Trajectory and space-based delivery rider station site selection method, device and medium

Abstract

The application discloses a kind of based on trajectory and space's take-out rider relay station site selection method, equipment and medium, comprising: collection rider trajectory data, and pre-processing;For each rider, according to the speed between each two rider trajectory data adjacent in time Identify multiple low-speed points, according to the distance between each two low-speed points adjacent in time Find low-speed point group, and according to the maximum time difference in each low-speed point group Find target low-speed point group, according to target low-speed point group Determine rider long time rest point;According to the intensive degree of rider long time rest point on space Rest demand map is generated;Based on rest demand map and construction feasibility evaluation system Preliminary site selection of take-out rider relay station, determine several feasibility site selection, form site selection scheme set;Construct space suitability site selection evaluation system, the construction priority of several feasibility site selection is sorted, complete accurate site selection.The application can select the best place of take-out rider relay station.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of post site selection, and in particular to a take-out rider post site selection method based on trajectory and space, a device and a medium. BACKGROUND

[0002] Under the background of people's city construction, a large number of new employment form workers typified by take-out riders have emerged. While they are serving the public day and night, the related supporting facilities provided by large cities for these people have not yet been perfect. It is necessary to promote the setting of temporary rest places in residential areas and commercial areas where new employment form workers concentrate to solve their parking, charging, drinking water and other problems. Although various cities have successively carried out pilot rider post construction exploration, there are generally problems such as form being greater than function and waste of space resources. The unreasonable layout of rider posts leads to a large number of riders still choosing to spontaneously gather and rest on the streets. These irregular rest spaces will also affect the social public safety order, so it is urgent to explore a rider post site selection decision-making method that can truly play a social security benefit.

[0003] The rider post needs to match the work trajectory of the rider and meet the psychological needs of the rider when resting in terms of space site selection. On the one hand, the rider's rest characteristics are highly related to the spatial distribution of orders and change with the dining time, so the rider's selection of rest space has multi-factor relevance. Most existing rider posts are based on various business halls or idle spaces, which are difficult to meet the actual needs in terms of time and space. On the other hand, the rider post located in the space of government, bank and other administrative or financial institutions will bring great psychological pressure to the rider due to the watchful eyes of the internal staff and the quiet and high-end environment. Such a place even makes them feel uneasy, which will exacerbate their sense of professional inferiority and social isolation.

[0004] Therefore, in view of the psychological threshold of the rider under the interweaving of self-esteem and inferiority, the spatial threshold under the relationship between the delivery supply and demand network, it is urgent to combine the rider work trajectory big data and the rider psychological demand survey analysis to scientifically site the take-out rider post. SUMMARY

[0005] In view of this, the purpose of the present application is to provide a take-out rider post site selection method based on trajectory and space.

[0006] In order to achieve the above technical purpose, the technical scheme adopted by the present application is:

[0007] The present application provides a take-out rider post site selection method based on trajectory and space, comprising the following steps:

[0008] Step 1, collecting rider trajectory data and preprocessing the rider trajectory data;

[0009] Step 2: For each rider, identify multiple low-speed points based on the speed between each two temporally adjacent rider trajectory data, find at least one low-speed point group based on the distance between each two temporally adjacent low-speed points, find a target low-speed point group based on the maximum time difference in each low-speed point group, and determine the rider's long-term rest stop points based on the target low-speed point group;

[0010] Step 3: Generate a rest demand map based on the spatial density of riders’ long-term rest stops;

[0011] Step 4: Conduct preliminary site selection for the delivery rider station based on the rest demand map and construction feasibility evaluation system, determine several feasible sites for the station construction, and form a set of site selection plans;

[0012] Step 5: Construct a spatial suitability site selection evaluation system that integrates the psychological needs of riders, sort the construction priorities of several feasible sites within the site selection plan set, and then complete the precise site selection.

[0013] Furthermore, the data is preprocessed in step 1, specifically including:

[0014] Step 11: Obtain rider trajectory data from the food delivery platform;

[0015] Step 12: Use the Python library to clean and format the rider trajectory data; the cleaning includes removing outliers in the rider trajectory data, including abnormal GPS drift points, duplicate data, and missing data;

[0016] Step 13: The cleaned and formatted rider trajectory data is used as a dataset and stored in a relational database.

[0017] Furthermore, the rider trajectory data is the GPS location of the rider when picking up and delivering food, and the GPS location includes the rider ID, longitude and latitude coordinates and recording time.

[0018] Furthermore, the step 2 specifically includes:

[0019] Step 21: Find the GPS location of the same rider based on the rider ID;

[0020] Step 22: Sort the GPS points of the same rider in the order of recording time;

[0021] Step 23: For each GPS point corresponding to the rider, the moving speed between each two temporally adjacent GPS points is calculated based on the latitude and longitude coordinates and the recording time of the GPS point, and it is determined whether the moving speed between the two temporally adjacent GPS points is less than or equal to the speed threshold. If so, the two temporally adjacent GPS points are marked as low-speed points; otherwise, no processing is performed;

[0022] Step 24: Determine whether the distance between the longitude and latitude coordinates corresponding to each two temporally adjacent low-speed points is less than a distance threshold. If so, classify the multiple consecutive low-speed points that meet the condition into the same low-speed point group to form at least one low-speed point group; otherwise, do not process the points.

[0023] Step 25: Determine whether the time difference between the recording time corresponding to the earliest low-speed point and the latest low-speed point in each low-speed point group exceeds a time threshold. If so, use the low-speed point group as the target low-speed point group.

[0024] Step 26: Connect the outermost low-speed points in the target low-speed point group to form a geometric figure, and select the center point of the geometric figure as the rider's long-term rest stop point.

[0025] Furthermore, the step 3 specifically includes:

[0026] Step 31: Use a hotspot detection algorithm to analyze the spatial distribution density of long-term rest stops for all riders;

[0027] Step 32: Based on the analysis results of spatial distribution density, a recreation demand map covering the entire study area is generated using GIS tools.

[0028] Furthermore, the step 4 specifically includes:

[0029] Step 41: Divide the rest demand map into multiple m*m grids, where the value of m is set by the user according to needs, and record the rest demand value corresponding to each m*m grid. The rest demand value is the value corresponding to the position of the m*m grid on the rest demand map.

[0030] Step 42: Using the natural breakpoint method, each m*m grid is divided into different levels of rest demand areas according to the rest demand value, namely, high rest demand area, relatively high rest demand area, medium rest demand area, relatively low rest demand area, and low rest demand area;

[0031] Step 43: Divide each determined high rest demand area into multiple n*n grids, where the value of n is set by the user according to needs and the value of n is smaller than the value of m;

[0032] Step 44: For each n*n grid, score it using the Likert five-level scale based on the construction feasibility evaluation system, arrange it in descending order of score, and select the first a n*n grids as feasible sites for the construction of food delivery rider stations to form a site selection plan set. The value of a is set by the user according to needs.

[0033] Furthermore, the step 5 specifically includes:

[0034] Step 51: Use the AHP analytic hierarchy process to construct a spatial suitability site selection evaluation system from the two dimensions of spatial environment and psychological environment;

[0035] Step 52: Based on the spatial suitability site selection evaluation system, use the Likert five-level scale to score several feasible sites in the site selection plan set, rank the several feasible sites according to the scores, and select the best feasible site with the highest score.

[0036] Furthermore, the construction feasibility evaluation system includes two dimensions: space capacity and infrastructure support. The space capacity dimension includes two indicators: the volume of available space and transportation accessibility; the infrastructure support dimension includes two indicators: water supply facilities and power supply facilities.

[0037] The indicators of the spatial environment include traffic accessibility, noise decibels, green coverage and environmental cleanliness; the indicators of the psychological environment include pedestrian attitudes, merchant attitudes and manager attitudes; the indicators of the spatial environment and the psychological environment are quantified using the Likert five-level scale.

[0038] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the method for selecting a delivery rider's station based on trajectory and space as described above is implemented.

[0039] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned trajectory and space-based takeaway rider station location selection method.

[0040] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0041] The present invention collects and analyzes a large amount of rider trajectory data from the food delivery platform, screens out areas where food delivery riders have the most intensive rest activities and the strongest rest needs, and arranges food delivery rider stations of different levels and capacities according to the intensity of the needs, which can effectively meet the riders' rest needs and reduce the time it takes for riders to find rest stations. In addition, while data is driving the site selection, attention is paid to spatial indicators such as urban space capacity and infrastructure, and psychological indicators such as the tolerance of the surrounding environment. By revising the preliminary site selection, the quality of food delivery riders' rest can be further improved and the efficiency of food delivery delivery can be improved. Therefore, the scientific site selection technology for rider stations can, on the one hand, help to solve the social pain points of a large number of current love stations being idle due to improper site selection, and food delivery riders gathering in streets and alleys to rest, which affects public safety and order; on the other hand, it can improve the safety of new employment groups such as riders under the purpose of building a people's city, and improve the comprehensive utilization efficiency of the space of love stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a flowchart of an execution method for selecting a delivery rider's station location based on trajectory and space provided by an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of an electronic device provided by an embodiment of the present invention.

[0045] Figure 3 It is a schematic diagram of a computer-readable storage medium provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be described in further detail below with reference to the accompanying drawings and examples. It is particularly noted that the following examples are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. Similarly, the following examples are only some embodiments of the present invention and are not intended to be exhaustive. All other embodiments obtained by those of ordinary skill in the art without creative effort are intended to fall within the scope of protection of the present invention.

[0047] See Figure 1 The present invention provides a method for selecting a delivery rider station location based on trajectory and space, comprising the following steps:

[0048] Step 1: Collect rider trajectory data and pre-process the rider trajectory data;

[0049] In this embodiment, the data is preprocessed in step 1, specifically including:

[0050] Step 11, obtaining rider trajectory data from a takeout platform, the rider trajectory data being GPS points (the position of a certain rider recorded by the takeout platform every equal time (such as 30s)) when the rider picks up and delivers food, the GPS points including rider ID, latitude and longitude coordinates, and recording time;

[0051] Step 12, cleaning and formatting the rider trajectory data using Python libraries (such as Pandas, NumPy, etc.); the cleaning includes removing abnormal values in the rider trajectory data, the abnormal values including abnormal GPS drift points, repeated data, and missing data; this step is to clean and format the data, which can improve data quality and ensure data consistency and accuracy, thereby laying a solid foundation for subsequent data analysis.

[0052] Step 13, taking the cleaned and formatted rider trajectory data as a data set and storing the data set in a relational database; this step is for data storage for subsequent analysis.

[0053] Step 2, for each rider, identifying a plurality of low-speed points according to the speed between each two adjacent rider trajectory data in time, finding at least one low-speed point group according to the distance between each two adjacent low-speed points in time, and finding a target low-speed point group according to the maximum time difference in each low-speed point group, and determining a long-time rest point of the rider according to the target low-speed point group;

[0054] In this embodiment, step 2 specifically includes:

[0055] Step 21, finding the GPS points of the same rider according to the rider ID;

[0056] Step 22, sorting the GPS points of the same rider according to the chronological order of recording time; this step is to sort the time, providing a timestamp for each GPS point, which is crucial for understanding the order and duration of events.

[0057] Step 23. For each GPS point corresponding to each rider, the moving speed between each two temporally adjacent GPS points is calculated based on the latitude and longitude coordinates and recording time of the GPS points. The calculation formula is: moving speed between two temporally adjacent GPS points = distance between the latitude and longitude coordinates of the front GPS point and the latitude and longitude coordinates of the back GPS point / time difference between the recording time of the front GPS point and the recording time of the back GPS point; determine whether the moving speed between two temporally adjacent GPS points is less than or equal to the speed threshold. If so, mark the two temporally adjacent GPS points as low-speed points; otherwise, do not process them. This step is to calculate the speed, find the low-speed points, and lay the foundation for subsequent calculations.

[0058] Step 24: Determine whether the distance between the longitude and latitude coordinates corresponding to each two temporally adjacent low-speed points is less than a distance threshold. If so, classify the multiple consecutive low-speed points that meet the condition into the same low-speed point group to form at least one low-speed point group; otherwise, do not process the points.

[0059] Step 25: Determine whether the time difference between the recording time corresponding to the earliest low-speed point and the latest low-speed point in each low-speed point group exceeds a time threshold. If so, use the low-speed point group as the target low-speed point group.

[0060] Step 26: Connect the outermost low-speed points in the target low-speed point group to form a geometric figure, and select the center point of the geometric figure as the rider's long-term rest stop. Steps 24 to 26 are for spatial aggregation. Aggregating multiple low-speed points in space into multiple low-speed point groups according to certain rules helps to understand the distribution characteristics of spatial data. Through spatial aggregation technology, the rider's rest point can be identified more accurately. This method aggregates trajectory points with spatiotemporal similarities so that points with similar spatiotemporal characteristics are classified into one category, thereby improving the accuracy of recognition.

[0061] For example:

[0062] Set the speed threshold to 1km / h, the distance threshold to 50m, and the time threshold to 5 minutes;

[0063] S1. Find all GPS points corresponding to rider 1 whose rider ID is ID001 from the rider ID in each GPS point;

[0064] S2. Arrange all GPS points of rider 1 in the order of recording time (timestamp), including the 1st GPS point, the 2nd GPS point, the 3rd GPS point, the 4th GPS point, the 5th GPS point, the 6th GPS point, the 7th GPS point, the 8th GPS point, the 9th GPS point and the 10th GPS point;

[0065] S3. Determine two GPS points that are adjacent in time: 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10, calculate the speed between all two adjacent points, if the speed between the first GPS point and the second GPS point is lower than the speed threshold, mark the first GPS point and the second GPS point as low-speed points; if the speed between the second GPS point and the third GPS point is not lower than the speed threshold, do not process it; if the speed between the third GPS point and the fourth GPS point is lower than the speed threshold, mark the third GPS point and the fourth GPS point as low-speed points; if the speed between the fourth GPS point and the fifth GPS point is lower than the speed threshold , the 4th and 5th GPS points are marked as low-speed points; if the speed between the 5th and 6th GPS points is not lower than the speed threshold, no processing is performed; if the speed between the 6th and 7th GPS points is not lower than the speed threshold, no processing is performed; if the speed between the 7th and 8th GPS points is lower than the speed threshold, the 7th and 8th GPS points are marked as low-speed points; if the speed between the 8th and 9th GPS points is lower than the speed threshold, the 8th and 9th GPS points are marked as low-speed points; if the speed between the 9th and 10th GPS points is not lower than the speed threshold, no processing is performed.

[0066] Therefore, the low-speed points obtained are: the first GPS point, the second GPS point, the third GPS point, the fourth GPS point, the fifth GPS point, the seventh GPS point, the eighth GPS point and the ninth GPS point.

[0067] S4. Determine the distance between two temporally adjacent low-speed points: 1-2, 2-3, 3-4, 4-5, 5-7, 7-8, 8-9. Any distance between two points less than 50m is classified into the same low-speed point cluster. Assuming the distance between 5-7 is no less than 50m, and the distances between all other points are less than 50m, then the consecutive points 1, 2, 3, 4, and 5 are classified into one low-speed point cluster (cluster 1), and the consecutive points 7, 8, and 9 are classified into one low-speed point cluster (cluster 2). Thus, two low-speed point clusters are obtained.

[0068] S5. Calculate the time difference between the earliest and latest points in each low-speed point group. If the time difference between the recorded time of point 5 and the recorded time of point 1 in group 1 is greater than 5 minutes, then select that low-speed point group (group 1) as the target low-speed point group. If the time difference between the recorded time of point 9 and the recorded time of point 7 in group 2 is less than 5 minutes, then group 2 is not processed.

[0069] S6, connect the outermost points in all points in group 1: points 1, 2, 3, 4 and 5 to form a geometric figure that can cover all points in group 1, and identify the center point of the geometric figure as the long-time resting point of the rider.

[0070] Step 3, generate a resting demand map according to the density of the long-time resting point of the rider in space;

[0071] In this embodiment, step 3 specifically comprises:

[0072] Step 31, analyze the spatial distribution density of the long-time resting point of the rider using a hot spot detection algorithm (Kernel Density Estimation, KDE); this step is to perform hot spot detection, and the hot spot detection technology has practical application value in identifying the long-time resting point of the rider. By analyzing the rider trajectory data, the rider's resting hot spot area can be found, which is of great significance for providing better resting facilities, optimizing city services and improving the working conditions of the rider, etc.

[0073] Step 32, generate a resting demand map covering the entire study area according to the analysis results of the spatial distribution density and using GIS tools (ArcGIS); this step is to visualize the long-time resting point of the rider, and KDE is a density-based spatial point pattern analysis technique that can be used to study spatial point clustering. In the identification of trajectory resting points, KDE visually expresses the change of the spatio-temporal clustering degree of trajectory points in a graphical manner. This method can visually display the change of the spatio-temporal clustering degree of trajectories in their life cycle and effectively extract trajectory resting. Through this method, the long-time resting point of the rider can be effectively visualized, so as to better understand and analyze the behavior pattern of the rider. Not only does it improve the accuracy of identification, but also enhances the interpretability of the results, so that non-professionals can also participate in the identification and analysis process of the resting point.

[0074] Step 4, based on the resting demand map and the construction feasibility evaluation system, preliminarily select the site of the delivery rider relay station, determine several feasible sites for the construction of the relay station, and form a set of site selection schemes;

[0075] In this embodiment, step 4 specifically comprises:

[0076] Step 41, divide the resting demand map into a plurality of m*m grids, the value of m is set by the user according to the demand, and record the resting demand value corresponding to each m*m grid, the resting demand value is the value corresponding to the position of the m*m grid on the resting demand map;

[0077] Step 42: Use the natural breakpoint method to divide each m*m grid into different levels of rest demand areas according to the rest demand value, namely high rest demand area, relatively high rest demand area, medium rest demand area, relatively low rest demand area, and low rest demand area. Steps 41 and 42 are for the purpose of identifying and grading the demand areas, identifying, classifying, and prioritizing different types of demand to ensure the rational allocation of resources, improve work efficiency, and optimize the decision-making process.

[0078] Step 43: Divide each determined high rest demand area into multiple n*n grids, where the value of n is set by the user according to needs and the value of n is smaller than the value of m;

[0079] Step 44: For each n*n grid, score it using the Likert five-level scale based on the construction feasibility evaluation system, arrange it in order from high to low, and select the first a n*n grids as feasible sites for the construction of food delivery rider stations to form a site selection plan set. The value of a is set by the user according to needs; Steps 43 and 44 are for the preliminary site selection of multiple points for food delivery rider stations at all levels to ensure that the location of the station can not only meet the core needs of the riders, but also optimize the riders' work experience.

[0080] The construction feasibility evaluation system includes two dimensions: space capacity and infrastructure support. The space capacity dimension includes two indicators: the volume of available space and transportation accessibility; the infrastructure support dimension includes two indicators: water supply facilities and power supply facilities.

[0081] Steps 41 and 44 are based on urban space research, which involves in-depth research on the city's current space, and include spaces that meet space capacity requirements and have conditions for water and electricity supply into the feasible site selection for food delivery rider stations.

[0082] Step 5: Construct a spatial suitability site selection evaluation system that integrates the psychological needs of riders, sort the construction priorities of several feasible sites within the site selection plan set, and then complete the precise site selection.

[0083] In this embodiment, step 5 specifically includes:

[0084] Step 51: Use the AHP analytic hierarchy process to construct a spatial suitability site selection evaluation system from the two dimensions of spatial environment and psychological environment;

[0085] Step 52: Based on the spatial suitability site selection evaluation system, use the Likert five-level scale to score several feasible sites in the site selection plan set, rank the several feasible sites according to the scores, and select the best feasible site with the highest score to ensure the practical operability and economic feasibility of the site selection.

[0086] Among them, the indicators of the spatial environment include traffic accessibility, noise decibels, green coverage rate and environmental cleanliness; the indicators of the psychological environment include pedestrian attitudes, merchant attitudes and manager attitudes; the indicators of the spatial environment and the psychological environment are quantified using the Likert five-level scale.

[0087] like Figure 2 As shown, an embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned trajectory- and space-based method for selecting a delivery rider's station location is implemented.

[0088] like Figure 3 As shown, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned trajectory and space-based takeaway rider station location selection method is implemented.

[0089] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0090] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0091] The above descriptions are only some embodiments of the present invention and do not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for selecting a delivery rider station location based on trajectory and space, characterized in that: The steps include: Step 1: Collect and pre-process rider trajectory data; the rider trajectory data is the GPS location of the rider when picking up and delivering food, and the GPS location includes the rider ID, longitude and latitude coordinates, and recording time; Step 2: For each rider, identify multiple low-speed points based on the speed between each two temporally adjacent rider trajectory data, find at least one low-speed point group based on the distance between each two temporally adjacent low-speed points, find a target low-speed point group based on the maximum time difference in each low-speed point group, and determine the rider's long-term rest stop point based on the target low-speed point group; specifically, the following steps are performed: Step 21: Find the GPS location of the same rider based on the rider ID; Step 22: Sort the GPS points of the same rider in the order of recording time; Step 23: For each GPS point corresponding to the rider, the moving speed between each two temporally adjacent GPS points is calculated based on the latitude and longitude coordinates and the recording time of the GPS point, and it is determined whether the moving speed between the two temporally adjacent GPS points is less than or equal to the speed threshold. If so, the two temporally adjacent GPS points are marked as low-speed points; otherwise, no processing is performed; Step 24: Determine whether the distance between the longitude and latitude coordinates corresponding to each two temporally adjacent low-speed points is less than a distance threshold. If so, classify the multiple consecutive low-speed points that meet the condition into the same low-speed point group to form at least one low-speed point group; otherwise, do not process the points. Step 25: Determine whether the time difference between the recording time corresponding to the earliest low-speed point and the latest low-speed point in each low-speed point group exceeds a time threshold. If so, use the low-speed point group as the target low-speed point group. Step 26: Connect the outermost low-speed points in the target low-speed point group to form a geometric figure, and select the center point of the geometric figure as the rider's long-term rest stop point; Step 3: Generate a rest demand map based on the spatial density of riders’ long-term rest stops; Step 4: Conduct preliminary site selection for the delivery rider station based on the rest demand map and construction feasibility evaluation system, determine several feasible sites for the station construction, and form a set of site selection plans; Step 5: Construct a spatial suitability site selection evaluation system that integrates the psychological needs of riders, sort the construction priorities of several feasible sites within the site selection plan set, and then complete the precise site selection.

2. The method for selecting a delivery rider station location based on trajectory and space according to claim 1, wherein: The data is preprocessed in step 1, specifically including: Step 11: Obtain rider trajectory data from the food delivery platform; Step 12: Use the Python library to clean and format the rider trajectory data; the cleaning includes removing outliers in the rider trajectory data, including abnormal GPS drift points, duplicate data, and missing data; Step 13: The cleaned and formatted rider trajectory data is used as a dataset and stored in a relational database.

3. The method for selecting a delivery rider station location based on trajectory and space according to claim 1, wherein: The step 3 specifically includes: Step 31: Use a hotspot detection algorithm to analyze the spatial distribution density of long-term rest stops for all riders; Step 32: Based on the analysis results of spatial distribution density, a recreation demand map covering the entire study area is generated using GIS tools.

4. The method for selecting a delivery rider station location based on trajectory and space according to claim 3, wherein: The step 4 specifically includes: Step 41: Divide the rest demand map into multiple m*m grids, where the value of m is set by the user according to needs, and record the rest demand value corresponding to each m*m grid. The rest demand value is the value corresponding to the position of the m*m grid on the rest demand map. Step 42: Using the natural breakpoint method, each m*m grid is divided into different levels of rest demand areas according to the rest demand value, namely, high rest demand area, relatively high rest demand area, medium rest demand area, relatively low rest demand area, and low rest demand area; Step 43: Divide each determined high rest demand area into multiple n*n grids, where the value of n is set by the user according to needs and the value of n is smaller than the value of m; Step 44: For each n*n grid, score it using the Likert five-level scale based on the construction feasibility evaluation system, arrange it in descending order of score, and select the first a n*n grids as feasible sites for the construction of food delivery rider stations to form a site selection plan set. The value of a is set by the user according to needs.

5. The method for selecting a delivery rider station location based on trajectory and space according to claim 4, wherein: The step 5 specifically includes: Step 51: Use the AHP analytic hierarchy process to construct a spatial suitability site selection evaluation system from the two dimensions of spatial environment and psychological environment; Step 52: Based on the spatial suitability site selection evaluation system, use the Likert five-level scale to score several feasible sites in the site selection plan set, rank the several feasible sites according to the scores, and select the feasible site with the highest score as the best location.

6. The method for selecting a delivery rider station location based on trajectory and space according to claim 5, wherein: The construction feasibility evaluation system includes two dimensions: space capacity and infrastructure support. The space capacity dimension includes two indicators: the volume of available space and transportation accessibility; the infrastructure support dimension includes two indicators: water supply facilities and power supply facilities. The indicators of the spatial environment include traffic accessibility, noise decibels, green coverage and environmental cleanliness; the indicators of the psychological environment include pedestrian attitudes, merchant attitudes and manager attitudes; the indicators of the spatial environment and the psychological environment are quantified using the Likert five-level scale.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the trajectory and space-based takeaway rider station location selection method as described in any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the trajectory and space-based takeaway rider station location selection method as described in any one of claims 1 to 6 is implemented.

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