CN114943305A

CN114943305A - Data processing method, data processing device, storage medium and server

Info

Publication number: CN114943305A
Application number: CN202210725134.4A
Authority: CN
Inventors: 陈冲冲; 石立臣; 强成仓; 廖泽平
Original assignee: Shenzhen Yishi Huolala Technology Co Ltd
Current assignee: Shenzhen Yishi Huolala Technology Co Ltd
Priority date: 2022-06-24
Filing date: 2022-06-24
Publication date: 2022-08-26

Abstract

The embodiment of the application discloses a data processing method, a data processing device, a storage medium and a server. The method comprises the following steps: based on the order data and the driver operation data, restricting the time and the position of the track points in the driving track data, and determining a plurality of candidate track points from the driving track data; clustering the candidate track points to obtain a plurality of cluster clusters; based on the order data and the driver operation data, constraining the positions of cluster center points of the plurality of clustering clusters, and determining candidate clusters from the plurality of clustering clusters; and (4) restraining the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining a target starting point or a target end point of the order from the track points in the candidate cluster. According to the scheme, the clustering algorithm and the strategy are used for identifying the actual loading and unloading points, so that the accuracy of real position point mining in a freight scene can be improved.

Description

Data processing method, data processing device, storage medium and server

Technical Field

The present application relates to the field of electronic computer technologies, and in particular, to a data processing method and apparatus, a storage medium, and a server.

Background

Most of the existing track mining schemes relate to the recognition algorithm research of a stopping point, the research of a taxi boarding and alighting place in the passenger transportation industry and the research of a long-distance freight car loading and unloading point. The research on the identification algorithm of the loading and unloading points of the trucks in the same city freight scene is still in a vacant state at present, but the demand of the same city freight industry for the identification of the loading and unloading points is more and more urgent.

For a three-party platform, a typical transportation track of the same-city freight transport is that a driver confirms order taking at the platform, confirms the order with a customer telephone, then goes to an order starting point according to a appointed time, stops nearby the order starting point for a period of time, confirms arrival at the platform and confirms an actual loading point with the customer telephone, then goes to the actual loading point and stays for a period of time for loading, then goes to an order terminal, and also stays for a period of time after arriving at a destination, confirms arrival at the platform and confirms an actual unloading point with the customer telephone, then goes to the actual unloading point and stays for a period of time for unloading. After the unloading is finished, the next round of transportation route is prepared to start from the starting point of the next order. It can be known that improving the ratio of the excavation point to the real loading and unloading point is of great significance for improving the surface contact efficiency.

At present, a three-party platform user places an order on the platform, partial orders fill a starting point planning route according to the order placing of the user after the driver takes the order, and the user needs to communicate with the driver through a telephone for manual navigation before the order reaches the starting point, so that the communication cost is high when the driver and the user meet each other. In the analysis of driver track data, the driver track comes from driver positioning, and the track loss rate is about 5%; secondly, the proportion that the driver confirms that the loading points are real loading points is 51 percent, and the proportion that the driver confirms that the unloading points are real unloading points is 49 percent; thirdly, the proportion of the real loading point at the ordering starting point of the user is 13 percent, and the proportion of the real loading point at the ordering ending point of the user is 7 percent. It can be known that the accuracy of loading and unloading position information in the order information of the existing scheme is poor.

Disclosure of Invention

The embodiment of the application provides a data processing method, a data processing device, a storage medium and a server, which can improve the accuracy of real position point mining.

In a first aspect, an embodiment of the present application provides a data processing method, applied in a freight scenario, including:

acquiring order data and corresponding driving track data;

constraining the time and the position of a track point in the driving track data based on order data and driver operation data, and determining a plurality of candidate track points from the driving track data;

clustering the candidate track points to obtain a plurality of clustering clusters;

based on the order data and the driver operation data, constraining the positions of cluster center points of the plurality of cluster clusters, and determining candidate clusters from the plurality of cluster clusters;

and restricting the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining the target starting point of the order from the track points in the candidate cluster.

In a second aspect, an embodiment of the present application provides another data processing method, which is applied in a freight scenario, and includes:

acquiring order data and corresponding driving track data;

and restricting the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining the target end point of the order from the track points in the candidate cluster.

In a third aspect, an embodiment of the present application provides another data processing apparatus, which is applied in a freight scenario, and includes:

the first acquisition unit is used for acquiring order data and corresponding driving track data;

the first determining unit is used for constraining the time and the position of track points in the driving track data based on order data and driver operation data and determining a plurality of candidate track points from the driving track data;

the first clustering unit is used for clustering the candidate track points to obtain a plurality of clustering clusters;

a second determining unit, configured to constrain positions of cluster center points of the plurality of clusters based on the order data and the driver operation data, and determine a candidate cluster from the plurality of clusters;

and the third determining unit is used for restricting the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining the target starting point of the order from the track points in the candidate cluster.

In a fourth aspect, an embodiment of the present application provides another data processing apparatus, which is applied in a freight scenario, and includes:

the second acquisition unit is used for acquiring order data and corresponding driving track data;

the fourth determining unit is used for restricting the time and the position of the track point in the driving track data based on the order data and the driver operation data and determining a plurality of candidate track points from the driving track data;

the second clustering unit is used for clustering the candidate track points to obtain a plurality of clustering clusters;

a fifth determining unit, configured to constrain positions of cluster center points of the plurality of clusters based on the order data and the driver operation data, and determine a candidate cluster from the plurality of clusters;

and the sixth determining unit is used for constraining the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining the target end point of the order from the track points in the candidate cluster.

In a fifth aspect, the present application further provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are adapted to be loaded by a processor to execute the data processing method described above.

In a sixth aspect, an embodiment of the present application further provides a server, which includes a processor and a memory, where the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used to execute the data processing method.

According to the method and the device, the time and the position of the track point in the driving track data are restrained based on the order data and the driver operation data, and a plurality of candidate track points are determined from the driving track data; clustering the candidate track points to obtain a plurality of clustering clusters; based on the order data and the driver operation data, the positions of cluster center points of the plurality of clustering clusters are constrained, and candidate clusters are determined from the plurality of clustering clusters; and (4) restraining the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining a target starting point or a target end point of the order from the track points in the candidate cluster. According to the scheme, the clustering algorithm and the strategy are used for identifying the actual loading and unloading points, so that the accuracy of real position point mining in a freight scene can be improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flowchart of a data processing method according to an embodiment of the present application.

Fig. 2 is a schematic structural diagram of a loading and unloading point excavation system according to an embodiment of the present application.

Fig. 3 is another schematic flow chart of a data processing method according to an embodiment of the present application.

Fig. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Fig. 7 is another schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

At present, the loading and unloading points are basically identified based on truck monitoring data to analyze the speed, the load and the direction of the truck, so that the loading and unloading points are identified, specific behaviors of a driver are identified, the cargo allocation information is pushed for the driver, and intelligent cargo allocation is realized. However, in the same-city freight service scenario where the three-party freight platform issues the order, the data that can be generally acquired includes driver positioning information, driver operation information, and other order information. The proportion of the loading points confirmed by the operation of the driver to be the real loading points is 51%, the proportion of the loading points at the unloading points is only 49%, the proportion of the loading points at the ordering starting point of the user is 13%, and the proportion of the loading points at the ordering ending point of the user is 7%. Therefore, new methods are needed for mining real load and unload points based on available data. Meanwhile, the higher the proportion of the actual loading and unloading points is, the higher the proportion of accurate loading and unloading points recommended to the user is, which has important significance for providing loading and unloading point recommendation service for the invoice issuing user, improving the vehicle and cargo meeting efficiency and improving the riding experience.

In order to improve the collision efficiency of driver goods, reduce collision cost and enable more drivers and users to select and store on the platform, a method for excavating actual loading and unloading goods points is needed, so that a real loading and unloading goods point database is generated, and loading and unloading goods point recommendation service is provided for the users. Based on this, the embodiment of the application provides a data processing method, a data processing device, a storage medium and a server, which are used for identifying and mining real loading and unloading goods points by combining user order data, driver operation data and driving track data, and can improve the fixed point rate in the freight order receiving process.

In one embodiment, a data processing method is provided in an application server. Referring to fig. 1, a specific flow of the data processing method may be as follows:

101. and acquiring order data and corresponding driving track data.

The scheme is applied to the freight scene. Specifically, the order data refers to data related to a shipping requirement initiated by a user through a shipping APP, an applet or a web page installed in the electronic device, for example, basic information of the user (such as name and contact information), basic information of shipping (such as a shipping start point, a shipping end point, a shipping time, a cargo detail, and the like).

102. And based on the order data and the driver operation data, restricting the time and the position of the track point in the driving track data, and determining a plurality of candidate track points from the driving track data. The travel track data refers to actual travel route data and travel time data in the current freight process.

In an embodiment, when the time and the position of the track point in the travel track data are constrained based on the order data and the driver operation data, and a plurality of candidate track points are determined from the travel track data, the following process may be specifically included:

determining a starting point road section of the order track according to the driver operation data, the time of each track point in the driving track data and a first time constraint condition;

and determining a plurality of candidate track points from the starting road section according to the order data, the driver operation data, the positions of the track points in the starting road section, the time of the track points in the starting road section, the first distance constraint condition and the second time constraint condition.

In one embodiment, the driver operational data includes: the driver confirms the order pickup time and the driver confirms the arrival starting time. When the starting point road section of the order track is determined according to the driver operation data, the time of each track point in the track data and the first time constraint condition, the track point corresponding to the order receiving time confirmed by the driver and the track point in the specified time period before the driver confirms to reach the starting point can be selected as the starting point road section of the order track.

In one embodiment, the order data includes: the order starting point, driver operational data includes: the driver confirms the order taking time, the driver confirms the arrival starting time and the driver confirms the loading point. When determining a plurality of candidate track points from the starting point road segment according to the order data, the driver operation data, the positions of the track points in the starting point road segment, the time of the track points in the starting point road segment, the first distance constraint condition, and the second time constraint condition, the following process may be specifically included:

screening track points meeting a first distance constraint condition from a starting point road section based on the distance between each track point in the driving track data and an order starting point or the distance between each track point in the driving track data and a loading confirmation point of a driver;

and determining the track points with the time difference meeting the second time constraint condition from the screened track points as candidate track points based on the time difference between the screened track points and the time for confirming the loading time of the driver and the time difference between the screened track points and the time for confirming the arrival starting point time of the driver.

103. And clustering the candidate track points to obtain a plurality of cluster clusters.

Specifically, the loading and unloading candidate track points can be clustered by a density clustering based method DBSCAN, wherein the minimum sample size n is 2, and the epsilon-neighborhood radius eps is 0.00009.

104. And based on the order data and the driver operation data, constraining the positions of cluster center points of the plurality of cluster clusters, and determining candidate clusters from the plurality of cluster clusters.

In one embodiment, the order data may include: an order starting point, the driver operational data comprising: the driver confirms the loading completion point. When the positions of the cluster center points of the plurality of clusters are constrained based on the order data and the driver operation data, and a candidate cluster is determined from the plurality of clusters, the method may specifically include the following steps:

acquiring a first distance between a cluster center point and an order starting point and a second distance between the cluster center point and a loading completion point confirmed by a driver;

sorting the plurality of clustering clusters according to the first distance and the second distance respectively to obtain a first sorting result and a second sorting result;

screening cluster clusters with the ordering meeting the first ordering constraint condition from the plurality of cluster clusters according to the first ordering result and the second ordering result respectively;

and taking the screened cluster and the cluster where the loading completion point is confirmed by the driver as candidate clusters.

105. And (4) restraining the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining a target starting point of the order from the track points in the candidate cluster.

In an embodiment, when determining the target starting point of the order from the track points in the candidate cluster, a third distance between each track point in the candidate cluster and the starting point of the order, a fourth distance between each track point in the candidate cluster and the confirmed loading point of the driver, and a fifth distance between each track point in the candidate cluster and the confirmed loading completion point of the driver may be respectively obtained; and acquiring a first ratio of the stay time to the third distance, a second ratio of the stay time to the fourth distance, and a third ratio of the stay time to the fifth distance. And finally, determining a target starting point of the order from the track points in the candidate cluster based on the first ratio, the second ratio and the third ratio.

In an embodiment, when the target starting point of the order is determined from the track points in the candidate cluster based on the first ratio, the second ratio and the third ratio, the track points in the candidate cluster may be scored based on the first ratio, the second ratio, the third ratio and a preset scoring policy. And then, according to the scoring result, selecting track points with the highest score from the track points in the candidate clusters, and determining the track points as target starting points of the order.

Similarly, in one embodiment, the above logic strategy may be referenced for mining and identifying a track order target endpoint. That is, another data processing method is also provided, which specifically may be as follows:

acquiring order data and corresponding driving track data;

based on the order data and the driver operation data, restricting the time and the position of track points in the driving track data, and determining a plurality of candidate track points from the driving track data;

based on the order data and the driver operation data, constraining the positions of cluster center points of the plurality of clustering clusters, and determining candidate clusters from the plurality of clustering clusters;

and (4) restricting the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining the target end point of the order from the track points in the candidate cluster.

Specifically, when mining the target endpoint, the order data may include: user basic information (such as name and contact), and basic freight information (such as freight starting point, freight ending point, freight time, freight details, and the like). The user operation data may include: the driver confirms the arrival terminal time, the driver confirms the completion time, the driver confirms the information of the unloading point, and the driver confirms the information of the unloading completion point.

As can be seen from the above, the data processing method provided in this embodiment constrains the time and the position of the trajectory point in the travel trajectory data based on the order data and the driver operation data, and determines a plurality of candidate trajectory points from the travel trajectory data; clustering the candidate track points to obtain a plurality of clustering clusters; based on the order data and the driver operation data, constraining the positions of cluster center points of the plurality of clustering clusters, and determining candidate clusters from the plurality of clustering clusters; and (4) restraining the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining a target starting point or a target end point of the order from the track points in the candidate cluster. According to the scheme, the clustering algorithm and the strategy are used for identifying the actual loading and unloading points, so that the accuracy of real position point mining in a freight scene can be improved.

In yet another embodiment of the present application, a virtual load and unload point excavation system is also provided. Referring to fig. 2, the system architecture may include: an electronic device, a communication device, and a server device. The communication equipment service is used for communicating the server with the terminal equipment and providing a data interaction link; the communication device may be implemented by, but is not limited to, the following devices: wireless network (WiFi/4G/5G), wired network, satellite communication, etc.

Specifically, the user can use the electronic device to perform interactive operation of data transmission and data reception with the server through the communication device. Software programs can be operated in the server and the electronic equipment to realize tasks such as data sending, data receiving, data processing, data displaying, model building, model prediction and the like.

The electronic device includes, but is not limited to, a computer, a mobile phone, a tablet and other intelligent terminal devices, and may receive data from the server device through the communication device. The electronic equipment can be provided with a freight APP, a user can place an order through the APP to form a freight order, and a driver can receive the freight order through the APP arranged in the electronic equipment and reach a freight starting point to execute freight operation according to order information. In this embodiment, a server generally refers to a server facility, which may be a single independent server or a server cluster, and may implement model construction and deployment by running a corresponding program in the server.

The server provides basic service capability through system software and application software, and on the basis, the server provides mining capability of actual cargo loading and unloading points of freight. In this scenario, referring to fig. 3, the server may implement the following functions:

step one, cleaning and processing data

(11) Track data segmentation: the later time when the driver confirms the order taking time and the time 20min before the driver confirms the arrival of the starting point is taken as the starting point of the order track, the time 20min after the driver confirms the order taking time and the earlier time 20min after the driver confirms the unloading completion are taken as the end points of the order track, and then the order data and the track data are correlated through the driver ID and the time.

Specifically, the driver track data is stored in time, and the track data di is { driver _ id, inactivoni, ti }, i is 1, 2, …, m, where driver _ id is the driver code, inactivoni is { loni, lati }, loni is the longitude of a position ti (e.g., "2021-12-1713: 13: 13") at a certain time, and lati is the latitude of the position ti at the certain time; order data order { order _ id, driver _ id, o _ order _ t, o _ order _ location, o _ associated _ t, o _ loading _ t, o _ loading _ location, o _ loaded _ t, o _ loaded _ location, d _ order _ t, d _ order _ location, d _ associated _ t, d _ loading _ t, d _ unassembled _ location, d _ complete _ location }, wherein order _ id is the driver's code, o _ order _ t, o _ order _ location are the user's order entry starting time and the user's order entry starting position, o _ associated _ t, o _ load _ t, o _ loading _ t, o _ loading _ location, o _ loading _ end time, user's order entry, user's entry, user's entry, user's entry, and entry's entry, user's entry, user's entry, and entry's entry, and entry's entry ' and entry's entry's entry ' and entry's, Unload start time and location, order completion time and location. The later time when the driver confirms the order taking time and the time before the driver confirms the arrival of the order (such as 20min) is taken as the starting point of the order track, the time when a period of time (such as 20min) elapses after the driver confirms the order taking time and the earlier time when the driver confirms the time (such as 20min) after the unloading is finished are taken as the ending point of the order track, and then the order data and the track data are correlated through the driver ID and the time.

(12) Due to the problems of the positioning accuracy and the signal intensity of the GPS, the track points in partial driver tracks drift and the like. Because the track data acquisition has certain time interval, whether the proportion of the sum of the distance between the track point and the front and rear track points and the linear distance between the front and rear track points is reasonable or not can be judged by calculating the track points. If the ratio exceeds the set ratio, the track data drifts, and the noise data is regarded as being removed.

(13) And (4) recording the operation positions and time of the user and the driver in the order data, and excluding data of which the operation time does not accord with the operation sequence. For example, the driver confirms that the arrival time at the starting point is later than the shipment start time, which is not logical in the operational sequence.

(14) Sorting the loaded and unloaded goods on the track: and (3) screening, wherein the distance from a single starting point of a user to a next starting point or a certain distance range (such as 500m) of a loading point confirmed by a driver is within a certain time, and the time difference between the track point and the loading time and the arrival starting point time confirmed by the driver is within a specified time (such as 10min), and marking the track point as a loading point candidate track point. Similarly, screening is within a certain distance range (such as 500m) from the user ordering terminal point or the driver confirms the unloading point, and the track point time is marked as the unloading point candidate track point within a specified time (such as 10min) from the unloading time confirmed by the driver and the arrival terminal point time.

Step two, judging the loading and unloading goods points

(21) And respectively clustering the loading and unloading candidate track points by using a density clustering-based method DBSCAN, wherein the minimum sample volume n is 2, the epsilon-neighborhood radius eps is 0.00009, the point cluster center point is used as a candidate point position, and entering a subsequent scoring stage.

(22) Because some tracks exist, drivers find the way, track points are repeated after the interval is long, and if the stay time is calculated by using the time difference value of the point positions in the cluster, an error exists. Therefore, assuming that the trajectory acquisition interval time is n seconds, the dwell time can be calculated by multiplying the number of point clusters by n seconds.

(23) Selecting the first three point locations with the closest distance between the cluster center point and the driver confirmation loading completion point, the point location with the closest distance between the cluster center point and the user ordering starting point and the driver confirmation loading completion point as candidate point locations of the actual loading point; and finishing the selection of the candidate point positions of the actual unloading points in the same way.

(24) Each candidate point is scored according to max { the stay time of the candidate point/(the distance between the candidate point and the starting point for ordering by the user) },/(/ (/) the stay time of the candidate point/(the distance between the candidate point and the starting point for confirming the loading by the driver) }, and the point with the highest score is taken as the excavation point of the loading point. And similarly, judging the unloading points, namely max { the stay time of the candidate point positions/(the distance between the candidate point position and the user ordering end point), the stay time of the candidate point positions/(the distance between the candidate point position and the driver confirmed unloading start point), the stay time of the candidate point positions/(the distance between the point position and the driver confirmed unloading end point) }, scoring each candidate point position according to the same strategy, and taking the point position with the highest score as the excavating point of the unloading point.

Step three, effect evaluation

(31) And calculating the distance between the manually marked loading and unloading point and the loading and unloading point output by the mining algorithm.

(32) And calculating the fixed point rate of each distance. The loading site fixed point rate is the ratio of the number of points within a certain range (such as 30m) of the distance between the loading site excavation point and the loading site manual marking point to the number of all marking points; the unloading site fixed point rate is the ratio of the number of points within a certain range (such as 30m) between the unloading site excavation point and the unloading site manual marking point to the number of all marking points.

Therefore, the scheme combines the user order data, the driving track data and the driver operation data to identify and mine the real loading point and the unloading point, and can improve the fixed point rate in the freight receiving process.

In another embodiment of the present application, a data processing apparatus is also provided. The data processing means may be integrated in the server in the form of software or hardware. As shown in fig. 4, the data processing apparatus 200 may include: a first obtaining unit 201, a first determining unit 202, a first clustering unit 203, a second determining unit 204, and a third determining unit 205, wherein:

a first obtaining unit 201, configured to obtain order data and corresponding travel track data;

the first determining unit 202 is configured to constrain the time and the position of a track point in the travel track data based on order data and driver operation data, and determine a plurality of candidate track points from the travel track data;

the first clustering unit 203 is configured to cluster the plurality of candidate track points to obtain a plurality of clustering clusters;

a second determining unit 204, configured to constrain positions of cluster center points of the multiple clusters based on the order data and the driver operation data, and determine a candidate cluster from the multiple clusters;

and a third determining unit 205, configured to constrain a duration of a driver staying on each track point in the candidate cluster and a position of each track point in the candidate cluster, and determine a target starting point of the order from the track points in the candidate cluster.

In an embodiment, the first determining unit 202 is configured to:

and determining a plurality of candidate track points from the starting road section according to the order data, the driver operation data, the positions of the track points in the starting road section, the time of the track points in the starting road section, a first distance constraint condition and a second time constraint condition.

In one embodiment, the driver operating data includes: the driver confirms the order receiving time and the driver confirms the time to reach the starting point; when determining a starting point road segment of the order track according to the driver operation data, the time of each track point in the driving track data, and the first time constraint condition, the first determining unit 202 is specifically configured to:

and selecting track points corresponding to the order taking confirmation time of the driver and track points in a specified time period before the driver confirms to reach the starting point as the starting point road section of the order track based on the time of each track point in the driving track data.

In one embodiment, the order data includes: an order starting point, the driver operational data comprising: the driver confirms the order receiving time, the driver confirms the arrival starting time and the driver confirms the loading point; when determining a plurality of candidate track points from the starting point road segment according to the order data, the driver operation data, the positions of the track points in the starting point road segment, the time of the track points in the starting point road segment, the first distance constraint condition, and the second time constraint condition, the first determining unit 202 is specifically configured to:

based on the distance between each track point in the driving track data and the order starting point or the distance between each track point in the driving track data and the loading point confirmed by the driver, selecting track points meeting a first distance constraint condition from the starting point road section;

and determining the track point with the time difference meeting the second time constraint condition from the screened track point based on the time difference between the screened track point and the confirmation loading time of the driver and the time difference between the screened track point and the confirmation arrival starting point time of the driver, and taking the track point as the candidate track point.

In one embodiment, the order data includes: an order starting point, the driver operational data comprising: the driver confirms the loading completion point; the second determination unit 204 is configured to:

acquiring a first distance between a cluster center point and an order starting point and a second distance between the cluster center point and a loading completion confirming point of a driver;

and taking the screened cluster and the cluster where the unloading completion point is confirmed by the driver as candidate clusters.

In an embodiment, the third determining unit 205 is configured to:

respectively obtaining a third distance between each track point in the candidate cluster and an order starting point, a fourth distance between each track point in the candidate cluster and a driver confirmation loading point, and a fifth distance between each track point in the candidate cluster and a driver confirmation loading completion point;

acquiring a first ratio of the stay time to the third distance, a second ratio of the stay time to the fourth distance, and a third ratio of the stay time to the fifth distance;

and determining a target starting point of the order from the track points in the candidate cluster based on the first ratio, the second ratio and the third ratio.

In an embodiment, when determining the target starting point of the order from the track points in the candidate cluster based on the first ratio, the second ratio, and the third ratio, the third determining unit 205 is further configured to:

scoring the track points in the candidate clusters based on the first ratio, the second ratio, the third ratio and a preset scoring strategy;

and according to the scoring result, screening track points with the highest score from the track points in the candidate clusters, and determining the track points as the target starting points of the order.

As can be seen from the above, the data processing apparatus provided in the embodiment of the present application restricts the time and the position of the trajectory point in the travel trajectory data based on the order data and the driver operation data, and determines a plurality of candidate trajectory points from the travel trajectory data; clustering the candidate track points to obtain a plurality of clustering clusters; based on the order data and the driver operation data, constraining the positions of cluster center points of the plurality of clustering clusters, and determining candidate clusters from the plurality of clustering clusters; and (4) restraining the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining a target starting point of the order from the track points in the candidate cluster. According to the scheme, the actual loading points are identified by using a clustering algorithm and a strategy, so that the accuracy of real position point mining in a freight scene can be improved.

In another embodiment of the present application, a data processing apparatus is also provided. The data processing means may be integrated in the server in the form of software or hardware. As shown in fig. 5, the data processing apparatus 300 may include: a second obtaining unit 301, a fourth determining unit 302, a second clustering unit 303, a fifth determining unit 304, and a sixth determining unit 305, wherein:

a first obtaining unit 301, configured to obtain order data and corresponding travel track data;

a fourth determining unit 302, configured to constrain time and positions of trajectory points in the travel trajectory data based on order data and driver operation data, and determine a plurality of candidate trajectory points from the travel trajectory data;

the second clustering unit 303 is configured to cluster the multiple candidate track points to obtain multiple clustering clusters;

a fifth determining unit 304, configured to constrain positions of cluster center points of the multiple clusters based on the order data and the driver operation data, and determine a candidate cluster from the multiple clusters;

a sixth determining unit 305, configured to constrain a duration of the driver staying at each trace point in the candidate cluster and a position of each trace point in the candidate cluster, and determine a target starting point of the order from the trace points in the candidate cluster.

In an embodiment, the fourth determining unit 302 is configured to:

determining a terminal road section of the order track according to the driver operation data, the time of each track point in the driving track data and a first time constraint condition;

and determining a plurality of candidate track points from the terminal road section according to the order data, the driver operation data, the positions of the track points in the terminal road section, the time of the track points in the terminal road section, a first distance constraint condition and a second time constraint condition.

In one embodiment, the driver operating data includes: the driver confirms the order completion time and the driver confirms the arrival terminal time; when determining the terminal road segment of the order track according to the driver operation data, the time of each track point in the driving track data, and the first time constraint condition, the fourth determining unit 302 is specifically configured to:

and selecting track points corresponding to the time when the driver confirms the order and track points in a specified time period before the driver confirms the order as the terminal road section of the order track based on the time of each track point in the driving track data.

In one embodiment, the order data includes: an order endpoint, the driver operational data comprising: the driver confirms the order completion time, the driver confirms the arrival terminal time and the driver confirms the unloading point; when determining a plurality of candidate track points from the end point road segment according to the order data, the driver operation data, the positions of the track points in the end point road segment, the time of the track points in the end point road segment, the first distance constraint condition, and the second time constraint condition, the fourth determining unit 302 is specifically configured to:

screening track points meeting a first distance constraint condition from the terminal road section based on the distance between each track point in the driving track data and the order terminal or the distance between each track point in the driving track data and the unloading point confirmed by the driver;

and determining the track point with the time difference meeting the second time constraint condition from the screened track point based on the time difference between the screened track point and the unloading confirmation time of the driver and the time difference between the screened track point and the terminal arrival time of the driver, and taking the track point as the candidate track point.

In one embodiment, the order data includes: an order endpoint, the driver operational data comprising: the driver confirms the unloading completion point; the fifth determining unit 304 is configured to:

acquiring a first distance between a cluster center point and an order end point and a second distance between the cluster center point and a unloading completion point confirmed by a driver;

screening cluster clusters with the ordering meeting a first ordering constraint condition from the plurality of cluster clusters according to the first ordering result and the second ordering result respectively;

In an embodiment, the sixth determining unit 305 is configured to:

respectively obtaining a third distance between each track point in the candidate cluster and an order end point, a fourth distance between each track point in the candidate cluster and a confirmed unloading point of a driver, and a fifth distance between each track point in the candidate cluster and a confirmed unloading completion point of the driver;

and determining a target end point of the order from the track points in the candidate cluster based on the first ratio, the second ratio and the third ratio.

In an embodiment, when determining the target end point of the order from the track points in the candidate cluster based on the first ratio, the second ratio, and the third ratio, the sixth determining unit 305 is further configured to:

and according to the scoring result, screening track points with the highest score from the track points in the candidate cluster, and determining the track points as the target end points of the order.

As can be seen from the above, the data processing apparatus provided in the embodiment of the present application restricts the time and position of the trajectory point in the travel trajectory data based on the order data and the driver operation data, and determines a plurality of candidate trajectory points from the travel trajectory data; clustering the candidate track points to obtain a plurality of clustering clusters; based on the order data and the driver operation data, constraining the positions of cluster center points of the plurality of clustering clusters, and determining candidate clusters from the plurality of clustering clusters; and (4) restraining the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining the target end point of the order from the track points in the candidate cluster. According to the scheme, the actual unloading point is identified by using a clustering algorithm and a strategy, so that the accuracy of digging the real position point in a freight scene can be improved.

In yet another embodiment of the present application, a server is also provided. As shown in fig. 6, the server 400 includes a processor 401 and a memory 402. The processor 401 is electrically connected to the memory 402.

The processor 401 is a control center of the server 400, connects various parts of the entire server using various interfaces and lines, performs various functions of the server and processes data by running or loading an application stored in the memory 402 and calling data stored in the memory 402, thereby performing overall monitoring of the server.

In an embodiment, the processor 401 in the server 400 loads instructions corresponding to processes of one or more applications into the memory 402, and the processor 401 executes the applications stored in the memory 402, so as to implement various functions as follows:

acquiring order data and corresponding driving track data;

In one embodiment, when the time and position of the trajectory point in the travel trajectory data are constrained based on the order data and the driver operation data, and a plurality of candidate trajectory points are determined from the travel trajectory data, the processor 401 may perform the following operations:

In one embodiment, the driver operating data includes: the driver confirms the order receiving time and the driver confirms the time to reach the starting point; when determining the starting point road segment of the order track according to the driver operation data, the time of each track point in the driving track data, and the first time constraint condition, the processor 401 may perform the following operations:

In one embodiment, the order data includes: an order starting point, the driver operational data comprising: the driver confirms the order receiving time, the driver confirms the arrival starting time and the driver confirms the unloading point; when determining a plurality of candidate track points from the starting road segment according to the order data, the driver operation data, the position of each track point in the starting road segment, the time of each track point in the starting road segment, the first distance constraint condition, and the second time constraint condition, the processor 401 may perform the following operations:

screening track points meeting a first distance constraint condition from the starting point road section based on the distance between each track point in the driving track data and the starting point of the order or the distance between each track point in the driving track data and the unloading point confirmed by the driver;

and determining the track point with the time difference meeting the second time constraint condition from the screened track point as the candidate track point based on the time difference between the screened track point and the unloading confirmation time of the driver and the time difference between the screened track point and the arrival starting point time of the driver.

In one embodiment, the order data includes: an order starting point, the driver operational data comprising: the driver confirms the unloading completion point; when the positions of the cluster center points of the plurality of clusters are constrained based on the order data and the driver operation data, and candidate clusters are determined from the plurality of clusters, the processor 401 may perform the following operations:

In an embodiment, when the time and the position of the trajectory point in the travel trajectory data are constrained based on the order data and the driver operation data, and a plurality of candidate trajectory points are determined from the travel trajectory data, the processor 401 may specifically perform the following operations:

In one embodiment, the driver operating data includes: the driver confirms the order receiving time and the driver confirms the time to reach the starting point; when determining the starting point road segment of the order track according to the driver operation data, the time of each track point in the driving track data, and the first time constraint condition, the processor 401 may specifically perform the following operations:

Alternatively, in an embodiment, the processor 401 in the server 400 loads instructions corresponding to processes of one or more applications into the memory 402 according to the following steps, and the processor 401 runs the applications stored in the memory 402, so as to implement various functions:

acquiring order data and corresponding driving track data;

The memory 402 may be used to store applications and data. The memory 402 stores applications containing instructions executable in the processor. Applications may constitute various functional modules. The processor 401 executes various functional applications and data processing by running applications stored in the memory 402.

In some embodiments, as shown in fig. 7, the server 400 further comprises: a display 403, a control circuit 404, a radio frequency circuit 405, an input unit 406, and a power supply 407. The processor 401 is electrically connected to the display 403, the control circuit 404, the rf circuit 405, the input unit 406, and the power source 407.

The display screen 403 may be used to display information entered by the user or provided to the user for various graphical user interfaces of the server, which may be composed of images, text, icons, video, and any combination thereof.

The control circuit 404 is electrically connected to the display 403, and is configured to control the display 403 to display information.

The rf circuit 405 is configured to transmit and receive rf signals, so as to establish wireless communication with an electronic device or other server through wireless communication, and transmit and receive signals with the electronic device or other server.

The input unit 406 may be used to receive input numbers, character information, or user characteristic information (e.g., fingerprint), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. The input unit 406 may include a fingerprint recognition module.

The power supply 407 is used to supply power to the various components of the server 400. In some embodiments, the power supply 407 may be logically coupled to the processor 401 via a power management system, such that the power management system may perform functions of managing charging, discharging, and power consumption.

Although not shown in fig. 7, the server 400 may further include a speaker, a bluetooth module, a camera, etc., which are not described in detail herein.

As can be seen from the above, the server provided in the embodiment of the present application, based on the order data and the driver operation data, constrains the time and the position of the trajectory point in the travel trajectory data, and determines a plurality of candidate trajectory points from the travel trajectory data; clustering the candidate track points to obtain a plurality of cluster clusters; based on the order data and the driver operation data, constraining the positions of cluster center points of the plurality of clustering clusters, and determining candidate clusters from the plurality of clustering clusters; and (4) restraining the stay time of the driver on each track point in the candidate cluster and the position of each track point in the candidate cluster, and determining a target starting point or a target end point of the order from the track points in the candidate cluster. According to the scheme, the clustering algorithm and the strategy are used for identifying the actual loading and unloading points, so that the accuracy of digging the real position points in the freight scene can be improved.

In some embodiments, there is also provided a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor to perform any of the data processing methods described above.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.

The data processing method, the data processing apparatus, the storage medium, and the server provided in the embodiments of the present application are described in detail above, and a specific example is applied in the description to explain the principles and the embodiments of the present application, and the description of the embodiments above is only used to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A data processing method is applied to a freight scene, and is characterized by comprising the following steps:

acquiring order data and corresponding driving track data;

2. The data processing method according to claim 1, wherein the determining a plurality of candidate trajectory points from the travel trajectory data by constraining the time and position of the trajectory points in the travel trajectory data based on the order data and the driver operation data comprises:

3. The data processing method of claim 2, wherein the driver operation data comprises: the driver confirms the order receiving time and the driver confirms the time to reach the starting point;

determining a starting point road section of the order track according to the driver operation data, the time of each track point in the driving track data and a first time constraint condition, wherein the method comprises the following steps:

4. The data processing method of claim 2, wherein the order data comprises: an order starting point, the driver operational data comprising: the driver confirms the order receiving time, the driver confirms the arrival starting time and the driver confirms the loading point;

determining a plurality of candidate track points from the starting point road section according to the order data, the driver operation data, the positions of the track points in the starting point road section, the time of the track points in the starting point road section, a first distance constraint condition and a second time constraint condition, and including:

5. The data processing method of claim 1, wherein the order data comprises: an order starting point, the driver operational data comprising: the driver confirms the loading completion point;

the constraining the positions of cluster center points of the plurality of clusters based on the order data and the driver operation data, and determining candidate clusters from the plurality of clusters, comprising:

6. The data processing method according to any one of claims 1 to 5, wherein the determining the target starting point of the order from the track points in the candidate cluster by constraining the stay time of the driver on the track points in the candidate cluster and the positions of the track points in the candidate cluster comprises:

respectively obtaining a third distance between each track point in the candidate cluster and an order starting point, a fourth distance between each track point in the candidate cluster and a driver cargo confirmation point, and a fifth distance between each track point in the candidate cluster and a driver cargo confirmation completion point;

7. The data processing method of claim 6, wherein determining the target starting point of the order from the track points in the candidate cluster based on the first ratio, the second ratio, and the third ratio comprises:

8. A data processing method is applied to a freight scene, and is characterized by comprising the following steps:

acquiring order data and corresponding driving track data;

based on the order data and the driver operation data, the positions of cluster center points of the plurality of clusters are restrained, and candidate clusters are determined from the plurality of clusters;

9. A computer-readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor to perform the data processing method of any of claims 1-8.

10. A server, comprising a processor and a memory, wherein the processor is electrically connected to the memory, and the memory is used for storing instructions and data; the processor is configured to perform the data processing method of any one of claims 1-8.