CN111611046A - Travel data visualization method, device, device and readable storage medium - Google Patents

Abstract

The invention discloses a travel data visualization method, device, equipment and readable storage medium. The method includes: collecting a plurality of travel orders of a subject, wherein the travel orders at least include amount data, location data and time data; Classify and generate the sub-subject amount information of each sub-subject in the subject; according to the location data in each travel order, cluster each travel order to generate multiple travel location information; according to the amount information of each sub-subject, multiple travel locations Information and time data in each travel order to generate a visual view of the subject's travel data. In the present invention, the travel expenses of each sub-subject are represented by the amount information of the sub-subjects in the visual view; the travel between employees in the subject in terms of location and time is represented by a plurality of travel location information and time data in each travel order. The demand information realizes the in-depth and efficient analysis of the main travel data in multiple directions.

Description

Travel data visualization method, device, device and readable storage medium

technical field

The present invention relates to the technical field of financial technology (Fintech), and in particular, to a travel data visualization method, device, device and readable storage medium.

Background technique

With the continuous development of financial technology (Fintech), especially Internet technology finance, more and more technologies (such as artificial intelligence, big data, cloud storage, visualization, etc.) are applied in the financial field. Higher requirements are put forward, such as requiring accurate and efficient analysis of the travel data of enterprise employees.

During the operation of the enterprise, the travel data of the employees of the enterprise is usually collected and analyzed on a regular basis to control the travel expenses of various departments in the enterprise. However, this type of summary analysis often pays too much attention to the level of total travel expenses, while ignoring the travel demand information among employees, lacking more in-depth accurate analysis. In addition, the current summary is realized by manual operation with the help of a table tool, which takes a long time and is inefficient. Therefore, how to conduct in-depth and efficient analysis of enterprise travel data is a technical problem that needs to be solved urgently.

SUMMARY OF THE INVENTION

The main purpose of the present invention is to provide a travel data visualization method, device, device and readable storage medium, which aims to solve the technical problem of how to conduct in-depth and efficient analysis of enterprise travel data in the prior art.

In order to achieve the above object, the present invention provides a travel data visualization method, and the travel data visualization method includes the following steps:

Collect multiple travel orders of the subject, wherein the travel orders include at least amount data, location data and time data;

Classifying each of the amount data, and generating sub-subject amount information of each sub-subject in the subject;

According to the location data in each of the travel orders, cluster each of the travel orders to generate a plurality of travel location information;

According to the amount information of each of the sub-subjects, a plurality of the travel location information, and the time data in each of the travel orders, a visual view of the travel data of the subject is generated.

Optionally, the step of generating a visualization view of the travel data of the subject according to the amount information of each of the sub-subjects, a plurality of the travel location information and the time data in each of the travel orders includes:

generating the amount information of each of the sub-subjects into a sub-subject table view, and generating a timeline view and an order table view based on the sub-subject table view;

generating a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view and a projection view, respectively, according to a plurality of the travel location information and the time data in each of the travel orders;

The sub-subject table view, time axis view, order table view, map view, chord diagram, source-destination comparison view, parallel coordinate view and projection view are jointly generated as the travel data visualization view of the subject.

Optionally, the step of respectively generating a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view and a projection view according to a plurality of the travel location information and the time data in each of the travel orders includes:

According to the time data in each of the travel orders, the plurality of travel location information is divided into subject location information and a plurality of residential location information, and a plurality of residential location information and the main body location information are respectively generated. flow information;

Based on a preset map, generating the subject location information, a plurality of the residential location information and the flow direction information into a map view;

generating a chord diagram according to the subject position information and a plurality of the residence position information, and generating a source-destination comparison view based on the chord diagram;

Acquiring order attributes of each of the travel orders, and constructing a parallel coordinate view according to the order attributes of each of the travel orders;

According to the location data and time data in each of the travel orders, each of the travel orders is projected to generate a projection map.

Optionally, the step of generating a timeline view and an order table view based on the sub-subject table view includes:

When receiving a sub-subject selection instruction sent based on the sub-subject table view, determining a target sub-subject corresponding to the sub-subject selection instruction;

Obtain the target travel order quantity in the sub-subject amount information corresponding to the target sub-subject, and determine the daily order distribution information and time of the target sub-subject according to the travel time of each travel order corresponding to the target travel order quantity Order distribution information;

generating the daily order distribution information and the hourly order distribution information into a timeline view, and when receiving a screening instruction for the timeline view, determining a screening period corresponding to the screening instruction;

The travel information of each travel order corresponding to the screening period in the target sub-subject is obtained, and the travel information of each travel order is generated into an order table view.

Optionally, the step of generating a chord diagram according to the subject position information and a plurality of the residence position information includes:

constructing the subject position information into a subject position graphic of a preset shape, and constructing a plurality of the living position information into a preset shape living position graphic respectively;

splicing the main body position graph and a plurality of the residence position graphs into a chord diagram frame, wherein the main body position graph includes first travel order distribution information corresponding to the main body position information within a preset time period, The plurality of residential location graphs include second travel order distribution information corresponding to the plurality of residential location information within a preset time period;

According to the travel order corresponding to the subject location information and the travel orders corresponding to the plurality of residential location information, the first travel order distribution information and the plurality of second travel order distribution information are respectively associated to form The relationship between the subject position information and a plurality of the living position information in the chord diagram frame is obtained, so as to obtain the chord diagram.

Optionally, the step of generating a source-destination comparison view based on the chord diagram includes:

When receiving the comparison instruction sent based on the chord diagram, determining the target residence location information corresponding to the comparison instruction;

The correlation relationship corresponding to the target residential location information in the chord diagram is determined, and a source-destination comparison view between the target residential location information and the subject location information is generated according to the corresponding correlation relationship.

Optionally, the step of clustering each of the travel orders according to the location data in each of the travel orders, and generating a plurality of travel location information includes:

According to the location data in each of the travel orders, calculate the location similarity of each of the travel orders respectively;

According to the location similarity of each of the travel orders, the travel orders are clustered to generate a plurality of travel location information.

Optionally, the step of classifying each of the amount data and generating the sub-subject amount information of each sub-subject in the main body includes:

determining the sub-subject attributes of a plurality of the travel orders in the main body, classifying the amount data of the travel orders with the same sub-subject attributes into one category, and generating sub-subject amount data of each sub-subject in the main body;

Count the number of data items containing the sub-subject amount data for each sub-subject, obtain the travel order volume of each sub-subject, and add and calculate the sub-subject amount data of each sub-subject to obtain the total travel amount of each sub-subject;

Calculate the total travel amount of each of the sub-subjects according to the travel order amount of each of the sub-subjects, and obtain the average order amount of each of the sub-subjects;

The sub-subject name, the total travel amount, the travel order amount and the average value of the order amount of each of the sub-subjects are generated as the sub-subject amount information of each of the sub-subjects.

Further, in order to achieve the above object, the present invention also provides a travel data visualization device, and the travel data visualization device includes:

a collection module, configured to collect multiple travel orders of the subject, wherein the travel orders at least include amount data, location data and time data;

A classification module, configured to classify each of the amount data, and generate the sub-subject amount information of each sub-subject in the main body;

a clustering module, configured to cluster each of the travel orders according to the location data in each of the travel orders to generate a plurality of travel location information;

A generating module, configured to generate a visual view of the travel data of the subject according to the amount information of each of the sub-subjects, a plurality of the travel location information, and time data in each of the travel orders.

Further, in order to achieve the above object, the present invention also provides a travel data visualization device, the travel data visualization device includes a memory, a processor, and a travel data visualization stored on the memory and running on the processor. A program that, when executed by the processor, implements the steps of the method for visualizing travel data as described above.

Further, in order to achieve the above object, the present invention also provides a readable storage medium on which a travel data visualization program is stored, and when the travel data visualization program is executed by the processor, the travel as described above is realized. Steps of a data visualization method.

The travel data visualization method, device, device and computer-readable storage medium of the present invention realize in-depth and efficient analysis of the subject data by visualizing the travel data of the subject. First, collect multiple travel orders of the subject, and each travel order includes at least amount data, location data and time data; then classify the various amounts of data to obtain the sub-subject amount information of each sub-subject in the subject; and based on The location data in each travel order is used to cluster each travel order, and the travel orders with similar locations are clustered into one category, and multiple travel location information is obtained from the location in each cluster; and then based on the amount information of each sub-subject, Multiple travel location information and time data in each travel order to generate a visual view of the subject's travel data. The sub-subject amount information in the visual view is used to represent the total travel cost and average travel cost of each sub-subject, so as to realize automatic summarization of expenses and improve the efficiency of summarization. In addition, multiple travel location information and time data in each travel order are used to reflect the travel demand information between employees in the main body in terms of location and time, and then provide common travel suggestions for each employee, which is conducive to saving public travel resources. It is convenient for employees to travel quickly, and at the same time, it reduces the travel cost of the main body, and realizes the multi-faceted in-depth analysis of the main travel data.

Description of drawings

1 is a schematic structural diagram of a device hardware operating environment involved in an embodiment of a travel data visualization device according to the present invention;

FIG. 2 is a schematic flowchart of the first embodiment of the travel data visualization method of the present invention;

3 is a schematic diagram of functional modules of a preferred embodiment of the travel data visualization device of the present invention;

4 is a schematic diagram of a travel data visualization view formed by the travel data visualization method of the present invention;

5 is a schematic diagram of a department table view formed by the travel data visualization method of the present invention;

6 is a schematic diagram of a projection map formed by the travel data visualization method of the present invention;

7 is a schematic diagram of a time axis view formed by the travel data visualization method of the present invention;

8 is a schematic diagram of a map view formed by the travel data visualization method of the present invention;

9 is a schematic diagram of a source-destination comparison view formed by the travel data visualization method of the present invention;

10 is a schematic diagram of a chord diagram formed by the travel data visualization method of the present invention;

11 is a schematic diagram of a parallel coordinate view formed by the travel data visualization method of the present invention;

12 is a schematic diagram of an order table view formed by the travel data visualization method of the present invention.

The realization, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

The present invention provides a travel data visualization device. Referring to FIG. 1 , FIG. 1 is a schematic structural diagram of a device hardware operating environment involved in an embodiment of the travel data visualization device of the present invention.

As shown in FIG. 1 , the travel data visualization device may include: a processor 1001 , such as a CPU, a communication bus 1002 , a user interface 1003 , a network interface 1004 , and a memory 1005 . Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. Optionally, the network interface 1004 may include a standard wired interface and a wireless interface (eg, a WI-FI interface). The memory 1005 may be high-speed RAM memory, or may be non-volatile memory, such as disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001 .

Those skilled in the art can understand that the hardware structure of the travel data visualization device shown in FIG. 1 does not constitute a limitation on the travel data visualization device, and may include more or less components than those shown in the figure, or combine some components, Or a different component arrangement.

As shown in FIG. 1 , the memory 1005 as a readable storage medium may include an operating system, a network communication module, a user interface module, and a travel data visualization program. The operating system is a program that manages and controls travel data visualization equipment and software resources, and supports the operation of network communication modules, user interface modules, travel data visualization programs, and other programs or software; the network communication module is used to manage and control the network interface 1004 ; The user interface module is used to manage and control the user interface 1003.

In the hardware structure of the travel data visualization device shown in FIG. 1 , the network interface 1004 is mainly used to connect to the backend server and perform data communication with the backend server; the user interface 1003 is mainly used to connect the client (client) and perform data communication with the client. Communication; the processor 1001 can invoke the trip data visualization program stored in the memory 1005 and perform the following operations:

Collect multiple travel orders of the subject, wherein the travel orders include at least amount data, location data and time data;

Classifying each of the amount data, and generating sub-subject amount information of each sub-subject in the subject;

According to the location data in each of the travel orders, cluster each of the travel orders to generate a plurality of travel location information;

According to the amount information of each of the sub-subjects, a plurality of the travel location information, and the time data in each of the travel orders, a visual view of the travel data of the subject is generated.

Further, the step of generating a visualization view of the travel data of the subject according to the amount information of each of the sub-subjects, a plurality of the travel location information and the time data in each of the travel orders includes:

generating the amount information of each of the sub-subjects into a sub-subject table view, and generating a timeline view and an order table view based on the sub-subject table view;

generating a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view and a projection view, respectively, according to a plurality of the travel location information and the time data in each of the travel orders;

The sub-subject table view, time axis view, order table view, map view, chord diagram, source-destination comparison view, parallel coordinate view and projection view are jointly generated as the travel data visualization view of the subject.

Further, the step of respectively generating a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view and a projection view according to a plurality of the travel location information and the time data in each of the travel orders includes:

According to the time data in each of the travel orders, the plurality of travel location information is divided into subject location information and a plurality of residential location information, and a plurality of residential location information and the main body location information are respectively generated. flow information;

Based on a preset map, generating the subject location information, a plurality of the residential location information and the flow direction information into a map view;

generating a chord diagram according to the subject position information and a plurality of the residence position information, and generating a source-destination comparison view based on the chord diagram;

Acquiring order attributes of each of the travel orders, and constructing a parallel coordinate view according to the order attributes of each of the travel orders;

According to the location data and time data in each of the travel orders, each of the travel orders is projected to generate a projection map.

Further, the step of generating a timeline view and an order table view based on the sub-subject table view includes:

When receiving a sub-subject selection instruction sent based on the sub-subject table view, determining a target sub-subject corresponding to the sub-subject selection instruction;

Obtain the target travel order quantity in the sub-subject amount information corresponding to the target sub-subject, and determine the daily order distribution information and time of the target sub-subject according to the travel time of each travel order corresponding to the target travel order quantity Order distribution information;

generating the daily order distribution information and the hourly order distribution information into a timeline view, and when receiving a screening instruction for the timeline view, determining a screening period corresponding to the screening instruction;

The travel information of each travel order corresponding to the screening period in the target sub-subject is obtained, and the travel information of each travel order is generated into an order table view.

Further, the step of generating a chord diagram according to the subject position information and a plurality of the residence position information includes:

constructing the subject position information into a subject position graphic of a preset shape, and constructing a plurality of the living position information into a preset shape living position graphic respectively;

splicing the main body position graph and a plurality of the residence position graphs into a chord diagram frame, wherein the main body position graph includes first travel order distribution information corresponding to the main body position information within a preset time period, The plurality of residential location graphs include second travel order distribution information corresponding to the plurality of residential location information within a preset time period;

According to the travel order corresponding to the subject location information and the travel orders corresponding to the plurality of residential location information, the first travel order distribution information and the plurality of second travel order distribution information are respectively associated to form The relationship between the subject position information and a plurality of the living position information in the chord diagram frame is obtained, so as to obtain the chord diagram.

Further, the step of generating a source-destination comparison view based on the chord diagram includes:

When receiving the comparison instruction sent based on the chord diagram, determining the target residence location information corresponding to the comparison instruction;

The correlation relationship corresponding to the target residential location information in the chord diagram is determined, and a source-destination comparison view between the target residential location information and the subject location information is generated according to the corresponding correlation relationship.

Further, the step of clustering each of the travel orders according to the location data in each of the travel orders to generate a plurality of travel location information includes:

According to the location data in each of the travel orders, calculate the location similarity of each of the travel orders respectively;

According to the location similarity of each of the travel orders, the travel orders are clustered to generate a plurality of travel location information.

Further, the step of classifying each of the amount data and generating the sub-subject amount information of each sub-subject in the main body includes:

determining the sub-subject attributes of a plurality of the travel orders in the main body, classifying the amount data of the travel orders with the same sub-subject attributes into one category, and generating sub-subject amount data of each sub-subject in the main body;

Count the number of data items containing the sub-subject amount data for each sub-subject, obtain the travel order volume of each sub-subject, and add and calculate the sub-subject amount data of each sub-subject to obtain the total travel amount of each sub-subject;

Calculate the total travel amount of each of the sub-subjects according to the travel order amount of each of the sub-subjects, and obtain the average order amount of each of the sub-subjects;

The sub-subject name, the total travel amount, the travel order amount and the average value of the order amount of each of the sub-subjects are generated as the sub-subject amount information of each of the sub-subjects.

The specific implementation of the travel data visualization device of the present invention is basically the same as the embodiments of the following travel data visualization methods, and will not be repeated here.

The invention also provides a travel data visualization method.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of the first embodiment of the travel data visualization method of the present invention.

Embodiments of the present invention provide embodiments of a method for visualizing travel data. It should be noted that, although a logical sequence is shown in the flowchart, in some cases, the sequence shown or the sequence shown here may be performed in a different sequence. described steps. Specifically, the travel data visualization method in this embodiment includes:

Step S10, collecting multiple travel orders of the subject, wherein the travel orders at least include amount data, location data and time data;

The travel data visualization method in this embodiment is applied to the control center of the server or the client, and is suitable for visualizing the travel data of the subject through the control center of the server or the client, so as to analyze each subordinate of the subject according to the visualized travel data. The number of trips and travel costs of the subject. Among them, if applied to the server, the server and the client are connected in communication, and the client transmits the collected travel data to the server for visualization processing. If the application is the control center of the client, the control center will visualize the travel data uploaded through the client interface. Considering that each sub-subject in the subject is distributed in different regions, it is preferably applied to the server, that is, to visualize each travel data of the subject through the server. In addition, the subject can be an enterprise, government or institution, and each department in the enterprise, government or institution is each sub-subject in the subject. This embodiment is preferably described by taking an enterprise and its department as an example, and the visualized travel data in this embodiment can be used to analyze the travel location of each employee in the enterprise in addition to analyzing the travel times and travel expenses of each department in the enterprise. According to the travel location, provide each employee with travel carpool suggestions, set up shuttle stops, shuttle routes, etc. Among them, travel data comes from travel and transportation reimbursement vouchers provided by employees in the enterprise, such as invoices for public transportation such as taxis, subways, and buses, including time, address, and amount. The transportation reimbursement voucher for each trip of an employee is a travel order, and the travel orders of each employee in the enterprise are collected; and each travel order collected contains at least the amount data, location data and time data to reflect the employees respectively. The amount divided for each trip, the starting location, ending location of the trip, and the starting time and arrival time of the trip.

Step S20, classifying each of the amount data, and generating sub-subject amount information of each sub-subject in the subject;

Further, the amount data contained in each travel order is classified according to the department where the employee is located, and the departmental amount information of each department is obtained. The department amount information includes at least the department name of each department, the total amount of travel, the amount of travel orders and the average value of the order amount, so as to use this type of information in the visual view to represent the total travel cost of each department and the travel of employees in each department. The number of times and the average cost of each time. Specifically, the steps of classifying each amount data and generating the departmental amount information of each department of the enterprise include:

Step S21: Determine the sub-subject attributes of a plurality of the travel orders in the main body, classify the amount data of the travel orders with the same sub-subject attributes into one category, and generate the sub-subject amount of each sub-subject in the main body data;

Step S22, count the number of data items including the sub-subject amount data for each sub-subject, obtain the travel order amount of each sub-subject, and add and calculate the sub-subject amount data of each sub-subject to obtain the total travel amount of each sub-subject;

Step S23: Calculate the total travel amount of each of the sub-subjects according to the amount of travel orders of each of the sub-subjects, and obtain the mean value of the order amount of each of the sub-subjects;

Step S24, generating the sub-subject amount information of each of the sub-subjects as the sub-subject name, the total travel amount, the travel order amount, and the average value of the order amount of each of the sub-subjects.

Further, in addition to specific travel information representing specific travel information, each employee's travel order also includes information including the employee's attributes in the enterprise, such as the employee's job number, department name, and the like. Read the department name in the travel order as the department attribute of the employee in the enterprise, and compare the department attributes of each employee to determine the employees with the same department attribute, and then divide the respective amount data of the employees with the same department attribute into one category. After the division of the amount data in each employee's travel order is completed, the departmental amount data of each department in the enterprise is obtained.

Further, count and count the departmental amount data contained in each department, and obtain the number of data items containing departmental amount data in each department. A departmental amount data comes from a travel order, and the number of data items in the departmental amount data is the department travel. order volume. In addition, the sum of the amount data of each department is calculated, and the travel amount represented by the amount data of each department in each department is added to obtain the total travel amount of each department. After that, the calculation is made between the total travel amount of each department and the respective travel order amount, and the total travel amount of each department is divided by the respective travel order amount to obtain the average value of the order amount of each department. Average spend on travel orders. Then, the department name, total travel amount, travel order amount, and average order amount of each department are generated as department amount information. Through the visual display of the amount information of each department, the travel expenses of each department are reflected.

Step S30, according to the location data in each of the travel orders, cluster each of the travel orders to generate a plurality of travel location information;

Furthermore, according to the location data in each travel order, each travel order is clustered, and the travel orders with similar locations are grouped into one category, and a plurality of travel location information is obtained from the positions in each cluster. Specifically, according to the location data in each travel order, each travel order is clustered, and the steps of generating a plurality of travel location information include:

Step S31, according to the position data in each of the travel orders, respectively calculate the position similarity of each of the travel orders in the position;

Step S32, according to the location similarity of each of the travel orders, cluster each of the travel orders to generate a plurality of travel location information.

Further, a preset clustering algorithm for clustering is preset, such as the geo-cluster [3] algorithm. The location information in each travel order is clustered by a preset clustering algorithm to determine the location similarity of each travel order. The location similarity reflects the distance between the daily travel locations of employees. If the location similarity of each travel order in the travel location is high, it means that the employees with this type of travel order travel close in location on a daily basis, such as from community a to office building b.

Further, according to the location similarity of each travel order, the travel orders are clustered, and the travel orders whose location similarity is higher than the preset threshold are grouped into one category to obtain multiple travel order categories. Further, a plurality of travel location information is determined from the location data included in each type of travel order. A type of travel order corresponds to a travel location information, representing a geographical location, where the range of the geographical location is determined by the clustering accuracy of the clustering algorithm, such as clustering for a range of 500m, or clustering for a range of 1000m.

Step S40, generating a visualization view of the travel data of the subject according to the amount information of each of the sub-subjects, a plurality of the travel location information, and time data in each of the travel orders.

Further, after obtaining the departmental amount information and multiple travel location information of each department in the enterprise, a visualization view of the travel data of the enterprise is generated according to the time data, the amount information of each department, and the multiple location information in each employee's travel order. The travel data visualization view reflects the travel situation of each department, the aggregation of the residence of each employee, and the travel time of each employee. Then, based on the visual view, it provides the basis for the management and control of travel expenses of various departments, and provides suggestions for the travel of employees.

The travel data visualization method of the present invention realizes in-depth and efficient analysis of the subject data by visualizing the subject travel data. First, collect multiple travel orders of the subject, and each travel order includes at least amount data, location data and time data; then classify the various amounts of data to obtain the sub-subject amount information of each sub-subject in the subject; and based on The location data in each travel order is used to cluster each travel order, and the travel orders with similar locations are clustered into one category, and multiple travel location information is obtained from the location in each cluster; and then based on the amount information of each sub-subject, Multiple travel location information and time data in each travel order to generate a visual view of the subject's travel data. The sub-subject amount information in the visual view is used to represent the total travel cost and average travel cost of each sub-subject, so as to realize automatic summarization of expenses and improve the efficiency of summarization. In addition, multiple travel location information and time data in each travel order are used to reflect the travel demand information between employees in the main body in terms of location and time, and then provide common travel suggestions for each employee, which is conducive to saving public travel resources. It is convenient for employees to travel quickly, and at the same time, it reduces the travel cost of the main body, and realizes the multi-faceted in-depth analysis of the main travel data.

Further, based on the first embodiment of the travel data visualization method of the present invention, a second embodiment of the travel data visualization method of the present invention is proposed.

The difference between the second embodiment of the method for visualizing travel data and the first embodiment of the method for visualizing travel data is: The step of generating a visual view of the travel data of the subject includes:

Step S31, generating the amount information of each sub-subject as a sub-subject table view, and generating a timeline view and an order table view based on the sub-subject table view;

In this embodiment, in the process of generating the visualization view of travel data, a department table view is generated according to the amount information of each department, and the department name, total travel amount, travel order amount, and average value of the order amount in the department amount information of each department are taken as The four data indicators are arranged in a preset format and generated as a table view of departments. For details, please refer to Part A in Figure 4 and Figure 5. The department table view supports sorting of various data indicators. For example, sorting according to the number of travel orders from high to high, the first row in the table displays the department with the most number of trips. At the same time, it supports to view the specific travel orders of each department. The content to be viewed includes the order status of each department and each time period every day, as well as the specific travel information of each travel order. Among them, the order situation of each department in each time period of each day is presented in the form of a timeline view, and the specific travel information of each travel order is presented in the form of an order table view. By clicking on the department displayed in the department table view, the order status and specific travel information of the department in the time period can be generated respectively to generate a timeline view and an order table view; realize the generation of a timeline view and an order table based on the department table view. Views to view specific travel orders in a department. Specifically, the steps of generating the timeline view and the order table view based on the department table view include:

Step S311, when receiving the sub-subject selection instruction sent based on the sub-subject table view, determine the target sub-subject corresponding to the sub-subject selection instruction;

Step S312: Obtain the target travel order quantity in the sub-subject amount information corresponding to the target sub-subject, and determine the daily order distribution of the target sub-subject according to the travel time of each travel order corresponding to the target travel order quantity Information and time order distribution information;

Step S313, generating the daily order distribution information and the hourly order distribution information into a timeline view, and when receiving a screening instruction for the timeline view, determining a screening period corresponding to the screening instruction;

Step S314: Acquire travel information of each travel order corresponding to the screening period in the target sub-subject, and generate the travel information of each travel order into an order table view.

Further, when there is a demand for viewing a specific travel order of a certain department, the department is selected from the department table view, and the selection operation triggers a department selection instruction to be sent to the server. When receiving the department selection instruction sent based on the department table view, the server determines the target department corresponding to the department selection instruction, that is, determines the department to be viewed through the selection operation. Further, the department amount information of the target department is searched, and the travel order amount representing the number of trips is read from the department amount information as the target travel order amount. After that, find the travel time of each travel order that forms the target travel order quantity, and divide each travel order that forms the target travel order quantity according to each travel time, and divide the travel orders with the same travel date into one category to form the target department's travel order. Daily order distribution information, which represents the distribution of travel orders with dates. For each travel order in each date, it is classified according to the time point in the travel time, and the order distribution information is formed, which represents the distribution of the travel order volume in each hour.

Further, the daily order distribution information and the hourly order distribution information are generated as a time axis view. The time axis view exists in the form of a double-layered time axis. For details, please refer to Part C in FIG. 4 and as shown in FIG. 7 . In the timeline view, the lower timeline is represented by daily order distribution information, which reflects the daily travel order volume of the selected target department; the upper timeline is represented by time order distribution information, which reflects the hourly travel order volume of the target department. By performing date filtering on the lower timeline, the upper timeline displays the number of travel orders for each time period within the screening date, reflecting the distribution of travel orders for the target department within the selected date, that is, travel orders per hour within the date Quantity changes over time.

Further, the timeline view also supports viewing the specific travel information of each travel order within the time period by selecting the time period on the upper timeline. Specifically, the selection operation on the time in the upper timeline triggers a screening instruction, and when the server receives the screening instruction, the server determines the screening period corresponding to the screening instruction, that is, determines the time period selected by the selection operation in the upper timeline. After that, find the travel orders generated by the target department during the screening period, and obtain travel information from each travel order found. The travel information at least includes the employee number, department, start time, arrival time, The starting location, ending location, type of travel tool, etc. of the trip. The travel information composed of this type of information in each travel order is generated as an order table view, please refer to part H in FIG. 4 and FIG. 12 for details; it should be noted that the Company-Center and Home-Center is used to characterize the start and end positions.

Step S32, respectively generating a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view, and a projection view according to a plurality of the travel location information and the time data in each of the travel orders;

Further, after generating the time axis view and the order table view, continue to generate a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view and a projection view respectively based on the multiple travel location information and the time data in each travel order. . Wherein, the steps of respectively generating a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view and a projection view according to the multiple travel location information and the time data in each travel order include:

Step S321, according to the time data in each of the travel orders, divide a plurality of the travel location information into main body location information and a plurality of residential location information, and generate a plurality of the residential location information and the main body location information respectively. flow information between;

Step S322, based on a preset map, generate the subject location information, a plurality of the residential location information and the flow direction information into a map view;

Understandably, the daily travel of employees has different orientation characteristics at different times. For example, the orientation characteristic in the morning is from the residence to the enterprise location, and the visit characteristic in the evening is from the enterprise location to the residence location. After obtaining a plurality of travel location information for the distance of each travel order through the location data and time data in each travel order, continue to divide the plurality of travel location information. According to the time data in each travel order, combined with the initial position or the end position in the respective position data, the plurality of travel position information is divided into enterprise position information and a plurality of residential position information. When the time data is morning time, the travel location information corresponding to the initial location is the residential location information, and the travel location information corresponding to the destination location is the enterprise location information; and when the time data is evening time, the travel location information corresponding to the initial location is the enterprise location information. Location information, the travel location information corresponding to the destination location is the residential location information.

Further, according to the time data, generate flow information between a plurality of residence location information and the enterprise location information; for the morning time, generate a plurality of residence location information to respectively point to the flow information of the enterprise location information, indicating that the employee flows from the residence location to the enterprise. Location; for night time, the flow direction information in which the enterprise location information points to a plurality of residential location information respectively is generated, which represents the flow of employees from the corporate location to the residential location. At the same time, a preset map is preset, and the generated enterprise location information, multiple residential location information and flow direction information are added to the preset map, and a map view representing each employee gathering point is generated. For details, please refer to D in Figure 4 section, as well as shown in Figure 8. In part D, the circle at 480 represents the location information of the enterprise, the circles such as 62, 251, 51, 99 represent the residential location information, and the numbers in each circle represent the number of location data in the clustered travel orders. Location data for class travel orders. In addition, individual locations represented by location data in the clustered travel orders can be added to the map view for easy viewing of the actual location of individual employees.

Further, in order to represent the travel route and road conditions of the employees, the optimal route information between the location represented by the multiple residential location information and the location represented by the enterprise location information can be obtained from the preset map, and each optimal route Information is added to the corresponding location in the map view to represent the travel route. At the same time, the congestion information of the travel route represented by each optimal travel information in different days and different time periods is obtained, and according to the congestion situation of different road sections represented by the congestion information, the simulated trajectories of road conditions on different days and different time periods are generated and added to the map view to represent different Travel conditions at different times of the day. Then, the travel route and stopping point of the shuttle are set based on the travel route and travel road conditions. At the same time, the travel time data corresponding to multiple residential location information is combined to set the stop time of the shuttle, which is convenient for employees to travel.

Step S323, generating a chord diagram according to the subject position information and a plurality of the living position information, and generating a source-destination comparison view based on the chord diagram;

Step S324, acquiring the order attributes of each of the travel orders, and constructing a parallel coordinate view according to the order attributes of each of the travel orders;

Step S325: Project each of the travel orders according to the location data and time data in each of the travel orders to generate a projection map.

Further, the enterprise location information and the multiple residence location information are generated into a chord diagram, and the chord diagram exists in a certain shape, such as a circle or a regular polygon, and the enterprise location information and the multiple residence location information all form a part of the shape, Such as one-eighth of a circle or one side of a regular polygon. The part of the shape formed by the enterprise location information and multiple residence location information in the chord diagram contains a preset time period, and the time data of each travel order corresponding to the enterprise location information and multiple residence location information is distributed in the time period. At different time points, it represents the travel orders of each location information and multiple residential location information in the time period. By selecting multiple residence location information in the chord diagram, a source-destination comparison view between the selected residence location information and enterprise location information can be generated, reflecting the travel time of each travel order between the selected residence location information and enterprise location information . For travel orders with similar travel times, carpooling suggestions can be provided to employees who generate such travel orders to avoid the problem of limited public travel resources within the same segment, which makes it difficult for employees to travel quickly.

Further, the order attributes of each travel order are obtained, and the order attributes include start time, end time, order amount, start place, end place, etc.; the order attributes of each travel order are constructed as parallel coordinates view, please refer to part G in FIG. 4 and FIG. 11 for details. Each axis in the parallel coordinate view represents a type of order attribute, and the lines between different axes represent the order attributes of the travel order. The attributes can be filtered by selecting the axis.

Further, through a preset preset projection algorithm, such as t-SNE, the location data and time data contained in each travel order are processed to realize the projection of each travel order, and generate a projection map. For details, please refer to Figure 4. part B, as well as shown in Figure 6. A point in the projection map represents a travel order, and the closer the point is, the greater the probability that the travel order departs to a similar place at a similar time.

Step S33, the department table view, the time axis view, the order table view, the map view, the chord diagram, the source-destination comparison view, the parallel coordinate view and the projection diagram are jointly generated as the travel data visualization view of the enterprise.

After obtaining the department table view, time axis view, order table view, map view, chord diagram, source-destination comparison view, parallel coordinate view and projection view, these types of views are jointly generated into the enterprise's travel data visualization view, as shown in the following example: As shown in Figure 4, in order to provide a basis for the travel cost management and control of various departments and the setting of shuttle buses, it also provides a basis for employees' travel carpooling suggestions.

This embodiment reflects the travel cost of each department in the enterprise by generating a travel data visualization view including a department table view, a time axis view, an order table view, a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view, and a projection view. Usage, as well as the travel location and travel time of each employee; it is convenient to control the travel cost of each department based on the visual view of travel data, and set the shuttle route, stop point, stop time point, etc. In-depth analysis of travel data.

Further, based on the first or second embodiment of the travel data visualization method of the present invention, a third embodiment of the travel data visualization method of the present invention is proposed.

The difference between the third embodiment of the travel data visualization method and the first or second embodiment of the travel data visualization method is that the step of generating a chord diagram according to the subject position information and a plurality of the residential position information includes the following steps: :

Step S3231, constructing the subject position information into a subject position graphic of a preset shape, and constructing a plurality of the residence position information into a preset shape living position graphic respectively;

Step S3232, splicing the main body position graphic and a plurality of the living position graphics into a chord diagram frame, wherein the main body position graphic contains the first travel order corresponding to the main body position information within a preset time period distribution information, the plurality of residence location graphs include second travel order distribution information corresponding to the plurality of residence location information within a preset time period;

Step S3233, according to the travel order corresponding to the subject location information and the travel orders corresponding to the plurality of residential location information respectively, separate the first travel order distribution information and a plurality of the second travel order distribution information. The chord diagram is obtained by forming an association relationship between the main body position information and a plurality of the residence position information in the chord diagram frame.

In the process of generating the chord diagram in this embodiment, the enterprise location information is constructed as a preset-shaped enterprise location graphic, and a plurality of residential location information is constructed as a preset-shaped residential location graphic respectively. The preset shape is consistent with the existing shape of the chord diagram. If the chord diagram exists in a circle, the business location image and the multiple residential location graphics are arcs forming a circle, and each occupies an equal portion of the circle. , to form a circle together. A chord diagram frame is formed by splicing an enterprise location image formed by the enterprise location information and a plurality of enterprise location images formed by a plurality of residential location information. Each arc forming the chord diagram frame is set with a preset time period, which is represented by the arc length scale of each arc. 7 o'clock. The time data of the travel order corresponding to the enterprise location information is added to the preset time period of the enterprise location graph to form the distribution information of the first travel order corresponding to the enterprise location information within the preset time period in the enterprise location graph, indicating that the enterprise is in the pre-determined time period. Set the distribution of each travel order in the time period. At the same time, the time data of the travel order corresponding to each residence location information is added to the preset time period of each residence location graph to form the second travel order distribution information corresponding to each residence location information in each residence location graph within the preset time period , which represents the distribution of travel orders for each residence within a preset time period.

Further, according to the travel orders corresponding to the enterprise location information and the travel orders corresponding to the plurality of residential location information, the first travel order distribution information and the plurality of second travel order distribution information are respectively associated. In the first travel order distribution information and multiple second travel order distribution information, the same travel orders are associated according to their actual time and end time to form a chord diagram between the enterprise location information and the multiple residential location information respectively. The association relationship represents the travel time between the enterprise location and the residence location in the travel order, and the final chord diagram is obtained. For details, please refer to Part F in FIG. 4 and FIG. 10 .

Further, after the chord diagram is generated, a source-destination comparison view may be generated based on the chord diagram. Specifically, the step of generating the source-destination comparison view based on the chord diagram includes:

Step S3234, when receiving the comparison instruction sent based on the chord diagram, determine the target residence location information corresponding to the comparison instruction;

Step S3235: Determine the association relationship corresponding to the target residence location information in the chord diagram, and generate a source-destination comparison view between the target residence location information and the subject location information according to the corresponding association relationship.

Further, after the chord diagram is generated, the selection of multiple residential location information can be realized by selecting arcs in the chord diagram to represent the residential location of the travel order to be viewed. The selection operation triggers the comparison instruction to the server, and after receiving the comparison instruction sent based on the chord diagram, the server determines the target residence location information corresponding to the comparison instruction, that is, determines the selected arc. Then, find the association relationship corresponding to the target residential location information in the chord diagram, and generate the target residential location information and the enterprise location according to the travel order in the preset time period between the target residential location information and the enterprise location information represented by the association relationship. For a source-destination comparison view between messages, see Section E in Figure 4 and Figure 9 for details. In the source-destination comparison view, the upper axis represents the enterprise location information, and the lower axis represents the target residence location information; multiple time scales are set on the axes to represent different times; the line between the two axes represents the target residence location information The start and end time of a travel order between it and the business location. The higher the density of the connection, the more the number of travel orders between the target residential location information and the enterprise location information in a certain period of time. Such travel orders are similar in time and location, so the generation probability can be recommended. Users of travel orders carpool.

This embodiment generates a chord diagram based on the enterprise location information and a plurality of the residence location information, and generates a source-destination comparison view based on the chord diagram; it shows the distribution of travel orders between the enterprise and multiple residence locations, as well as the distribution of travel orders between the enterprise and the multiple residence locations. The order situation between the residence locations in each time period is easy to find employees with similar travel locations and travel times, and provides carpooling suggestions for such employees, which is conducive to saving public travel resources, improving the success rate of carpooling and the travel efficiency of employees.

The invention also provides a travel data visualization device.

Referring to FIG. 3 , FIG. 3 is a schematic diagram of functional modules of the first embodiment of the travel data visualization device of the present invention. The travel data visualization device includes:

A collection module 10, configured to collect multiple travel orders of the subject, wherein the travel orders at least include amount data, location data and time data;

A classification module 20, configured to classify each of the amount data, and generate sub-subject amount information of each sub-subject in the subject;

The clustering module 30 is configured to perform clustering on each of the travel orders according to the location data in each of the travel orders to generate a plurality of travel location information;

The generating module 40 is configured to generate a visualization view of the travel data of the subject according to the amount information of each of the sub-subjects, a plurality of the travel location information, and time data in each of the travel orders.

Further, the generating module 40 also includes:

a first generating unit, configured to generate the amount information of each sub-subject into a sub-subject table view, and generate a timeline view and an order table view based on the sub-subject table view;

a second generating unit, configured to respectively generate a map view, a chord diagram, a source-destination comparison view, a parallel coordinate view and a projection view according to a plurality of the travel location information and time data in each of the travel orders;

The third generating unit is configured to jointly generate the sub-subject table view, time axis view, order table view, map view, chord diagram, source-destination comparison view, parallel coordinate view and projection view as the travel data visualization of the subject view.

Further, the second generating unit is also used for:

According to the time data in each of the travel orders, the plurality of travel location information is divided into subject location information and a plurality of residential location information, and a plurality of residential location information and the main body location information are respectively generated. flow information;

Based on a preset map, generating the subject location information, a plurality of the residential location information and the flow direction information into a map view;

generating a chord diagram according to the subject position information and a plurality of the residence position information, and generating a source-destination comparison view based on the chord diagram;

Acquiring order attributes of each of the travel orders, and constructing a parallel coordinate view according to the order attributes of each of the travel orders;

According to the location data and time data in each of the travel orders, each of the travel orders is projected to generate a projection map.

Further, the first generating unit is also used for:

When receiving a sub-subject selection instruction sent based on the sub-subject table view, determining a target sub-subject corresponding to the sub-subject selection instruction;

Obtain the target travel order quantity in the sub-subject amount information corresponding to the target sub-subject, and determine the daily order distribution information and time of the target sub-subject according to the travel time of each travel order corresponding to the target travel order quantity Order distribution information;

generating the daily order distribution information and the hourly order distribution information into a timeline view, and when receiving a screening instruction for the timeline view, determining a screening period corresponding to the screening instruction;

The travel information of each travel order corresponding to the screening period in the target sub-subject is obtained, and the travel information of each travel order is generated into an order table view.

Further, the second generating unit is also used for:

constructing the subject position information into a subject position graphic of a preset shape, and constructing a plurality of the living position information into a preset shape living position graphic respectively;

splicing the main body position graph and a plurality of the residence position graphs into a chord diagram frame, wherein the main body position graph includes first travel order distribution information corresponding to the main body position information within a preset time period, The plurality of residential location graphs include second travel order distribution information corresponding to the plurality of residential location information within a preset time period;

According to the travel order corresponding to the subject location information and the travel orders corresponding to the plurality of residential location information, the first travel order distribution information and the plurality of second travel order distribution information are respectively associated to form The relationship between the subject position information and a plurality of the living position information in the chord diagram frame is obtained, so as to obtain the chord diagram.

Further, the second generating unit is also used for:

When receiving the comparison instruction sent based on the chord diagram, determining the target residence location information corresponding to the comparison instruction;

The correlation relationship corresponding to the target residential location information in the chord diagram is determined, and a source-destination comparison view between the target residential location information and the subject location information is generated according to the corresponding correlation relationship.

Further, the clustering module 30 also includes:

a first calculation unit, configured to calculate the position similarity of each of the travel orders in terms of location according to the position data in each of the travel orders;

The clustering unit is configured to cluster each of the travel orders according to the position similarity of each of the travel orders to generate a plurality of travel location information.

Further, the classification module 20 also includes:

The determining unit is configured to determine the sub-subject attributes of a plurality of the travel orders in the main body, divide the amount data of the travel orders with the same sub-subject attributes into one category, and generate sub-subjects of each sub-subject in the main body. subject amount data;

The statistical unit is used to count the number of data items containing the sub-subject amount data for each sub-subject, obtain the travel order volume of each sub-subject, and add and calculate the sub-subject amount data of each sub-subject to obtain the total travel amount of each sub-subject ;

The second calculation unit is configured to calculate the total travel amount of each of the sub-subjects according to the travel order amount of each of the sub-subjects, and obtain the average value of the order amount of each of the sub-subjects;

The fifth generating unit is configured to generate the sub-subject amount information of each of the sub-subjects as the sub-subject name, the total travel amount, the travel order amount and the average value of the order amount of each of the sub-subjects.

The specific implementation manner of the travel data visualization device of the present invention is basically the same as the above-mentioned embodiments of the travel data visualization method, and will not be repeated here.

In addition, an embodiment of the present invention also provides a readable storage medium.

A travel data visualization program is stored on the readable storage medium, and when the travel data visualization program is executed by the processor, the steps of the above travel data visualization method are implemented.

The readable storage medium of the present invention may be a computer-readable storage medium, the specific implementation manner of which is basically the same as that of the above embodiments of the travel data visualization method, and will not be repeated here.

The embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments, which are merely illustrative rather than restrictive. Under the inspiration of the present invention, without departing from the scope of protection of the purpose of the present invention and the claims, many forms can be made. Directly or indirectly applied in other related technical fields, these all belong to the protection of the present invention.

Claims (10)

1. A travel data visualization method is characterized by comprising the following steps:

collecting a plurality of travel orders of a main body, wherein the travel orders at least comprise money data, position data and time data;

classifying the amount data to generate sub-main body amount information of each sub-main body in the main body;

clustering the travel orders according to the position data in the travel orders to generate a plurality of travel position information;

and generating a travel data visual view of the main body according to the sub-main body amount information, the plurality of travel position information and the time data in the travel orders.

2. A travel data visualization method according to claim 1, wherein the step of generating a travel data visualization view of the subject based on the information of the amount of money of each sub-subject, the information of the plurality of travel locations, and the time data in each travel order includes:

generating each sub-main body amount information into a sub-main body table view, and generating a time axis view and an order table view based on the sub-main body table view;

respectively generating a map view, a chord graph, a source-destination comparison view, a parallel coordinate view and a projection view according to the travel position information and the time data in each travel order;

and generating the sub-main body table view, the time axis view, the order table view, the map view, the chord graph, the source-destination comparison view, the parallel coordinate view and the projection graph into a travel data visualization view of the main body.

3. A travel data visualization method as set forth in claim 2, wherein the step of generating a map view, a chord graph, a source-destination comparison view, a parallel coordinate view, and a projection graph from the plurality of travel location information and the time data in each of the travel orders, respectively, comprises:

dividing the travel position information into main body position information and living position information according to time data in each travel order, and generating flow direction information between the living position information and the main body position information;

generating the subject position information, the plurality of living position information and the flow direction information into a map view based on a preset map;

generating a chord graph according to the subject position information and the plurality of residence position information, and generating a source-destination comparison view based on the chord graph;

obtaining order attributes of each travel order, and constructing a parallel coordinate view according to the order attributes of each travel order;

and projecting each travel order according to the position data and the time data in each travel order to generate a projection drawing.

4. A method of visualization of travel data as recited in claim 2, wherein the step of generating a timeline view and an order form view based on the sub-body form view comprises:

when a sub-body selection instruction sent based on the sub-body table view is received, determining a target sub-body corresponding to the sub-body selection instruction;

acquiring a target travel order amount in the sub-main body amount information corresponding to the target sub-main body, and determining daily order distribution information and temporal order distribution information of the target sub-main body according to travel time of each travel order corresponding to the target travel order amount;

generating the daily order distribution information and the temporal order distribution information into a time axis view, and determining a screening time period corresponding to a screening instruction when the screening instruction of the time axis view is received;

and obtaining the travel information of each travel order corresponding to the screening time period in the target sub-main body, and generating a position order form view of the travel information of each travel order.

5. A travel data visualization method as set forth in claim 3, wherein the step of generating a chord chart from the subject location information and the plurality of living location information comprises:

constructing the subject position information into a subject position graph in a preset shape, and respectively constructing a plurality of living position information into living position graphs in the preset shape;

splicing the main body position graph and the living position graphs into a chord graph frame, wherein the main body position graph comprises first travel order distribution information corresponding to the main body position information in a preset time period, and the living position graphs comprise second travel order distribution information corresponding to the living position information in the preset time period;

and respectively associating the first travel order distribution information and the second travel order distribution information according to the travel orders corresponding to the main body position information and the travel orders corresponding to the living position information respectively to form an association relation between the main body position information and the living position information in the chord graph frame so as to obtain the chord graph.

6. A travel data visualization method as claimed in claim 5, wherein said step of generating a source-destination comparison view based on said chord graph comprises:

when a comparison instruction sent based on the chord graph is received, determining target living position information corresponding to the comparison instruction;

and determining the corresponding incidence relation of the target living position information in the chord graph, and generating a source-destination comparison view between the target living position information and the main body position information according to the corresponding incidence relation.

7. A travel data visualization method as claimed in any one of claims 1 to 6, wherein the step of clustering each of the travel orders according to the position data in each of the travel orders to generate a plurality of travel position information comprises:

respectively calculating the position similarity of each travel order on the position according to the position data in each travel order;

and clustering the travel orders according to the position similarity of the travel orders on the positions to generate a plurality of travel position information.

8. A method for visualizing travel data as claimed in any one of claims 1 to 6, wherein said step of classifying each of said amount data and generating sub-subject amount information for each sub-subject in said subject comprises:

determining sub-main body attributes of a plurality of travel orders in the main body, dividing money data of the travel orders with the same sub-main body attributes into a class, and generating sub-main body money data of each sub-main body in the main body;

counting the number of data items of each sub-main body including sub-main body amount data to obtain the travel order amount of each sub-main body, and summing up the sub-main body amount data of each sub-main body to obtain the total travel amount of each sub-main body;

calculating the total amount of travel of each sub-main body according to the travel order amount of each sub-main body to obtain the average value of the order amount of each sub-main body;

and generating the sub-main body name, the total trip amount, the trip order quantity and the order amount mean value of each sub-main body as the sub-main body amount information of each sub-main body.

9. Travel data visualization device, characterized in that it comprises a memory, a processor and a travel data visualization program stored on said memory and executable on said processor, said travel data visualization program, when executed by said processor, implementing the steps of the travel data visualization method according to any one of claims 1-8.

10. A readable storage medium, characterized in that the readable storage medium has stored thereon a travel data visualization program, which when executed by a processor implements the steps of the travel data visualization method according to any one of claims 1 to 8.

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