A method, system, device, and medium for screening areas suitable for low altitude logistics layout

Abstract

The application provides a method, system, device and medium for screening a region suitable for low-altitude logistics layout, relates to the fields of low-altitude economy and geographic information, and comprises the following steps: acquiring navigation electronic map data, DEM elevation data and administrative division data, calculating the average altitude of each township based on the DEM elevation data and the administrative division data of the whole country to generate a preliminary screening region; calculating the elevation characteristics and slope characteristics of each road in the preliminary screening region by using the DEM elevation data to form a road elevation-slope characteristic data set; calculating the road bypassing coefficient, the number of sharp bends and the convenient accessibility index in the preliminary screening region based on the road elevation-slope characteristic data set and the navigation electronic map data, and constructing a road traffic obstacle index set; and evaluating the road traffic obstacle index set by using the analytic hierarchy process, and screening a region suitable for low-altitude logistics layout according to the evaluation result.

Description

Technical Field

[0001] This invention relates to the field of low-altitude economics and geographic information technology, specifically to a method, system, equipment, and medium for screening areas suitable for low-altitude logistics layout. Background Technology

[0002] In the agricultural product distribution channels, drones can overcome the time and space limitations of traditional transportation, enabling direct connections from fields to distribution centers. In emergency material delivery scenarios, their rapid response capabilities buy critical time windows for disaster relief. However, the complex and diverse terrain of mountainous areas, coupled with the interaction between road networks and geological conditions, has led to long-term challenges in some townships, including high logistics costs, poor timeliness, and insufficient service coverage. The construction of low-altitude logistics networks has become a key path to solving the "last mile" delivery problem. However, existing methods for assessing accessibility often focus on a single dimension, such as using isolated indicators like road mileage or terrain slope. This makes it difficult to comprehensively depict the combined traffic resistance under the complex terrain and road network of mountainous areas, resulting in biases in the identification of low-altitude logistics demand. Summary of the Invention

[0003] This invention addresses the problems existing in the prior art by providing a method, system, equipment, and medium for screening areas suitable for low-altitude logistics layout, and accurately locating township areas suitable for low-altitude logistics layout.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] Acquire navigation electronic map data, DEM elevation data, and administrative division data. Calculate the average elevation of each township based on the national DEM elevation data and administrative division data. Filter out areas where the proportion of townships with average elevations within a preset range exceeds a preset threshold, generating preliminary filtered areas. In the initially screened areas, the elevation and slope characteristics of each road are calculated using DEM elevation data to form a road elevation-slope characteristic dataset; Based on the road elevation-slope feature dataset and navigation electronic map data, the road detour coefficient, the number of sharp bends, and the accessibility index in the preliminary screening areas are calculated, and a set of road traffic obstacle indicators is constructed. The road traffic obstacle index set was evaluated using the analytic hierarchy process (AHP), and suitable areas for low-altitude logistics layout were selected based on the evaluation results.

[0006] In some embodiments, the process of calculating the elevation and slope characteristics of each road using DEM elevation data in the initially screened area to form a road elevation-slope feature dataset includes: Based on DEM elevation data, the average elevation, maximum elevation, and minimum elevation of each road within the jurisdiction of each township are calculated to generate a road elevation feature dataset; Road-level slope maps are generated based on DEM elevation data. The average slope, maximum slope, and minimum slope of each road are calculated and merged with the road elevation feature dataset to form a complete road elevation-slope feature dataset.

[0007] In some embodiments, the process of calculating the road detour coefficient and the number of sharp bends based on the road elevation-slope feature dataset and navigation electronic map data includes: Obtain the actual driving distance and calculate the detour coefficient based on the straight-line distance between the starting point and the destination. The detour coefficient is calculated as: actual driving distance / straight-line distance. When the detour coefficient is greater than the preset coefficient, the road is divided into segments according to the navigation electronic map data. The shape points of each segment are subjected to density filtering and interpolation. For each segment after processing, the starting point, midpoint and ending point are taken to form a standardized shape point sequence. The steering angle is calculated using the vector cross product method for the three shape points of each road segment in the standardized shape point sequence; The turning angle greater than a preset angle is defined as a sharp turn, and the number of sharp turns in the standardized shape point sequence is counted.

[0008] In some embodiments, the process of dividing the road into segments of a preset length based on navigation electronic map data, performing density filtering and interpolation on the shape points of each segment, and taking the start point, midpoint, and end point of each processed segment to form a standardized shape point sequence includes: The shape points of the road are obtained from the navigation electronic map data. The total length of the road is calculated by accumulating the geographical coordinates of the shape points. The number of segments corresponding to the complete preset length is determined by rounding down. Density filtering is performed using the Douglas-Peucker algorithm to eliminate redundant points and obtain key shape points whose shape point density matches the road complexity. For each road segment, an interpolation assignment is performed, with a key shape point distributed at the start, middle, and end points, resulting in a shape point sequence; The standardized shape point sequence is obtained by statistically analyzing the shape point sequences of all roads.

[0009] In some embodiments, the process of calculating the accessibility index of the initially screened areas based on the road elevation-slope feature dataset and navigation electronic map data includes: Obtain the location data of village committees or other grassroots administrative centers from navigation electronic map data; Dijkstra's algorithm was used to calculate the shortest path distance from each village committee to the provincial highway; Set a baseline distance and perform a dimensionless transformation to convert the shortest path distance into a reasonable distance. The reasonable distance is corrected for terrain using the slope standard deviation, and the corrected distance is used as the accessibility index.

[0010] In some embodiments, the road accessibility index set includes the percentage of high-grade roads, the percentage of detour roads, the percentage of sharp bends, the percentage of steep slopes, and the accessibility index. Among them, the proportion of high-grade roads is the ratio of the length of expressways, expressways, and national highways to the total road length in the region; The percentage of detour routes is the percentage of road lengths whose detour coefficient is greater than a preset coefficient; The percentage of roads with sharp bends is the percentage of road segments that include sharp bends. The percentage of steep slope roads is the percentage of road length with an average slope greater than 8 degrees.

[0011] In some embodiments, the step of evaluating the road traffic obstacle index set using the analytic hierarchy process (AHP) and selecting suitable areas for low-altitude logistics layout based on the evaluation results includes: The road traffic obstacle index set is normalized to obtain a normalized index set; Subjective weights are determined by combining the analytic hierarchy process (AHP) with expert scoring. Pairwise comparison judgment matrices are constructed based on each normalized indicator, and initial weights are calculated using the eigenvector method. After consistency checks and adjustments, the subjective weight vectors of each normalized indicator are obtained. The information entropy of each normalized index is calculated using the entropy weight method, and the objective weight vector of each normalized index is obtained by inversely proportionalizing the entropy value. The subjective and objective weights of each normalized indicator are integrated according to a preset ratio to generate a corresponding comprehensive weight vector, and the evaluation result is obtained by weighted summation. Areas with poor transportation will be considered suitable for low-altitude logistics development.

[0012] This invention proposes a system for screening regions suitable for low-altitude logistics layout, comprising: The initial screening unit is configured to acquire navigation electronic map data, DEM elevation data and administrative division data. Based on the national DEM elevation data and administrative division data, it calculates the average elevation of each township and filters out areas where the proportion of townships with average elevations within a preset range exceeds a preset threshold, thus generating the initial screening areas. The first calculation unit is configured to calculate the elevation and slope characteristics of each road in the preliminarily screened area using DEM elevation data, forming a road elevation-slope feature dataset; The second calculation unit is configured to calculate the road detour coefficient, the number of sharp bends, and the accessibility index in the preliminary screening area based on the road elevation-slope feature dataset and navigation electronic map data, and to construct a set of road traffic obstacle indicators. The evaluation unit is configured to evaluate the set of road traffic obstacle indicators using the analytic hierarchy process (AHP) and select suitable areas for low-altitude logistics layout based on the evaluation results.

[0013] This invention proposes a computer device, comprising: At least one processor; and a memory storing a computer program executable on the processor, which, when executing the program, performs the steps of the method for screening areas suitable for low-altitude logistics layout.

[0014] The present invention proposes a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method for screening regions suitable for low-altitude logistics layout.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes a method, system, equipment, and medium for screening suitable areas for low-altitude logistics layout. The method includes: acquiring navigation electronic map data, DEM elevation data, and administrative division data; calculating the average altitude of each township based on national DEM elevation data and administrative division data; screening areas where the proportion of townships with average altitudes within a preset range exceeds a preset threshold, generating preliminary screening areas; calculating the elevation and slope characteristics of each road in the preliminary screening areas using DEM elevation data, forming a road elevation-slope characteristic dataset; calculating road detour coefficients, the number of sharp bends, and the accessibility index in the preliminary screening areas based on the road elevation-slope characteristic dataset and navigation electronic map data, and constructing a road obstacle index set; evaluating the road obstacle index set using the analytic hierarchy process (AHP), and selecting suitable areas for low-altitude logistics layout based on the evaluation results.

[0016] This invention quickly identifies areas with significant logistical obstacles in mountainous regions, avoiding the blind spots of traditional manual surveys. It ensures that low-altitude logistics deployment prioritizes coverage of towns with complex terrain and low ground transportation efficiency, improving the accuracy of resource allocation. Based on DEM data, it extracts road elevation and slope characteristics and combines this with navigation electronic maps to calculate dynamic indicators such as detour coefficients and the number of sharp bends, constructing a multi-dimensional road obstacle assessment system that accurately reflects the actual degree of obstruction posed by mountainous roads to logistics transportation.

[0017] This method automates the entire process from data collection to regional selection, significantly shortening the planning cycle of low-altitude logistics networks. At the same time, it reduces subjective judgment bias through quantitative evaluation, ensuring that the layout plan meets the actual needs of the terrain and promotes the large-scale implementation of the low-altitude economy.

[0018] This invention employs a multi-dimensional quantitative assessment method that departs from single indicators such as road length or slope. Instead, it integrates multiple factors, including road grade, detour coefficient, sharp bends, slope, and distance from provincial highways, to construct a traffic inconvenience index, resulting in a more scientific and comprehensive assessment. Spatially, it features refined processing, using DEM data and road shape points to precisely identify micro-topographic features such as sharp bends and slopes, improving assessment accuracy. It filters mountainous towns using altitude thresholds, avoiding unnecessary calculations for plains areas and improving processing efficiency. Furthermore, it supports analytic hierarchy process (AHP) weight training to adapt to the differentiated needs of different regions and application scenarios. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0020] Figure 1 The present invention provides a flowchart of a method for screening regions suitable for low-altitude logistics layout.

[0021] Figure 2 This invention provides a system module diagram for screening regions suitable for low-altitude logistics layout.

[0022] Figure 3 A schematic diagram of the structure of an embodiment of the computer device provided by the present invention.

[0023] Figure 4 This is a schematic diagram of an embodiment of the computer-readable storage medium provided by the present invention.

[0024] Figure 5 This is a flowchart of an embodiment of a method for selecting regions suitable for low-altitude logistics layout provided by the present invention.

[0025] Figure 6 This invention provides a DEM elevation data table as an embodiment of a method for screening areas suitable for low-altitude logistics layout.

[0026] Figure 7 This invention provides a road elevation-slope feature dataset as an embodiment of a method for screening areas suitable for low-altitude logistics layout. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention. It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application.

[0028] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two entities or parameters with the same name but different names. It is clear that "first" and "second" are only for the convenience of expression and should not be construed as limiting the embodiments of the present invention. Subsequent embodiments will not explain this in detail.

[0029] This invention proposes a method for selecting suitable regions for low-altitude logistics layout. Please refer to [link / reference]. Figure 1 and Figure 5 ,include: Acquire navigation electronic map data, DEM elevation data, and administrative division data. Calculate the average elevation of each township based on the national DEM elevation data and administrative division data. Filter out areas where the proportion of townships with average elevations within a preset range exceeds a preset threshold, generating preliminary filtered areas. In the initially screened areas, the elevation and slope characteristics of each road are calculated using DEM elevation data to form a road elevation-slope characteristic dataset; Based on the road elevation-slope feature dataset and navigation electronic map data, the road detour coefficient, the number of sharp bends, and the accessibility index in the preliminary screening areas are calculated, and a set of road traffic obstacle indicators is constructed. The road traffic obstacle index set was evaluated using the analytic hierarchy process (AHP), and suitable areas for low-altitude logistics layout were selected based on the evaluation results.

[0030] A Digital Elevation Model (DEM) is a digital simulation of ground terrain (i.e., a digital representation of the surface morphology of terrain) achieved through limited terrain elevation data. It is a physical ground model that represents ground elevation using an ordered array of numerical values. It is a branch of the Digital Terrain Model (DTM), from which various other terrain feature values ​​can be derived.

[0031] The purpose of this invention is to provide a method for constructing and screening a traffic inconvenience index for mountainous towns, so as to solve the problem of lack of quantitative assessment of the degree of traffic inconvenience in mountainous towns in the existing technology, and to provide a scientific basis for assessing remote mountainous towns suitable for low-altitude logistics layout.

[0032] First, a basic analytical framework is constructed through multi-source data fusion: Elevation point sets for each township are extracted using national DEM elevation data. The elevation of the geometric center point is calculated by combining this with administrative division vector boundaries, and a weighted average elevation of the townships is obtained. Areas within the same prefecture-level city where the percentage of townships within a predetermined average elevation range exceeds 50% are selected as preliminary candidate areas. This step excludes plains areas and focuses on mid-to-high mountain regions. Within the candidate areas, elevation values ​​are sampled along the road centerline based on DEM data, and the elevation difference per 100-meter road segment is calculated to reflect the degree of undulation and slope variability, as well as the frequency of slope changes, thus constructing a road elevation-slope feature dataset. Further, the topological relationships of navigation electronic maps are combined to calculate road detour coefficients, the number of sharp bends, and the reachability index. These indicators are incorporated into a road accessibility indicator set, and a judgment matrix is ​​constructed using the analytic hierarchy process (AHP). After calculating the eigenvectors to obtain initial weights, a consistency check is performed, ultimately forming a comprehensive evaluation model. The predetermined range is 160-2800 meters, and the predetermined threshold is 50%.

[0033] Through multi-dimensional quantitative evaluation, the decision-making process for low-altitude logistics layout shifts from experience-based judgment to data-driven approaches. This ensures that the identified areas possess both complex terrain features and significant ground transportation efficiency losses, providing precise targets for drone network node site selection and effectively reducing trial-and-error costs.

[0034] In some embodiments, please refer to Figure 1 and Figure 5 The process of calculating the elevation and slope characteristics of each road using DEM elevation data in the initially screened area to form a road elevation-slope feature dataset includes: Based on DEM elevation data, the average elevation, maximum elevation, and minimum elevation of each road within the jurisdiction of each township are calculated to generate a road elevation feature dataset; Road-level slope maps are generated based on DEM elevation data. The average slope, maximum slope, and minimum slope of each road are calculated and merged with the road elevation feature dataset to form a complete road elevation-slope feature dataset.

[0035] Within the initially screened areas, for the road network under the jurisdiction of each township, spatial overlay analysis was used to match the road centerlines with the DEM grid. Elevation values ​​were sampled every 50 meters along the road, and the elevation statistics of individual roads were calculated: the average elevation was the arithmetic mean of the elevations of all sampled points, reflecting the overall elevation of the road; the maximum / minimum elevation was determined by comparing the values ​​of all sampled points, used to identify extreme terrain undulations along the road. For example, a mountain road may have an average elevation of 800 meters, but there are local mountain passes with elevations as high as 1200 meters. Such abrupt elevation changes would significantly increase transportation energy consumption.

[0036] Generating road-level slope maps based on a DEM requires a 3D terrain analysis tool. The slope value for each sampling point is calculated using the neighborhood elevation difference, employing an Arctan algorithm with a 3×3 grid window. The slope values ​​are then mapped to the road centerline, forming a continuous slope sequence. By statistically analyzing the average slope in the sequence, the overall steepness of the road is reflected; maximum slopes identify dangerous road sections; and minimum slopes analyze the distribution of gentle slope sections, quantifying the road's geometric resistance to traffic.

[0037] The elevation feature dataset (average / maximum / minimum elevation) and the slope feature dataset (average / maximum / minimum slope) are associated and merged by road ID to form a road elevation-slope feature dataset containing 6 core indicators. This dataset can be used to independently analyze the accessibility of individual roads, and can also identify regional terrain obstacle patterns through spatial clustering.

[0038] In some embodiments, please refer to Figure 1 and Figure 5 The process of calculating the road detour coefficient and the number of sharp bends based on the road elevation-slope feature dataset and navigation electronic map data includes: Obtain the actual driving distance and calculate the detour coefficient based on the straight-line distance between the starting point and the destination. The detour coefficient is calculated as: actual driving distance / straight-line distance. When the detour coefficient is greater than the preset coefficient, the road is divided into segments every preset length according to the navigation electronic map data. The shape points of each segment are subjected to density filtering and interpolation. For each segment after processing, the starting point, midpoint and ending point are taken to form a standardized shape point sequence. The steering angle is calculated using the vector cross product method for the three shape points of each road segment in the standardized shape point sequence; The turning angle greater than a preset angle is defined as a sharp turn, and the number of sharp turns in the standardized shape point sequence is counted.

[0039] The detour coefficient is calculated by first obtaining the actual driving distance through the route planning function of the navigation electronic map. This distance is generated based on road topology and turning restrictions, and can accurately reflect the path length that the vehicle needs to follow. At the same time, the Euclidean straight-line distance is calculated using the coordinates of the starting point and the ending point. The ratio of the two is the detour coefficient. When the detour coefficient exceeds the preset coefficient, which is an empirical threshold reflecting significant detour, the system automatically triggers a sharp curve detection process when the preset coefficient is set to 1.27. The road is divided into segments of 80 meters as preset lengths to ensure that each segment contains enough geometric feature points. Then, redundant points, such as repeated points on continuous straight lines, are removed by density filtering. Finally, cubic spline interpolation is used to supplement key turning points, forming a standardized shape point sequence. Each segment contains a starting point, a midpoint, and an ending point.

[0040] During the sharp curve identification phase, the system constructs vectors for three shape points on each road segment: the vector pointing from the starting point to the midpoint is the reference vector, and the vector pointing from the midpoint to the ending point is the target vector. The angle between the two vectors is calculated using the cross product of the vectors as the steering angle. Since the sign of the cross product indicates whether it's a left or right turn, its absolute value reflects the sharpness of the turn. When the steering angle exceeds a preset angle of 120°, the system determines that the road segment contains a sharp curve. Such sharp curves force vehicles to significantly decelerate or even stop and turn, significantly increasing travel time and energy consumption. Finally, the number of sharp curves in all segments is counted.

[0041] In some embodiments, please refer to Figure 1 and Figure 5 The process of dividing the road into segments of a preset length based on navigation electronic map data, performing density filtering and interpolation on the shape points of each segment, and then taking the start point, midpoint, and end point of each processed segment to form a standardized shape point sequence includes: The shape points of the road are obtained from the navigation electronic map data. The total length of the road is calculated by accumulating the geographical coordinates of the shape points. The number of segments corresponding to the complete preset length is determined by rounding down. Density filtering is performed using the Douglas-Peucker algorithm to eliminate redundant points and obtain key shape points whose shape point density matches the road complexity. For each road segment, an interpolation assignment is performed, with a key shape point distributed at the start, middle, and end points, resulting in a shape point sequence; The standardized shape point sequence is obtained by statistically analyzing the shape point sequences of all roads.

[0042] The original shape points of the road are obtained based on GPS trajectory points on the navigation electronic map. The total road length is calculated by accumulating geographic coordinates: starting from the starting point, the spherical distances of adjacent shape points are calculated sequentially, using the Haversine formula to account for the curvature of the Earth, and accumulated to the end point to obtain the total length. Then, a preset length is used as a segmentation threshold and rounded down to determine the number of complete segments. The Douglas-Peucker algorithm is used for density filtering: this algorithm recursively compares the maximum vertical distance from the shape point to the baseline connecting the first and last points. When the distance is less than a preset threshold of 2 meters, the intermediate points are removed, and only key turning points are retained. Dense points on a straight segment are compressed into the starting and ending points, while curves with greater curvature retain more feature points, thus reducing the number of points while preserving the road's geometric features.

[0043] In the interpolation allocation stage, the filtered key points of each road segment are resampled. If a segment only contains a start and end point, a new point is generated at the midpoint using linear interpolation, with coordinates being the average of the two endpoints. If multiple key points are already present, such as in a curve segment, they are redistributed according to the principle of equal spacing, ensuring that each segment ultimately contains three points: start, midpoint, and end. For example, if a filtered segment contains three key points A(0,0), B(30,10), and C(80,0), the midpoint B is used as the standardized midpoint without interpolation. If a segment only contains A(0,0) and C(80,0), B(40,0) is generated as the midpoint through interpolation. Finally, the standardized shape point sequences (3 points per segment) of all road segments are integrated according to road ID to form a standardized dataset.

[0044] In some embodiments, please refer to Figure 1 and Figure 5 The process of calculating the accessibility index of the initially screened areas based on the road elevation-slope feature dataset and navigation electronic map data includes: Obtain the location data of village committees or other grassroots administrative centers from navigation electronic map data; Dijkstra's algorithm was used to calculate the shortest path distance from each village committee to the provincial highway; Set a baseline distance and perform a dimensionless transformation to convert the shortest path distance into a reasonable distance. The reasonable distance is corrected for terrain using the slope standard deviation, and the corrected distance is used as the accessibility index.

[0045] The accessibility index is calculated by integrating spatial network analysis and terrain correction to quantify the obstacles to travel in remote areas by ground transportation. Village committee location data is extracted from navigation electronic maps. Village committees or other grassroots administrative centers represent indicators of local transportation convenience, including their latitude and longitude coordinates and township information, ensuring connectivity between the locations and the road network topology. Subsequently, Dijkstra's algorithm is used to calculate the shortest path distance from each village committee to the nearest provincial highway entrance: this algorithm searches for the optimal path based on road weights, generating the actual ground distance from the village committee to the main road, reflecting the initial travel cost of ground transportation.

[0046] To eliminate the impact of regional size differences, the shortest path distance needs to be transformed into a dimensionless form. A baseline distance is set, with the average distance from a village committee to a provincial highway within the county being 5 kilometers. Dividing the original distance by the baseline distance yields a reasonable distance; for example, if the distance to a village committee is 8 kilometers, the reasonable distance is 1.6. This step ensures comparability of accessibility across different regions, avoiding assessment biases caused by geographical differences.

[0047] The terrain correction stage introduces the slope standard deviation: based on the aforementioned road elevation-slope feature dataset, the slope standard deviation of all road segments along the route from the village committee to the provincial highway is calculated. 0.1 is used as a correction factor. The final corrected distance is the accessibility index; the smaller the value, the higher the accessibility.

[0048] In some embodiments, please refer to Figure 1 and Figure 5 The road accessibility index set includes the proportion of high-grade roads, the proportion of detour roads, the proportion of sharp bends, the proportion of steep slopes, and the accessibility index. Among them, the proportion of high-grade roads is the ratio of the length of expressways, expressways, and national highways to the total road length in the region; The percentage of detour routes is the percentage of road lengths whose detour coefficient is greater than a preset coefficient; The percentage of roads with sharp bends is the percentage of road segments that include sharp bends. The percentage of steep slope roads is the percentage of road length with an average slope greater than 8 degrees.

[0049] The road traffic obstacle index set quantifies the geometric and topological characteristics of roads from multiple dimensions, comprehensively reflecting the traffic efficiency and safety risks of regional ground transportation.

[0050] The proportion of high-grade roads, using major roads such as expressways, expressways, and national highways as the assessment objects, is calculated through GIS spatial overlay analysis as the ratio of their total length to the total length of the regional road network. This indicator is directly related to transportation timeliness—the higher the proportion of high-grade roads, the stronger the region's external connectivity and the more significant the efficiency of logistics transshipment. For example, if a county has a total expressway length of 120 kilometers and a total regional road length of 800 kilometers, then the proportion of high-grade roads is 15%, indicating that 15% of the roads have expressway capacity.

[0051] The detour road percentage focuses on the issue of route redundancy. Using the aforementioned detour coefficient calculation model, road segments with detour coefficients greater than a preset coefficient are selected, and their length is calculated as a percentage of the total road length. These segments typically deviate significantly from the straight-line distance due to mountainous terrain, river cutting, or planning deficiencies, increasing transportation time and fuel consumption. For example, in a mountainous area with a total road length of 200 kilometers, if 45 kilometers of the road have a detour coefficient greater than the preset coefficient, then the detour road percentage is 22.5%, reflecting that 22.5% of the roads in this area have detour issues.

[0052] The percentage of sharp bends is used to identify dangerous road sections that require mandatory speed reduction by statistically analyzing the ratio of the number of road sections containing sharp bends to the total number of road sections.

[0053] The percentage of steep roads is calculated based on the average gradient of the road elevation data, selecting the proportion of road sections with a gradient greater than 8°. These sections are more likely to cause vehicle braking failure or a surge in energy consumption. For example, in a region with 1000 road sections, 200 sections contain sharp bends, accounting for 20% of the total; the total road length is 500 kilometers, and 80 kilometers of these sections have an average gradient greater than 8°, accounting for 16% of the total.

[0054] The accessibility index integrates the aforementioned indicators and quantifies the connection quality between remote areas and main roads, reflecting the coverage of basic public services by ground transportation.

[0055] In some embodiments, please refer to Figure 1 and Figure 5 The step of evaluating the road traffic obstacle index set using the analytic hierarchy process (AHP) and selecting suitable areas for low-altitude logistics layout based on the evaluation results includes: The road traffic obstacle index set is normalized to obtain a normalized index set; Subjective weights are determined by combining the analytic hierarchy process (AHP) with expert scoring. Pairwise comparison judgment matrices are constructed based on each normalized indicator, and initial weights are calculated using the eigenvector method. After consistency checks and adjustments, the subjective weight vectors of each normalized indicator are obtained. The information entropy of each normalized index is calculated using the entropy weight method, and the objective weight vector of each normalized index is obtained by inversely proportionalizing the entropy value. The subjective and objective weights of each normalized indicator are integrated according to a preset ratio to generate a corresponding comprehensive weight vector. The weighted sum is then used to obtain the evaluation result. Among them, 80 points or above indicates convenient transportation, 40-79 points indicates moderate transportation, and below 40 points indicates inconvenient transportation. Areas with poor transportation will be considered suitable for low-altitude logistics development.

[0056] Road accessibility assessment needs to integrate subjective experience with objective data. A combination of the Analytic Hierarchy Process (AHP) and entropy weighting is used to normalize the road accessibility indicator set: the original values ​​of each indicator are mapped to the [0,1] interval to eliminate dimensional differences. For example, the original value of high-grade roads (15%) is normalized to 0.15; the original value of sharp curves (20%) is normalized to 0.20, ensuring comparability between indicators.

[0057] Subjective weights were determined using the analytic hierarchy process (AHP). Experts in transportation planning and logistics were invited to conduct pairwise comparisons of the indicators to construct a judgment matrix (e.g., the importance score for the proportion of high-grade roads versus the proportion of detour roads was 3:1). Initial weight vectors were calculated using the eigenvector method, and a consistency check was performed. A consistency ratio (CR) of less than 0.1 was considered acceptable, and the matrix was adjusted until the consistency requirements were met.

[0058] Objective weights are calculated using the entropy weighting method, which calculates the information entropy of each indicator based on a normalized indicator set. The smaller the entropy value, the higher the indicator's discriminative power. For example, the proportion of steep roads in a certain area has a large coefficient of variation, resulting in a low entropy value and therefore a higher weight. The objective weight vector is then calculated using an inverse entropy formula.

[0059] The subjective and objective weights are combined and weighted at a preset ratio of 0.6:0.4 to generate a comprehensive weight vector, and areas with inconvenient transportation are selected as candidate areas for low-altitude logistics layout.

[0060] Taking a mountainous county as an example, the normalized index set is [0.12 (proportion of high-grade roads), 0.25 (proportion of detour roads), 0.22 (proportion of sharp bends), 0.18 (proportion of steep slopes), 0.23 (accessibility index)]. The comprehensive weight vector is [0.27, 0.24, 0.21, 0.16, 0.12]. The weighted sum score is 0.12×0.27+0.25×0.24+…+0.23×0.12=38.6 points, indicating a region with inconvenient transportation, suitable for low-altitude logistics deployment.

[0061] By integrating subjective and objective weights, this approach leverages expert experience to identify key indicators while employing entropy weighting to identify the high-discrimination ratio of steep roads in mountainous areas, thus avoiding the biases of a single weighting method. The evaluation results accurately pinpoint bottleneck areas in ground transportation, providing quantitative data for low-altitude logistics: in areas with poor transportation, drones can bypass complex terrain for direct delivery, significantly improving timeliness and coverage while reducing safety risks and costs associated with ground transportation.

[0062] This invention proposes a system for selecting regions suitable for low-altitude logistics layout. Please refer to [link / reference]. Figure 2 ,include: The initial screening unit is configured to acquire navigation electronic map data, DEM elevation data and administrative division data. Based on the national DEM elevation data and administrative division data, it calculates the average elevation of each township and filters out areas where the proportion of townships with average elevations within a preset range exceeds a preset threshold, thus generating the initial screening areas. The first calculation unit is configured to calculate the elevation and slope characteristics of each road in the preliminarily screened area using DEM elevation data, forming a road elevation-slope feature dataset; The second calculation unit is configured to calculate the road detour coefficient, the number of sharp bends, and the accessibility index in the preliminary screening area based on the road elevation-slope feature dataset and navigation electronic map data, and to construct a set of road traffic obstacle indicators. The evaluation unit is configured to evaluate the set of road traffic obstacle indicators using the analytic hierarchy process (AHP) and select suitable areas for low-altitude logistics layout based on the evaluation results.

[0063] This system mainly consists of six parts: navigation electronic map data, DEM elevation data, administrative division data, data processing and production subsystem, road accessibility evaluation data, and traffic accessibility assessment system.

[0064] 1. Navigation electronic map data: uses road network data, including road ID, road category, road name, road width, road length, road grade, geom (road shape data), etc. 2. DEM elevation data: used to obtain the elevation of the road (average elevation, maximum elevation, minimum elevation).

[0065] 3. Administrative division data: Includes national provincial, municipal, district / county, and township division codes, geom boundary and other attribute data; 4. Data processing and production subsystem: preprocesses the basic data.

[0066] 5. Road accessibility evaluation data: This data is produced by the data processing and production subsystem and provides data support for the traffic accessibility assessment model, enabling rapid assessment of townships within the district / county.

[0067] 6. Traffic Convenience Assessment System: Based on road convenience evaluation data, the system calculates relevant indices for districts and counties, assesses districts and counties suitable for low-altitude logistics, and displays districts and counties on the map with different color values ​​according to the assessment scores, making it easier for planners to quickly identify key towns and townships suitable for low-altitude logistics layout.

[0068] In some embodiments, please refer to Figure 5 , Figure 6 and Figure 7 The specific implementation process in one embodiment of the present invention is as follows: Step 1. Based on DEM elevation data, quickly filter out the townships with mountainous terrain in counties and districts across the country (the proportion of townships with an average elevation greater than 160 meters and less than 2800 meters in counties and districts is greater than 50%, and this value is derived from the topographic analysis of China's administrative counties and districts). Then, perform the following calculations and further filter the selected counties and districts. Step 2. Based on the DEM elevation data, calculate the average elevation, maximum elevation, and minimum elevation of each road in the selected districts and counties; Step 3. Generate a slope map using DEM elevation data to quickly calculate the average slope, maximum slope, and minimum slope of each road in the filtered districts and counties; Step 4. Calculate the road detour coefficient K for the filtered districts and counties, where K = actual road length / straight-line distance between the beginning and end of the road; Step 5. Calculate the number of sharp curves in each road of the filtered districts and counties. When the road detour coefficient K is less than the preset coefficient, set N=0. If the road detour coefficient K is greater than the preset coefficient, calculate the number of sharp curves in the road. Based on the road shape point data, determine the calculation on the road. Take the first, last and middle shape points (P1, P2, P3) of the shape points within each preset length on the road (take the remainder if it is less than the preset length) to calculate the turning angle θ. If θ>=120, it is a sharp curve. Calculate the number of sharp curves in the road accordingly.

[0069] Total formula for the number of sharp turns:

[0070] Symbol definition: The total number of sharp bends on a single road; Road detour coefficient ( ); The number of valid segments after the road is divided into segments according to the preset length; : No. The turning angle of the segment (unit: degrees); : Indicator function, returns 1 if the condition is true, otherwise returns 0 (only for statistical purposes). (The bend).

[0071] 2) Preset length segmentation rules: First calculate the total length of the road. (Based on the cumulative geographic distance of shape points), then segmented according to the preset length:

[0072] ; Take 3 key shape points for each segment: segment start point ( ), midpoint of segment ( ), End point of segment ( ).

[0073] 3) Steering angle calculate: (1) Vector calculate: For each segment of 3 shape points , , Calculate the vector: ; (2) Dot product With the length of the mold : ; ; (3) Steering angle (Radian to Angle): ; (Minimum value, such as) ): Avoid having a denominator of 0; Geographic coordinate correction: Latitude and longitude (WGS84) need to be converted to plane coordinates (UTM projection): ; ( The radius of the Earth is approximately 6,371,000 meters. (Latitude and longitude in radians).

[0074] Sharp curve judgment and counting: ; Total number of sharp bends on a single road: .

[0075] The threshold for calculating the number of road bends (road detour coefficient K) is a preset coefficient, which is an ideal value obtained from the calculation of road detours and sharp bends in the actual road calculation in the project. When the road detour coefficient K > the preset coefficient, the probability of there is a sharp bend in the road is >90%. The shape points within each preset length on the road (the remainder is taken for those less than the preset length) are taken to determine whether there is a sharp bend in the road. The reasonable value is obtained by analyzing a large amount of sharp bend road data through QGIS software.

[0076] Based on steps 1-5, road accessibility evaluation data is generated, such as... Figure 6 and Figure 7 As shown, this data provides support for the transportation convenience assessment model, enabling rapid assessment of townships within the district / county.

[0077] Step 6. Based on the road accessibility evaluation data, calculate the relevant indices of district and county roads, and assess the districts and counties suitable for low-altitude logistics.

[0078] 1) Calculate the proportion K1 of expressways, urban expressways, and national highways in the roads of the respective districts and counties, which is the proportion of high-grade roads; 2) Calculate the percentage (K2) of detour routes (national highways and lower-level roads) in the relevant district / county roads; this percentage represents the proportion of detour routes. 3) Calculate the percentage K3 of roads with more than 1 sharp bends in the roads of the district / county, which is the percentage of sharp bend roads; 4) Calculate the percentage (K4) of roads with a gradient greater than 8 in the total number of roads in the district / county; this represents the percentage of steep roads. 5) Calculate the shortest distance S3 from each village committee to the provincial highway. Calculate the reasonable value of the distance S from the village committee to the provincial highway using the dimensionless method. Add them together and calculate the average. Divide the average by S (S is within one kilometer) to obtain K5, which is the accessibility index. A scoring index was established for each K1, K2, K3, K4, and K5 value, and W1, W2, W3, W4, and W5 were obtained by normalizing them to a percentage system.

[0079] Weight the above data as follows: ;

[0080] (Based on historical logistics data training or analytic hierarchy process to determine a1, a2, a3, a4, a5) Output the scoring results.

[0081] Ultimately, a score is obtained for each district, county, and township suitable for low-altitude logistics. Districts and counties suitable for building low-altitude logistics are selected, and districts and counties are displayed on the map with different color values ​​according to the evaluation scores, so that planners can quickly identify key townships suitable for low-altitude logistics layout.

[0082] The more specific process includes: 1. Data preparation stage: Collect navigation electronic map data, DEM elevation data (resolution not less than 30 meters), and administrative division data (township level) of the target area.

[0083] The DEM data is preprocessed to generate a slope map and extract elevation information (average, maximum, and minimum elevation).

[0084] 2. Data processing stage: Based on DEM data, townships that meet the characteristics of mountainous areas (average altitude above 160 meters and mountainous area accounting for more than 50%) were selected.

[0085] Spatial analysis was performed on each road within the selected township area, and the following indicators were calculated: Average slope, maximum slope, and minimum slope of the road; Road detour coefficient K; Number of sharp road bends (based on steering angle θ ≥ preset angle); Road classification (expressway, national highway, provincial highway, etc.); The shortest distance from the village committee to the provincial highway.

[0086] 3. Index Construction Phase: Based on the above indicators, a traffic inconvenience index model is constructed: K1: Percentage of high-grade roads: Expressway / Fastway / National Highway; Detour route percentage K2: The percentage of roads below national highways with a detour coefficient greater than the preset coefficient; K3: Percentage of sharp bends; Steep slope road percentage (K4): Percentage of roads with a slope greater than 8°; Convenience and Accessibility Index K5: The standardized distance between the village committee and the provincial highway.

[0087] Normalize K1~K5 to obtain W1~W5.

[0088] 4. Weighting and Scoring: The weight coefficients a1 to a5 of each indicator are determined by using the Analytic Hierarchy Process (AHP) or training based on historical logistics data.

[0089] Calculate the comprehensive score W=Σ(Wi×ai) and output the traffic inconvenience index for each township.

[0090] Based on the same inventive concept, according to another aspect of the present invention, such as Figure 3 As shown, an embodiment of the present invention also provides a computer device 30, which includes a processor 310 and a memory 320. The memory 320 stores a computer program 321 that can be run on the processor. When the processor 310 executes the program, it performs the steps of the method described above.

[0091] Based on the same inventive concept, according to another aspect of the present invention, such as Figure 4 As shown, embodiments of the present invention also provide a computer-readable storage medium 40, which stores a computer program 410 that, when executed by a processor, performs the methods described above.

[0092] Embodiments of the present invention may also include a corresponding computer device. The computer device includes a memory, at least one processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes any of the methods described above when executing the program.

[0093] The memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions / modules in the embodiments of this application. The processor executes various functional applications and data processing of the device by running the non-volatile software programs, instructions, and modules stored in the memory, thereby implementing the above-described method.

[0094] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the device. Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the local module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0095] Finally, it should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The storage medium for the program can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The above computer program embodiments can achieve the same or similar effects as any of the corresponding foregoing method embodiments.

[0096] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the functionality of various illustrative components, blocks, modules, circuits, and steps has been generally described. Whether this functionality is implemented as software or as hardware depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the embodiments disclosed herein.

[0097] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. The sequence numbers of the disclosed embodiments of this invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0098] It should be understood that, as used herein, the singular form “a” is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, “and / or” refers to any and all possible combinations of one or more of the associated listed items.

[0099] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for selecting suitable regions for low-altitude logistics layout, characterized in that, include: Acquire navigation electronic map data, DEM elevation data, and administrative division data. Calculate the average elevation of each township based on the national DEM elevation data and administrative division data. Filter out areas where the percentage of townships with an average elevation within a preset range exceeds a preset threshold, generating preliminary filtered areas. In the initially screened areas, the elevation and slope characteristics of each road are calculated using DEM elevation data to form a road elevation-slope characteristic dataset; Based on the road elevation-slope feature dataset and navigation electronic map data, the road detour coefficient, the number of sharp bends, and the accessibility index in the preliminary screening areas are calculated, and a set of road traffic obstacle indicators is constructed. The road traffic obstacle index set was evaluated using the analytic hierarchy process (AHP), and suitable areas for low-altitude logistics layout were selected based on the evaluation results.

2. The method for selecting suitable areas for low-altitude logistics layout according to claim 1, characterized in that, The process of calculating the elevation and slope characteristics of each road using DEM elevation data in the initially screened area to form a road elevation-slope feature dataset includes: Based on DEM elevation data, the average elevation, maximum elevation, and minimum elevation of each road within the jurisdiction of each township are calculated to generate a road elevation feature dataset; Road-level slope maps are generated based on DEM elevation data. The average slope, maximum slope, and minimum slope of each road are calculated and merged with the road elevation feature dataset to form a complete road elevation-slope feature dataset.

3. The method for selecting suitable areas for low-altitude logistics layout according to claim 2, characterized in that, The process of calculating the road detour coefficient and the number of sharp bends based on the road elevation-slope feature dataset and navigation electronic map data includes: Obtain the actual driving distance and calculate the detour coefficient based on the straight-line distance between the starting point and the destination. The detour coefficient is calculated as: actual driving distance / straight-line distance. When the detour coefficient is greater than the preset coefficient, the road is divided into segments every preset length according to the navigation electronic map data. The shape points of each segment are subjected to density filtering and interpolation. For each segment after processing, the starting point, midpoint and ending point are taken to form a standardized shape point sequence. The steering angle is calculated using the vector cross product method for the three shape points of each road segment in the standardized shape point sequence; The turning angle greater than a preset angle is defined as a sharp turn, and the number of sharp turns in the standardized shape point sequence is counted.

4. The method for selecting suitable areas for low-altitude logistics layout according to claim 3, characterized in that, The process of dividing the road into segments of a preset length based on navigation electronic map data, performing density filtering and interpolation on the shape points of each segment, and then taking the start point, midpoint, and end point of each processed segment to form a standardized shape point sequence includes: The shape points of the road are obtained from the navigation electronic map data. The total length of the road is calculated by accumulating the geographical coordinates of the shape points. The number of segments corresponding to the complete preset length is determined by rounding down. Density filtering is performed using the Douglas-Peucker algorithm to eliminate redundant points and obtain key shape points whose shape point density matches the road complexity. For each road segment, an interpolation assignment is performed, with a key shape point distributed at the start, middle, and end points, resulting in a shape point sequence; The standardized shape point sequence is obtained by statistically analyzing the shape point sequences of all roads.

5. The method for screening suitable areas for low-altitude logistics layout according to claim 3, characterized in that, The process of calculating the accessibility index of the initially screened areas based on the road elevation-slope feature dataset and navigation electronic map data includes: Obtain the location data of village committees or other grassroots administrative centers from navigation electronic map data; Dijkstra's algorithm was used to calculate the shortest path distance from each village committee to the provincial highway; Set a baseline distance and perform a dimensionless transformation to convert the shortest path distance into a reasonable distance. The reasonable distance is corrected for terrain using the slope standard deviation, and the corrected distance is used as the accessibility index.

6. The method for screening suitable areas for low-altitude logistics layout according to claim 5, characterized in that, The road accessibility index set includes the percentage of high-grade roads, the percentage of detour roads, the percentage of sharp bends, the percentage of steep slopes, and the accessibility index. Among them, the proportion of high-grade roads is the ratio of the length of expressways, expressways, and national highways to the total road length in the region; The percentage of detour routes is the percentage of road lengths whose detour coefficient is greater than a preset coefficient; The percentage of roads with sharp bends is the percentage of road segments that include sharp bends. The percentage of steep slope roads is the percentage of road length with an average slope greater than 8 degrees.

7. The method for screening suitable areas for low-altitude logistics layout according to claim 1, characterized in that, The steps of evaluating the road traffic obstacle index set using the analytic hierarchy process (AHP) and selecting suitable areas for low-altitude logistics layout based on the evaluation results include: The road traffic obstacle index set is normalized to obtain a normalized index set; Subjective weights are determined by combining the analytic hierarchy process (AHP) with expert scoring. Pairwise comparison judgment matrices are constructed based on each normalized indicator, and initial weights are calculated using the eigenvector method. After consistency checks and adjustments, the subjective weight vectors of each normalized indicator are obtained. The information entropy of each normalized index is calculated using the entropy weight method, and the objective weight vector of each normalized index is obtained by inversely proportionalizing the entropy value. The subjective and objective weights of each normalized indicator are combined according to a preset ratio to generate a corresponding comprehensive weight vector, and the evaluation result is obtained by weighted summation. Areas with poor transportation will be considered suitable for low-altitude logistics development.

8. A system for selecting regions suitable for low-altitude logistics layout, characterized in that, include: The initial screening unit is configured to acquire navigation electronic map data, DEM elevation data and administrative division data. Based on the national DEM elevation data and administrative division data, it calculates the average elevation of each township and filters out areas where the proportion of townships with average elevations within a preset range exceeds a preset threshold, thus generating the initial screening areas. The first calculation unit is configured to calculate the elevation and slope characteristics of each road in the preliminarily screened area using DEM elevation data, forming a road elevation-slope feature dataset; The second calculation unit is configured to calculate the road detour coefficient, the number of sharp bends, and the accessibility index in the preliminary screening area based on the road elevation-slope feature dataset and navigation electronic map data, and to construct a set of road traffic obstacle indicators. The evaluation unit is configured to evaluate the set of road traffic obstacle indicators using the analytic hierarchy process (AHP) and select suitable areas for low-altitude logistics layout based on the evaluation results.

9. A computer device, comprising: At least one processor; And a memory storing a computer program executable on the processor, characterized in that, when the processor executes the program, it performs the steps of a method for screening suitable low-altitude logistics layout areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it performs the steps of a method for screening regions suitable for low-altitude logistics layout as described in any one of claims 1 to 7.

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