Freight OD data acquisition method and device, medium and product

Abstract

The invention discloses a freight OD data acquisition method and device, a medium and a product, and relates to the field of road freight, and the method comprises the steps: determining a freight vehicle driving region based on to-be-processed freight vehicle trajectory data and a region road network; the freight vehicle driving area comprises a plurality of road sections; determining a freight vehicle parking node set based on the to-be-processed freight vehicle trajectory data; in the freight vehicle driving area, performing road section matching on track points in the to-be-processed freight vehicle track data to obtain a freight vehicle driving path; determining an OD point set based on the freight vehicle driving path and the freight vehicle parking node set; the OD point set comprises a travel starting point subset and a travel ending point subset, and points in the OD point set are all located on the road section. According to the invention, accurate freight OD data can be efficiently obtained.

Description

Technical Field

[0001] The present application relates to the field of road freight technology, and in particular to a method, device, medium and product for acquiring freight OD data. Background Art

[0002] Freight OD (Origin Destination) distribution is one of the key areas that the road freight industry needs to monitor and analyze. To improve the industry's regulatory service level and promote high-quality development of the industry, comprehensive and accurate freight OD data is needed. Traditional freight OD data is mainly obtained through traffic surveys, generally including roadside inquiries and questionnaires. These methods have problems such as high cost and low efficiency, which affect the accuracy of the obtained freight OD data. Summary of the invention

[0003] The purpose of this application is to provide a freight OD data acquisition method, device, medium and product, which can efficiently acquire accurate freight OD data.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for acquiring freight OD data, comprising:

[0006] Obtain the trajectory data of freight vehicles to be processed and the regional road network;

[0007] Based on the to-be-processed freight vehicle trajectory data and the regional road network, determining a freight vehicle driving area; the freight vehicle driving area includes a plurality of road sections;

[0008] Determine a set of parking nodes for freight vehicles based on the freight vehicle trajectory data to be processed;

[0009] In the freight vehicle driving area, performing road segment matching on the trajectory points in the freight vehicle trajectory data to be processed to obtain a freight vehicle driving path;

[0010] Based on the freight vehicle driving path and the freight vehicle parking node set, an OD point set is determined; the OD point set includes a travel starting point subset and a travel end point subset, and the points in the OD point set are all on the road section.

[0011] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for acquiring freight OD data.

[0012] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements a method for acquiring freight OD data when executed by a processor.

[0013] In a fourth aspect, the present application provides a computer program product, including a computer program, which implements a method for acquiring freight OD data when executed by a processor.

[0014] According to the specific embodiments provided by the present application, the present application has the following technical effects: the present application provides a method, device, medium and product for acquiring freight OD data, which determines the driving area of ​​the freight vehicle according to the freight vehicle trajectory data to be processed and the regional road network, thereby realizing the determination of the driving area, reducing the size of the area, avoiding the situation of processing a large amount of data in the subsequent matching process, and reducing the loss of computing resources; mining the application value of the freight vehicle trajectory data to be processed, and correspondingly determining the set of freight vehicle parking nodes; then, within the freight vehicle driving area, matching the trajectory points in the freight vehicle trajectory data to be processed with road sections to obtain an accurate freight vehicle driving path, and finally, based on the freight vehicle driving path and the set of freight vehicle parking nodes, the freight vehicle parking nodes are screened and classified to obtain a subset of travel starting points and a subset of travel end points. Compared with the manual survey in the prior art, the present application has high efficiency and higher accuracy in acquiring freight OD data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 This is an application environment diagram of a method for acquiring freight OD data in an embodiment of the present application;

[0017] Figure 2 A flow chart of a method for acquiring freight OD data provided in one embodiment of the present application;

[0018] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] The study found that freight vehicles are basically equipped with positioning software and connected to relevant information platforms, which makes it possible to obtain freight OD data based on vehicle trajectories, that is, to accurately obtain vehicle trajectory information through relevant information platforms, such as vehicle number, trajectory time, instantaneous position (latitude and longitude), instantaneous speed, direction angle, etc., and then extract various information related to vehicle travel from this information, such as freight OD point information, freight type, etc. On this basis, the freight OD data acquisition method, equipment, medium and product of this application are used to acquire freight OD data. On the one hand, it can further improve the existing monitoring and analysis system of the road freight industry, which is conducive to the relevant personnel to more systematically grasp the dynamics and laws of the spatiotemporal distribution of road freight; on the other hand, it can effectively overcome the drawbacks of high cost and low efficiency of traditional traffic surveys, and can improve the accuracy and scientificity of government management measures such as traffic planning and logistics planning, and improve the industry's governance capabilities and governance level.

[0021] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The freight OD data acquisition method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the to-be-processed freight vehicle trajectory data and the regional road network to the server 104, and the server 104 determines the freight vehicle driving area based on the to-be-processed freight vehicle trajectory data and the regional road network, determines the freight vehicle parking node set based on the to-be-processed freight vehicle trajectory data, and performs road segment matching on the trajectory points in the to-be-processed freight vehicle trajectory data in the freight vehicle driving area to obtain the freight vehicle driving path, and determines the OD point set based on the freight vehicle driving path and the freight vehicle parking node set. The server 104 can feedback the obtained OD point set to the terminal 102. In addition, in some embodiments, the freight OD data acquisition method can be implemented by the server 104 or the terminal 102 alone, or the server 104 can obtain the to-be-processed freight vehicle trajectory data and the regional road network from the data storage system, and process them to obtain the OD point set.

[0023] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0024] In an exemplary embodiment, Figure 2 As shown, a method for obtaining freight OD data is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 205.

[0025] Step 201, obtaining the track data of the freight vehicle to be processed and the regional road network. The steps for obtaining the track data of the freight vehicle to be processed include:

[0026] (11) Obtaining original freight vehicle trajectory data. The original freight vehicle trajectory data can be directly called from the relevant information platform, but there may be problems such as missing, duplication, and drift.

[0027] (12) Cleaning the original freight vehicle trajectory data; using data cleaning technology to identify and eliminate missing and duplicate problems in the original freight vehicle trajectory data.

[0028] (13) The original freight vehicle trajectory data after data cleaning is optimized by using a Kalman filter algorithm to obtain the freight vehicle trajectory data to be processed. That is, for the drift problem existing in the original freight vehicle trajectory data, the Kalman filter algorithm is used to optimize the trajectory point position by calculating the direction angle change and the average speed of the freight vehicle at each trajectory point to obtain the freight vehicle trajectory data to be processed, specifically including:

[0029] 131) For any current trajectory point in the original freight vehicle trajectory data after data cleaning, obtain the previous trajectory point corresponding to the current trajectory point; the data corresponding to the current trajectory point includes a preliminary observation value of the current angular state and a preliminary observation value of the current speed state, and the data corresponding to the previous trajectory point includes an estimated value of the previous angular state and an estimated value of the previous speed state.

[0030] 132) using a first prediction equation to calculate a current azimuth state prediction value based on the previous azimuth state estimation value and the current azimuth state preliminary observation value, wherein the first prediction equation takes into account a dynamic model of the freight vehicle movement.

[0031] 133) weighted fusion is performed on the current angular state prediction value and the current angular state preliminary observation value to obtain an optimized angular state. Specifically, a weighted coefficient is calculated based on the covariance matrix of the current angular state prediction value and the current angular state preliminary observation value, and then weighted fusion is performed.

[0032] 134) using a second prediction equation to calculate a current speed state prediction value based on the previous speed state estimation value and the current speed state preliminary observation value, wherein the second prediction equation takes into account a dynamic model of the freight vehicle movement.

[0033] 135) weighted fusion is performed on the current speed state prediction value and the current speed state preliminary observation value to obtain the optimized speed state. The specific weighting process is similar to the process of step 133) above. In actual application, the current speed state preliminary observation value corresponding to the current trajectory point is the average speed of the freight vehicle.

[0034] 136) Determine the optimized data corresponding to the current trajectory point according to the optimized direction angle state and the optimized speed state; the optimized data corresponding to all trajectory points constitute the freight vehicle trajectory data to be processed.

[0035] The above steps 132)-136) realize the optimization of the current trajectory point. When optimizing the next trajectory point, the current trajectory point is used as the corresponding previous trajectory point, the optimized angular state corresponding to the current trajectory point is used as the previous angular state estimate, and the optimized speed state corresponding to the current trajectory point is used as the previous speed state estimate, and then the above steps 132)-136) are repeated.

[0036] Step 202, based on the to-be-processed freight vehicle trajectory data and the regional road network, determine a freight vehicle driving area; the freight vehicle driving area includes a plurality of road sections.

[0037] In an application example, first, a spatial grid index of the regional road network is established to form a boundary and index ID system to facilitate subsequent data call and processing; secondly, a mapping relationship between the regional road grid and the road section is established, and a road section-grid relationship table is generated; then, the actual road section where the freight vehicle is located is determined based on the GPS longitude and latitude coordinates of each track point in the freight vehicle trajectory data to be processed, and then the actual grid where the freight vehicle is located is determined based on the road section-grid relationship table, and the map in the grid is used as the freight vehicle driving area.

[0038] Step 203: Determine a set of parking nodes for the freight vehicle based on the freight vehicle trajectory data to be processed.

[0039] In an application example, step 203 includes:

[0040] (31) Segment the to-be-processed freight vehicle trajectory data according to a preset time length to obtain a plurality of vehicle trajectory segments; in practical applications, the preset time length may be set to 20 minutes, and a plurality of continuous vehicle trajectory segments are obtained after segmentation.

[0041] (32) For any of the vehicle trajectory segments, the driving distance within the vehicle trajectory segment is calculated by the accumulation method; that is, the driving distance between consecutive trajectory points is calculated and recorded, and the driving distance within each vehicle trajectory segment is accumulated.

[0042] (33) Calculate an average driving speed within the vehicle segment based on the driving distance within the vehicle segment and the preset duration.

[0043] (34) Determine a set of parking nodes for freight vehicles based on the average driving speed in the vehicle segments corresponding to each of the vehicle trajectory segments. In practical applications, step (34) specifically includes:

[0044] 341) Based on the average driving speed in the vehicle segment corresponding to each of the vehicle trajectory segments, the parking speed threshold is determined by using the quantile method. For example, after the average driving speed in the vehicle segment corresponding to each of the vehicle trajectory segments is arranged in order of magnitude, ten quantiles are set, and the tenth quantile can be selected as the parking speed threshold. Then, the average driving speed in the vehicle segment corresponding to 90% of the vehicle trajectory segments is higher than the parking speed threshold, and the average driving speed in the vehicle segment corresponding to 10% of the vehicle trajectory segments is lower than the parking speed threshold.

[0045] 342) From the plurality of vehicle trajectory segments, select the vehicle trajectory segments whose average driving speed is less than the parking speed threshold and mark them as parking trajectory segments. Corresponding to step 341 above, if 10% of the vehicle trajectory segments correspond to vehicle segments whose average driving speed is less than the parking speed threshold, then the 10% of vehicle trajectory segments can be regarded as parking trajectory segments, in which the freight vehicles are parked for a long time.

[0046] 343) Marking the trajectory points in the parking trajectory segment as freight vehicle parking nodes; all the freight vehicle parking nodes constitute a freight vehicle parking node set.

[0047] Step 204 , within the freight vehicle driving area, performing road segment matching on the trajectory points in the freight vehicle trajectory data to be processed to obtain a freight vehicle driving path.

[0048] In one application example, step 204 includes:

[0049] (41) For each road section within the freight vehicle driving area, a preset scoring function is used to calculate the corresponding road section score; the road section score is used to characterize the road section length, road section type (such as main roads, branch roads, etc. with different weights) and the proximity between the road section and the nodes in the freight vehicle parking node set.

[0050] (42) Based on the hidden Markov model, the track points in the to-be-processed freight vehicle track data are searched and matched in the freight vehicle driving area to obtain all possible paths, and the total road segment score corresponding to each possible path is calculated. Each possible path includes at least one road segment. Specifically, when traversing all road segments in the freight vehicle driving area to determine all possible paths, a depth-first search or a breadth-first search algorithm can be used.

[0051] (43) The possible path with the maximum total road segment score is marked as the driving path of the freight vehicle. At this point, the matching of the road segment and the trajectory point of the freight vehicle is completed.

[0052] Step 205, determining an OD point set based on the freight vehicle driving path and the freight vehicle parking node set; the OD point set includes a travel starting point subset and a travel end point subset, and the points in the OD point set are all on the road section.

[0053] In an application example, step 205 includes:

[0054] (51) For any freight vehicle parking node in the freight vehicle parking node set, if the freight vehicle parking node is not on the road on which the freight vehicle is traveling, it indicates that the freight vehicle parking point is not successfully matched with the road network, and the freight vehicle parking node is deleted at this time.

[0055] (52) If the freight vehicle parking node is on the road where the freight vehicle is traveling, it indicates that the freight vehicle parking point is successfully matched with the road network. At this time, the freight vehicle parking node is temporarily stored in the OD set to be sorted.

[0056] (53) Calculate the distance interval between any two adjacent freight vehicle parking nodes in the OD set to be sorted.

[0057] (54) Calculate the average distance interval based on all the distance intervals in the OD set to be sorted.

[0058] (55) When the distance interval between the i-th freight vehicle parking node and the i-1-th freight vehicle parking node is greater than the average distance interval, the i-th freight vehicle parking node is marked as a travel destination; and a plurality of the travel destinations constitute a travel destination subset.

[0059] (56) When the distance interval between the i-th freight vehicle parking node and the i-1-th freight vehicle parking node is less than the average distance interval, the i-th freight vehicle parking node is marked as a travel starting point; multiple travel starting points constitute a travel starting point subset; the travel starting point subset and the travel end point subset constitute an OD point set.

[0060] Compared with the prior art, this application has the following advantages:

[0061] (1) This application studies the freight OD data extraction and automatic generation technology based on big data, which can effectively avoid the problems of low efficiency, high cost, and high error rate faced by manual investigation.

[0062] (2) This application studies freight OD data based on freight trajectory data, accurately analyzes the spatial distribution of freight space demand, and identifies major freight channels and key nodes, which is conducive to optimizing transportation logistics planning methods and improving the scientific nature of planning.

[0063] (3) This application fully explores the application value of freight vehicle trajectory data. By analyzing the vehicle OD data, combined with the application of OD data, it can comprehensively judge the operating rules and existing problems of the industry. It can also provide quantitative support for the research and formulation of relevant industry plans, and improve the level and efficiency of industry governance.

[0064] (4) This application can be used to characterize the characteristics of freight logistics. Combined with the layout of transportation facilities and the layout of industries, it can further analyze the matching and support between transportation facilities and industries and urban functional areas, thereby driving the coordinated optimization of transportation facilities and industrial structures and achieving dynamic and high-level matching between the two.

[0065] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for acquiring freight OD data is implemented.

[0066] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0067] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0068] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0070] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0071] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0072] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for acquiring freight OD data, characterized in that: The freight OD data acquisition method comprises: Obtain the trajectory data of freight vehicles to be processed and the regional road network; Based on the to-be-processed freight vehicle trajectory data and the regional road network, determining a freight vehicle driving area; the freight vehicle driving area includes a plurality of road sections; Determine a set of parking nodes for freight vehicles based on the freight vehicle trajectory data to be processed; In the freight vehicle driving area, performing road segment matching on the trajectory points in the freight vehicle trajectory data to be processed to obtain a freight vehicle driving path; Based on the freight vehicle driving path and the freight vehicle parking node set, an OD point set is determined; the OD point set includes a travel starting point subset and a travel end point subset, and the points in the OD point set are all on the road section.

2. The method for acquiring freight OD data according to claim 1, characterized in that: Obtain the trajectory data of freight vehicles to be processed, including: Obtaining original freight vehicle trajectory data; Performing data cleaning on the original freight vehicle trajectory data; The original freight vehicle trajectory data after data cleaning is optimized by using a Kalman filter algorithm to obtain the freight vehicle trajectory data to be processed.

3. The method for acquiring freight OD data according to claim 2, characterized in that: The original freight vehicle trajectory data after data cleaning is optimized by using a Kalman filter algorithm to obtain the freight vehicle trajectory data to be processed, including: For any current track point in the original freight vehicle track data after data cleaning, obtain the previous track point corresponding to the current track point; the data corresponding to the current track point includes a preliminary observation value of the current direction angle state and a preliminary observation value of the current speed state, and the data corresponding to the previous track point includes an estimated value of the previous direction angle state and an estimated value of the previous speed state; Using a first prediction equation, according to the previous azimuth state estimation value and the current azimuth state preliminary observation value, a current azimuth state prediction value is calculated; Performing weighted fusion on the predicted value of the current azimuth state and the preliminary observed value of the current azimuth state to obtain an optimized azimuth state; Using a second prediction equation, according to the previous speed state estimation value and the current speed state preliminary observation value, a current speed state prediction value is calculated; Performing weighted fusion on the current speed state prediction value and the current speed state preliminary observation value to obtain an optimized speed state; According to the optimized direction angle state and the optimized speed state, the optimized data corresponding to the current trajectory point is determined; the optimized data corresponding to all trajectory points constitute the freight vehicle trajectory data to be processed.

4. The method for acquiring freight OD data according to claim 1, characterized in that: Determining a set of parking nodes for freight vehicles based on the freight vehicle trajectory data to be processed includes: Segmenting the to-be-processed freight vehicle trajectory data according to a preset time length to obtain a plurality of vehicle trajectory segments; For any of the vehicle trajectory segments, calculating the travel distance within the vehicle segment by accumulation method; Calculating an average driving speed within the vehicle segment based on the driving distance within the vehicle segment and the preset duration; A set of parking nodes for freight vehicles is determined based on the average driving speed in the vehicle segments corresponding to each of the vehicle trajectory segments.

5. The method for acquiring freight OD data according to claim 4, characterized in that: Determining a set of parking nodes for freight vehicles based on average driving speeds in vehicle segments corresponding to each of the vehicle trajectory segments includes: Determining a parking speed threshold by using a quantile method based on an average driving speed in a vehicle segment corresponding to each of the vehicle trajectory segments; Selecting, from the plurality of vehicle trajectory segments, a vehicle trajectory segment whose average driving speed is less than the parking speed threshold and marking it as a parking trajectory segment; The trajectory points within the parking trajectory segment are marked as freight vehicle parking nodes; all the freight vehicle parking nodes constitute a freight vehicle parking node set.

6. The method for acquiring freight OD data according to claim 1, characterized in that: In the freight vehicle driving area, performing road segment matching on the trajectory points in the freight vehicle trajectory data to be processed to obtain the freight vehicle driving path, including: For each road section in the freight vehicle driving area, a preset scoring function is used to calculate the corresponding road section score; the road section score is used to characterize the road section length, road section type and the proximity between the road section and the nodes in the freight vehicle parking node set; Based on the hidden Markov model, the track points in the to-be-processed freight vehicle track data are searched and matched within the freight vehicle driving area to obtain all possible paths, and the total road segment score corresponding to each possible path is calculated; The possible path with the largest total road segment score is marked as the freight vehicle travel path.

7. The method for acquiring freight OD data according to claim 1, characterized in that: Determining an OD point set based on the freight vehicle driving path and the freight vehicle parking node set includes: For any freight vehicle parking node in the freight vehicle parking node set, if the freight vehicle parking node is not on the freight vehicle driving road, then delete the freight vehicle parking node; If the freight vehicle parking node is on the road where the freight vehicle is traveling, the freight vehicle parking node is temporarily stored in the OD set to be sorted; Calculate the distance interval between any two adjacent freight vehicle parking nodes in the OD set to be sorted; Calculate the average distance interval according to all the distance intervals in the OD set to be sorted; When the distance interval between the i-th freight vehicle parking node and the i-1-th freight vehicle parking node is greater than the average distance interval, the i-th freight vehicle parking node is marked as a travel destination; a plurality of the travel destinations constitute a travel destination subset; When the distance interval between the i-th freight vehicle parking node and the i-1-th freight vehicle parking node is less than the average distance interval, the i-th freight vehicle parking node is marked as a travel starting point; multiple travel starting points constitute a travel starting point subset; the travel starting point subset and the travel end point subset constitute an OD point set.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the freight OD data acquisition method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for acquiring freight OD data described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for acquiring freight OD data described in any one of claims 1 to 7 is implemented.

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