost function for path optimization within each region, used to estimate the remaining flight cost from the current node within the region to the departure point of the region.

[0059] In this embodiment, the unit distance benchmark cost can be used as a conversion parameter. For a path search scenario within a single transit area, the latitude and longitude location information of the current processing node and the departure point within the area can be obtained in real time to calculate the great circle distance within the area. Using the great circle distance within the area as input and the unit distance benchmark cost as weight, a heuristic function specific to the area can be constructed.

[0060] S210. The total cost function is obtained by adding the fuel cost function, time cost function and navigation cost function. The regional segment cost function is obtained by adding the fuel cost function and time cost function.

[0061] In this embodiment, the output values ​​of the fuel cost function, time cost function, and navigation cost function can be linearly superimposed to obtain the total cost function. Furthermore, the output values ​​of the fuel cost function and the time cost function can be superimposed to obtain the regional segment cost function.

[0062] S211. Construct the first cost function by adding the first cumulative actual cost value output by the total cost function to the first estimated cost value output by the heuristic function of cross-regional path search.

[0063] The first cumulative actual cost value is calculated using the total cost function, representing the true cumulative comprehensive operating cost from the route origin to the current node along the route. The first estimated cost value is the estimated remaining cost from the current node to the route destination, output by the cross-regional heuristic function.

[0064] In this embodiment, during the cross-regional path search process, the first cumulative actual cost value from the route origin to the current network node can be calculated in real time using the total cost function, while the first estimated cost value from the current node to the route destination can be calculated using the cross-regional heuristic function. The actual cumulative cost value and the estimated cost value are added together to construct the first cost function.

[0065] S212. Take the route starting point as the current extended node, determine the successor network nodes of the current extended node according to the route node network, determine the first cost function value corresponding to each successor network node through the first cost function, set the parent node of the successor network node as the current extended node, and record the cross-regional parent node association relationship.

[0066] In this context, a successor network node is a downstream network node that has a topological connection to the current expansion node and can be directly accessed. The first cost function value refers to the comprehensive cost value corresponding to each successor network node. The cross-regional parent node association is used to record the correspondence between each successor node and the upstream current expansion node in the cross-regional search.

[0067] In this embodiment, the global route starting point can be set as the initial current extended node. Based on the topological connection relationship of the route node network, all compliant successor network nodes of the current extended node are identified through traversal. The first cost function value corresponding to each successor network node is calculated one by one using the first cost function. At the same time, an upstream association relationship is bound to each successor network node, and its parent and child nodes are set as the current extended node. The parent node association mapping data across the entire cross-regional route is recorded synchronously and completely.

[0068] S213. Select the successor network node with the minimum value of the first cost function as the new current expansion node, and continue to iterate until the destination of the route is found. The initial path is obtained by backtracking through the parent node association relationship across regions, which is composed of the route starting point, the boundary nodes of each region passed through, and the route destination.

[0069] In this embodiment, among all the successor network nodes of the current expanded node, the network node with the smallest first cost function value can be selected, and this network node can be updated as the current expanded node for the new round of iteration. The iterative process of node expansion, cost calculation, and association record recording is executed repeatedly until the current expanded node finds a matching global route endpoint. After the search is completed, the node links are traced back in reverse based on the recorded cross-regional parent node associations, and the route starting point, the optimal boundary nodes of each transit area, and the route endpoint are sequentially connected to generate the initial path.

[0070] S214. Construct a second cost function by adding the cumulative actual cost value of the region output by the regional segment cost function to the second estimated cost value output by the heuristic function of the internal path search of the region.

[0071] The regional cumulative actual cost is the total cost of fuel and time from the entry point to the current node within the region, calculated using the regional segment cost function. The second estimated cost refers to the estimated remaining cost from the current node to the departure point within the region, output by the regional heuristic function.

[0072] In this embodiment, for each transit area, the cumulative actual cost value from the entry point of the area to the node in the current area can be calculated using the regional segment cost function, and the second estimated cost value from the current node to the departure point of the area can be calculated using the regional internal heuristic function. The sum of the cumulative actual cost value and the second estimated cost value is used as the second cost function.

[0073] S215. Take the entry point of the corresponding route area as the current processing node, determine the successor network node of the current processing node in this area, determine the second cost function value corresponding to each successor network node through the second cost function, set the parent node of the successor network node as the current processing node, and record the parent node association relationship in the area.

[0074] In this embodiment, for each region traversed by the initial path, the entry point of the traversed region is initialized as the current processing node for the current round of internal search. All traversable successor network nodes within the airspace of the current processing node are traversed, and the second cost function value of each successor node is calculated one by one using the second cost function. Parent node relationships are bound to all successor nodes within each region, their upstream nodes are set as the current processing node, and the independent parent node relationships within each region are recorded synchronously.

[0075] S216. Select the successor network node with the smallest second cost function value as the new current processing node, and continue iterating until the departure point of the route area is found. The internal path of the route area is obtained by backtracking through the parent node association relationship in the area.

[0076] In this embodiment, the node with the smallest second cost function value among all successor network nodes in the current region can be selected and updated as the new current processing node. The node expansion, cost calculation, and association record operations within the region are continuously iterated until the current processing node retrieves a matching exit point for the transit region. After completing the single-region search, the internal paths of the transit regions are generated by backtracking based on the independent parent node relationships within the region. The above operations are performed on all transit regions one by one to obtain the internal paths of all regions.

[0077] S217. Concatenate the internal paths corresponding to each area along the route in sequence, and combine them with the initial path to obtain the target path.

[0078] In this embodiment, the passage order of the areas traversed by the initial path is used as the overall framework. The internal paths are then used to replace the path segments from the entry point to the exit point of each area in the initial path. The internal paths of all areas are then sequentially spliced ​​and merged according to the flight sequence to obtain the target path.

[0079] This invention, in its embodiments, matches the target cruise altitude from an aircraft performance database based on initial cruise weight, cruise speed, and wind and temperature conditions, eliminating the need for fixed, empirically-based altitude planning. Based on this, a fuel cost function and a time cost function coupled with a cost index are constructed, deeply binding actual aircraft flight performance with route cost calculations. This reduces fuel consumption and duration calculation deviations caused by unreasonable altitude settings, making cost outputs more closely reflect actual flight operations and enhancing the engineering practicality of the optimized results. Independent navigation fee coefficients are set for different transit areas, and a navigation cost function is constructed based on the internal great circle distance of boundary nodes within the same area, simulating the linear billing rules of navigation fees in various countries, avoiding cost distortion caused by the rough estimation of navigation fees in traditional schemes. A dual-layer A The search architecture decouples macro-level framework planning from regional internal route refinement, balancing route compliance and solution quality. It constructs a route node network containing only boundary nodes. The first layer of cross-regional path search outputs an initial path connected by boundary nodes, prioritizing the determination of the passage order through the transit areas and the entry and exit points for each area. Then, using this set of boundary points as constraints, it performs internal path searches layer by layer for each transit area. It separates national airspace boundary constraints from domestic route network constraints, avoiding the potential for invalid routes that violate the airspace management rules of transit countries through direct global optimization, thus improving the actual executability of the output routes.

[0080] Example 3 In one embodiment, this embodiment is a further explanation of a path planning method based on the above embodiments. Step 1: Construction of the core cost function. The total cost function is defined as the sum of three items: route navigation fee, fuel cost, and time cost, i.e.: Ctotal = Cnavigation + Cfuel + Ctime.

[0081] The route navigation fee (C-navigation) adopts differentiated linear billing rules for each country, using a single transit area as the accounting unit. It calculates the great circle distance between the entry and exit points of each transit area, and the navigation fee is a linear function of this great circle distance. The navigation cost function can be... Where, K_i is the preset navigation fee coefficient for the i-th transit area (i.e., the linear coefficient of the navigation fee, which can be published by the civil aviation authorities of each country), and D_i is the great circle distance between the entry point and the exit point of the i-th transit area. This is a summation operation performed while traversing the regions traversed by the flight path.

[0082] Fuel cost (Cfuel) is precisely calculated based on actual aircraft operating parameters and performance data. The fuel cost function can be Cfuel = Qtotal × Pfuel, where Qtotal is the total fuel consumption for a flight segment, calculated from the initial cruise weight, cruise speed, target cruise altitude, and wind and temperature conditions, combined with a preset aircraft performance database; Pfuel is the preset fuel price per unit. The total fuel consumption calculation logic is as follows: Qtotal can be calculated by retrieving the performance manual of the corresponding aircraft model, inputting the current cruise altitude (target cruise altitude), initial cruise weight, and set cruise speed, and adjusting aerodynamic parameters based on real-time wind and temperature conditions, calculating the fuel consumption per unit flight distance, and then multiplying it by the flight distance to obtain the total fuel consumption. The total fuel consumption is obtained by summing the fuel consumption of each flight segment.

[0083] Time cost (C-hour) is calculated by coupling fuel cost and flight time using a cost index. The time cost function can be C-hour = C-fuel × CI. Here, CI is a preset cost index (which can be a parameter set by the airline based on operational efficiency and flight connection requirements), reflecting the weight of time cost relative to fuel cost.

[0084] Step 2, First layer A Algorithm: Macro-boundary node route network planning.

[0085] A global flight segment network is constructed using national border nodes as nodes. The core objective is to minimize the total cost (C-guide + C-fuel + C-time). The steps are as follows: extract the legal border nodes of the flight origin and destination and all areas traversed, construct the route node network, and label the latitude and longitude of each network node, the area it traverses, and the navigation fee coefficient K_i of the traversed area. Based on the aircraft's initial cruise weight, set cruise speed, average wind and temperature conditions of the flight segment, and query the aircraft performance database, determine the optimal cruise altitude (target cruise altitude).

[0086] A Algorithm cost function setting: A is the total cost between nodes (navigation fee + fuel cost + time cost). The core cost function of the algorithm, the heuristic function, uses the estimated total cost corresponding to the distance between the start and end points of the great circle to avoid local optima. This is achieved through A... The algorithm traverses the node network and selects the macroscopic route with the minimum total cost. This route is composed of consecutive boundary nodes of the passing areas, thus determining the sequence of passing areas and boundary nodes.

[0087] Step 3, Second Layer A Algorithm: Micro-level domestic trajectory refinement planning.

[0088] For the adjacent boundary nodes obtained from the first-level planning, and in conjunction with official flight segment network structure data released by various countries, micro-level flight path optimization within the country is carried out. The core objective is to minimize fuel cost + time cost. The steps are as follows: retrieve data on the domestic legal flight segment network, airspace restricted areas, and navigation beacon locations released by the civil aviation authorities of the corresponding transit areas, construct refined domestic flight path nodes, eliminate navigation fee factors between transit areas, and use fuel cost + time cost as A. The algorithm's cost function, using the great circle distance between boundary nodes as a heuristic function to estimate the cost, is determined at a fixed optimal cruising altitude via A. The algorithm completes compliant horizontal trajectory planning between two boundary nodes, avoids airspace restrictions, ensures that the trajectory complies with the country's airspace management requirements, and stitches together all the refined trajectories within the passing area to form a complete, compliant, and cost-optimal final route.

[0089] In one embodiment, taking a flight that takes off from an airport in country A, passes through countries B and C, and arrives at an airport in country D as an example, the route originates in country A, ends in country D, passes through countries B and D, uses the optimal fixed cruising altitude as the target cruising altitude, uses the global node network as the route node network, and uses the domestic flight path as the internal path, further illustrating a route planning method.

[0090] Step 1: Determine the origin and destination latitude and longitude of the flight, the initial cruise weight of the aircraft, the cruise cost index, and the wind and temperature conditions at the starting cruise point. Query the optimal fixed cruise altitude H through the preset aircraft performance database. Collect the preset navigation fee coefficients K_B, K_C, and K_D for countries B, C, and D, respectively, as well as real-time wind and temperature data, preset fuel price P, and preset cost index.

[0091] Step 2: Extract all legal border nodes of countries B, C, and D, construct a global node network, calculate the navigation fee, fuel cost, and time cost between any two network nodes, and generate a total cost matrix; start A. The algorithm uses the total cost between nodes (navigation fee + fuel cost + time cost) as A. The core cost function of the algorithm, the heuristic function, uses the estimated total cost corresponding to the great circle distance between the start and end points to avoid local optima. That is, with the goal of minimizing the total cost, it plans a macroscopic route from the start point in country A → the border node in country B → the border node in country C → the entry point in country D → the destination, determining the sequence of border nodes for each country.

[0092] Step 3: For border nodes within country B, import network data for flight segments within country B, using fuel cost + time cost as A. The algorithm's cost function, using the heuristic function, estimates the cost by estimating the great circle distance between boundary nodes, aiming to minimize the cost through A. The algorithm plans domestic flight paths. Similarly, it plans domestic flight paths for countries C and D, and then stitches together all domestic flight paths to form a complete air route.

[0093] Step 4: Cost accounting and verification.

[0094] Calculate the total navigation cost, fuel cost, and time cost of the final route, confirm that the sum of the three costs is the optimal value, and verify whether the route complies with the airspace rules of various countries to ensure that it is feasible in practice.

[0095] In this embodiment, only horizontal path planning is performed throughout the entire process. The cruising altitude is fixed at the optimal performance altitude corresponding to the aircraft's current weight and cruising speed, without involving vertical profile optimization. All cost calculation parameters use real-time operational data (wind temperature, fuel price per unit) and officially released data (navigation fee coefficient, route network) to ensure calculation accuracy; Double-layer A The algorithm iterates independently. The first layer determines the macro-level path framework, and the second layer refines the compliant trajectory, balancing cost optimization and operational compliance.

[0096] Example 4 Figure 3 This is a schematic diagram of a path planning device according to Embodiment 4 of the present invention. Figure 3 As shown, the device includes: a height determination module 31, a network construction module 32, a function construction module 33, a path planning module 34, and a path generation module 35.

[0097] Among them, the altitude determination module 31 is used to determine the aircraft's cruise operating parameters, determine the target cruise altitude based on the cruise operating parameters, and construct the fuel cost function and time cost function based on the cruise operating parameters and the target cruise altitude.

[0098] The network construction module 32 is used to obtain the aircraft's route start point, route end point, and boundary nodes of each area it passes through, and to construct a route node network using the route start point, route end point, and each boundary node as network nodes, and to determine the location information corresponding to each network node.

[0099] The function construction module 33 is used to construct navigation cost function and heuristic function based on location information and cruise condition parameters, and to construct total cost function and regional segment cost function based on fuel cost function, time cost function and navigation cost function.

[0100] The path planning module 34 is used to perform cross-regional path search on the route node network. It constructs a first cost function through the total cost function and the heuristic function to perform the optimal search and obtain the initial path across the route regions.

[0101] The path generation module 35 is used to perform internal path search on the transit area, combine the regional segment cost function and the heuristic function to form a second cost function for optimal search, obtain the internal path of each transit area, and determine the target path according to the initial path and the internal path.

[0102] The technical solution of this invention involves determining the aircraft's cruise parameters through an altitude determination module, determining the target cruise altitude based on the cruise parameters, and constructing a fuel cost function and a time cost function based on the cruise parameters and the target cruise altitude. A network construction module obtains the aircraft's route start point, route end point, and boundary nodes of each transit area, constructs a route node network using the route start point, route end point, and boundary nodes as network nodes, and determines the position information corresponding to each network node. A function construction module 33 constructs a navigation cost function and a heuristic function based on the position information and cruise parameters, and constructs the total cost based on the fuel cost function, time cost function, and navigation cost function. The system employs a multi-dimensional collaborative optimization of operational costs through functions and regional segment cost functions, improving the accuracy of route cost accounting. The route planning module performs cross-regional route searches on the route node network, constructing a first cost function using the total cost function and heuristic function for optimal search, yielding initial routes across transit areas. The route generation module then performs internal route searches within the transit areas, combining the regional segment cost function and heuristic function to construct a second cost function for optimal search, obtaining internal routes for each transit area. The target route is determined based on the initial and internal routes, achieving two-layer route planning. This completely resolves the conflict between planned routes and airspace rules, improving route adaptability.

[0103] In one embodiment, the height determination module 31 includes: The altitude determination unit is used to collect the aircraft's initial cruise weight, set cruise speed, and wind and temperature conditions at the initial cruise point as cruise operating parameters, and to match the target cruise altitude in the preset aircraft performance database according to the initial cruise weight, set cruise speed, and wind and temperature conditions at the initial cruise point. The fuel cost construction unit is used to match the fuel consumption per unit distance in the preset aircraft performance database according to the cruise condition parameters and target cruise altitude. Based on the fuel consumption per unit distance, the flight distance of the flight segment to be evaluated, and the preset fuel unit price, a fuel cost function is constructed with the flight distance as input and the fuel cost as output. The time cost construction unit is used to multiply the output of the fuel cost function with a preset cost index to obtain a time cost function with the flight distance of the flight segment as input and the time cost as output.

[0104] In one embodiment, the network construction module 32 includes: The node determination unit is used to determine the aircraft's route start point, route end point, and transit areas, and extract the departure and entry points of each transit area as boundary nodes. The network construction unit is used to take the starting point, ending point and boundary nodes of each area along the route as network nodes, and establish the connection edges between each network node according to the preset route passage constraints, so as to obtain the route node network. The location determination unit is used to read the geographic coordinates corresponding to each network node as the location information of each network node; wherein, the location information includes the longitude and latitude information of the network node.

[0105] In one embodiment, the function construction module 33 includes: The navigation cost construction unit is used to construct a navigation cost function with the internal great circle distance as input and output as the navigation cost of the flight segment, by combining the preset navigation fee coefficients of each route area; wherein, the internal great circle distance is determined by the position information of the boundary nodes belonging to the same route area; The first function construction unit is used to query the corresponding unit distance benchmark comprehensive cost through cruise operating condition parameters and target cruise altitude. Combined with the unit distance benchmark comprehensive cost, a function is constructed with the outer great circle distance as input and the estimated cost as output as a heuristic function for performing cross-regional path search. The outer great circle distance is calculated from the position information of the current extended node and the route endpoint when performing cross-regional path search. The second function construction unit is used to combine the unit distance benchmark comprehensive cost to construct a function with the great circle distance within the region as input and the estimated cost as output as a heuristic function for performing internal path search within the region; wherein, the great circle distance within the region is calculated from the position information of the current processing node and the departure point of the current route region when performing internal path search; The total cost construction unit is used to add the fuel cost function, time cost function, and navigation cost function to obtain the total cost function, and to add the fuel cost function and time cost function to obtain the regional segment cost function.

[0106] In one embodiment, the path planning module 34 includes: The first cost function construction unit is used to add the first cumulative actual cost value output by the total cost function to the first estimated cost value output by the heuristic function of cross-regional path search to construct the first cost function; The first expansion unit is used to take the route origin as the current expansion node, determine the successor network node of the current expansion node according to the route node network, determine the first cost function value corresponding to each successor network node through the first cost function, set the parent node of the successor network node as the current expansion node, and record the cross-regional parent node association relationship. The path planning unit is used to select the successor network node with the minimum value of the first cost function as the new current expansion node, and iterates continuously until the destination of the route is found. The initial path is obtained by backtracking through the parent node association relationship across regions, which is composed of the route starting point, the boundary nodes of each region passed through, and the route destination.

[0107] In one embodiment, the path generation module 35 includes: The second cost function construction unit is used to add the cumulative actual cost value of the region output by the regional segment cost function to the second estimated cost value output by the heuristic function of the internal path search of the region to construct the second cost function; The second extension unit is used to take the entry point of the corresponding route area as the current processing node, determine the successor network node of the current processing node in this area, determine the second cost function value corresponding to each successor network node through the second cost function, set the parent node of the successor network node as the current processing node, and record the parent node association relationship in the area. The path query unit is used to select the successor network node with the smallest second cost function value as the new current processing node, and iterates continuously until the departure point of the route area is found. The internal path of the route area is obtained by backtracking through the parent node association relationship within the area. The path generation unit is used to sequentially concatenate the internal paths corresponding to each transit area and combine them with the initial path to obtain the target path.

[0108] In one embodiment, both cross-regional path search and internal path search are performed at the target cruising altitude, with horizontal path planning.

[0109] The path planning device provided in the embodiments of the present invention can execute the path planning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0110] Example 5 Figure 4This is a schematic diagram of the structure of an electronic device implementing the path planning method of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0111] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0112] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0113] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as path planning.

[0114] In some embodiments, path planning may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the path planning described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform path planning by any other suitable means (e.g., by means of firmware).

[0115] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0116] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0117] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0118] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0119] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0120] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0121] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements path planning according to any embodiment of the present invention.

[0122] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0124] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A path planning method characterized by, include: Determine the aircraft's cruise operating parameters, determine the target cruise altitude based on the cruise operating parameters, and construct a fuel cost function and a time cost function based on the cruise operating parameters and the target cruise altitude; Obtain the aircraft's route start point, route end point, and boundary nodes of each area it passes through; construct a route node network using the route start point, route end point, and each boundary node as network nodes; and determine the location information corresponding to each network node. Based on the location information and the cruise operating parameters, a navigation cost function and a heuristic function are constructed. Based on the fuel cost function, the time cost function, and the navigation cost function, a total cost function and a regional segment cost function are constructed. A cross-regional path search is performed on the route node network. A first cost function is constructed using the total cost function and the heuristic function to perform a selective search and obtain an initial path across the transit regions. An internal path search is performed on the routed areas. The second cost function is constructed by combining the regional segment cost function and the heuristic function to perform a selective search, thereby obtaining the internal paths of each routed area. The target path is determined according to the initial path and the internal paths.

2. The method according to claim 1, characterized in that, The process of determining the aircraft's cruise operating parameters, determining the target cruise altitude based on the cruise operating parameters, and constructing a fuel cost function and a time cost function based on the cruise operating parameters and the target cruise altitude includes: The initial cruise weight, set cruise speed, and wind and temperature conditions at the initial cruise point of the aircraft are collected as cruise operating parameters. The target cruise altitude is matched in the preset aircraft performance database according to the initial cruise weight, the set cruise speed, and the wind and temperature conditions at the initial cruise point. According to the cruise operating parameters and the target cruise altitude, the fuel consumption per unit distance is obtained by matching the preset aircraft performance database. Based on the fuel consumption per unit distance, the flight distance of the flight segment to be evaluated, and the preset fuel unit price, a fuel cost function is constructed with the flight distance as input and the fuel cost as output. Multiplying the output of the fuel cost function by a preset cost index yields a time cost function with the flight segment distance as input and time cost as output.

3. The method according to claim 1, characterized in that, The process of obtaining the aircraft's route start point, route end point, and boundary nodes of each area traversed, constructing a route node network using the route start point, route end point, and each boundary node as network nodes, and determining the location information corresponding to each network node includes: The origin, destination, and transit areas of the aircraft's route are determined, and the departure and entry points of each transit area are extracted as boundary nodes. The route start point, the route end point, and the boundary nodes of each of the routes are taken as network nodes. Based on the preset route passage constraints between each network node, connection edges are established between each network node to obtain the route node network. The geographic coordinates corresponding to each network node are read as the location information of each network node; wherein, the location information includes the longitude and latitude information of the network node.

4. The method according to claim 1, characterized in that, The step of constructing a navigation cost function and a heuristic function based on the location information and the cruise operating parameters, and constructing a total cost function and a regional segment cost function based on the fuel cost function, the time cost function, and the navigation cost function, includes: By combining the preset navigation fee coefficients of each of the aforementioned transit areas, a navigation cost function is constructed, which takes the internal great circle distance as input and outputs the segment navigation cost; wherein, the internal great circle distance is determined by the position information of the boundary nodes belonging to the same transit area; By querying the corresponding unit distance benchmark comprehensive cost using the cruise operating parameters and the target cruise altitude, and combining the unit distance benchmark comprehensive cost, a function is constructed with the outer great circle distance as input and the estimated cost as output as a heuristic function for performing cross-regional path search; wherein, the outer great circle distance is calculated from the position information of the current extended node and the route endpoint when performing cross-regional path search; Based on the aforementioned unit distance benchmark comprehensive cost, a function is constructed using the great circle distance within the region as input and the estimated cost as output as a heuristic function for performing internal path search within the region; wherein, the great circle distance within the region is calculated from the position information of the current processing node and the departure point of the current route region when performing internal path search; The total cost function is obtained by adding the fuel cost function, the time cost function, and the navigation cost function together. The regional segment cost function is obtained by adding the fuel cost function and the time cost function together.

5. The method according to claim 1, characterized in that, The step of performing cross-regional path search on the route node network, and constructing a first cost function using the total cost function and the heuristic function for optimal search, to obtain an initial path across the transit regions, includes: The first cost function is constructed by adding the first cumulative actual cost value output by the total cost function to the first estimated cost value output by the heuristic function of cross-regional path search. The starting point of the route is taken as the current extended node. The successor network nodes of the current extended node are determined according to the route node network. The first cost function value corresponding to each successor network node is determined by the first cost function. The parent node of the successor network node is set as the current extended node. The cross-regional parent node association relationship is recorded. The successor network node with the minimum value of the first cost function is selected as the new current expansion node. The process is iterated until the destination of the route is found. The initial path, which is composed of the route starting point, the boundary nodes of each region passed through, and the route destination, is obtained by backtracking through the cross-regional parent node association relationship.

6. The method according to claim 1, characterized in that, The process of performing an internal path search on the transit areas, combining the regional segment cost function and the heuristic function to form a second cost function for optimal search, obtaining the internal paths for each transit area, and determining the target path according to the initial path and the internal paths includes: The second cost function is constructed by adding the cumulative actual cost value of the region output by the regional segment cost function to the second estimated cost value output by the heuristic function of the internal path search of the region. The entry point of the corresponding route area is taken as the current processing node. The successor network nodes of the current processing node in this area are determined. The second cost function value corresponding to each successor network node is determined by the second cost function. The parent node of the successor network node is set as the current processing node. The parent node association relationship in the area is recorded. The successor network node with the smallest second cost function value is selected as the new current processing node. The process continues to iterate until the exit point of the route region is found. The internal path of the route region is obtained by backtracking through the parent node association relationship within the region. The internal paths corresponding to each area are sequentially pieced together, and combined with the initial path to obtain the target path.

7. The method according to claim 1, characterized in that, Both the cross-regional path search and the internal path search are performed at the target cruising altitude, with horizontal path planning.

8. A path planning device, characterized in that, include: The altitude determination module is used to determine the aircraft's cruise operating parameters, determine the target cruise altitude based on the cruise operating parameters, and construct a fuel cost function and a time cost function based on the cruise operating parameters and the target cruise altitude. The network construction module is used to obtain the aircraft's route start point, route end point, and boundary nodes of each area it passes through, construct a route node network using the route start point, route end point, and each boundary node as network nodes, and determine the location information corresponding to each network node. The function construction module is used to construct a navigation cost function and a heuristic function based on the location information and the cruise operating parameters, and to construct a total cost function and a regional segment cost function based on the fuel cost function, the time cost function, and the navigation cost function; The path planning module is used to perform cross-regional path search on the route node network. It constructs a first cost function through the total cost function and the heuristic function to perform optimal search and obtain an initial path across the transit areas. The path generation module is used to perform internal path search on the transit areas, combine the regional segment cost function and the heuristic function to form a second cost function for optimal search, obtain the internal path of each transit area, and determine the target path according to the initial path and the internal path.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the path planning method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the path planning method according to any one of claims 1-7.

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