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

Travel data visualization method, device and equipment and readable storage medium Download PDF

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CN111611046A
CN111611046A CN202010466479.3A CN202010466479A CN111611046A CN 111611046 A CN111611046 A CN 111611046A CN 202010466479 A CN202010466479 A CN 202010466479A CN 111611046 A CN111611046 A CN 111611046A
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CN111611046B (en
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刘强强
李�权
林焕彬
汤春峰
黎治伟
陈天健
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WeBank Co Ltd
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Abstract

The invention discloses a travel data visualization method, a travel data visualization device, travel data visualization equipment and a readable storage medium, wherein the method comprises 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 each travel order according to the position data in each travel order to generate a plurality of travel position information; and generating a travel data visual view of the main body according to the amount information of each sub-main body, the travel position information and the time data in each travel order. According to the method, the trip cost of each sub-main body is represented by the sub-main body amount information in the visual view; travel demand information of the main body among the employees on positions and time is reflected by the travel position information and the time data in the travel orders, and multi-directional deep and efficient analysis of the travel data of the main body is realized.

Description

Travel data visualization method, device and equipment and readable storage medium
Technical Field
The invention relates to the technical field of financial technology (Fintech), in particular to a travel data visualization method, device, equipment and readable storage medium.
Background
With the continuous development of financial science and technology (Fintech), especially internet science and technology finance, more and more technologies (such as artificial intelligence, big data, cloud storage, visualization and the like) are applied to the financial field, but the financial field also puts higher requirements on various technologies, and if the requirements are accurate and efficient, the trip data of enterprise employees are analyzed.
In the enterprise operation process, trip data of enterprise employees are collected regularly for summary analysis, so that trip expenses of all departments in the enterprise are controlled. However, the summary analysis usually pays more attention to the total travel cost, ignores travel demand information among employees, and lacks deeper accurate analysis. And the current summary is realized by manual operation by means of a table tool, so that the time is long and the efficiency is low. Therefore, how to carry out deep and efficient analysis on the trip data of the enterprise is a technical problem to be solved urgently.
Disclosure of Invention
The invention mainly aims to provide a travel data visualization method, a travel data visualization device, travel data visualization equipment and a readable storage medium, and aims to solve the technical problem of how to carry out deep and efficient analysis on enterprise travel data in the prior art.
In order to achieve the above object, the present invention provides a travel data visualization method, which includes 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.
Optionally, the step of generating a visual view of travel data of the main body according to the amount information of each sub-main body, the travel position information, 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.
Optionally, the step of 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 includes:
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.
Optionally, the step of generating a time axis view and an order form view based on the sub-body form view includes:
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.
Optionally, the step of generating a chord graph according to the subject position information and the plurality of living position information includes:
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.
Optionally, the step of generating a source-destination comparison view based on the chord graph includes:
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.
Optionally, the step of clustering each travel order according to the position data in each travel order to generate a plurality of travel position information includes:
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.
Optionally, the step of classifying the amount data to generate sub-main-body amount information of each sub-main-body in the main body includes:
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.
Further, in order to achieve the above object, the present invention further provides a travel data visualization apparatus, including:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a plurality of travel orders of a main body, and the travel orders at least comprise money data, position data and time data;
the classification module is used for classifying the amount data to generate sub-main body amount information of each sub-main body in the main body;
the clustering module is used for clustering the travel orders according to the position data in the travel orders to generate a plurality of travel position information;
and the generating module is used for generating a travel data visual view of the main body according to the sub-main body amount information, the travel position information and the time data in the travel orders.
Further, to achieve the above object, the present invention also provides a travel data visualization apparatus, which includes a memory, a processor and a travel data visualization program stored in the memory and operable on the processor, wherein the travel data visualization program, when executed by the processor, implements the steps of the travel data visualization method as described above.
Further, 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 a processor, the travel data visualization program implements the steps of the travel data visualization method as described above.
According to the travel data visualization method, the travel data visualization device, the travel data visualization equipment and the computer readable storage medium, deep and efficient analysis of the main body data is achieved in a mode of visualizing the main body travel data. Firstly, collecting a plurality of travel orders of a main body, wherein each travel order at least comprises money data, position data and time data; classifying the money data to obtain sub-main body money information of each sub-main body in the main body; clustering the travel orders according to the position data in the travel orders, gathering the travel orders with similar positions into one category, and obtaining a plurality of travel position information according to the positions in the clusters; and generating a travel data visual view of the main body according to the amount information of each sub-main body, a plurality of travel position information and time data in each travel order. The total travel cost, the average travel cost and the like of each sub-main body are represented by the sub-main body amount information in the visual view, so that the automatic collection of the cost is realized, and the collection efficiency is improved. And the travel demand information of the positions and the time among the employees in the main body is reflected by the travel position information and the time data in the travel orders, so that a common travel suggestion is provided for the employees, the public travel resource is saved, the employees can conveniently and rapidly travel, the travel cost of the main body is reduced, and the multidirectional deep analysis of the travel data of the main body is realized.
Drawings
Fig. 1 is a schematic structural diagram of an apparatus hardware operating environment according to an embodiment of the travel data visualization apparatus of the present invention;
fig. 2 is a schematic flow chart of a first embodiment of a travel data visualization method according to the present invention;
fig. 3 is a functional module diagram of a trip data visualization apparatus according to a preferred embodiment of the present invention;
FIG. 4 is a schematic diagram of a travel data visualization view formed by the travel data visualization method according to the present invention;
FIG. 5 is a diagram of a department table view formed by the travel data visualization method according to the present invention;
FIG. 6 is a schematic diagram of a projection diagram formed by the travel data visualization method according to the present invention;
FIG. 7 is a schematic diagram of a timeline view formed by a travel data visualization method according to the present invention;
FIG. 8 is a schematic diagram of a map view formed by the travel data visualization method according to the present invention;
FIG. 9 is a schematic diagram of a source-destination comparison view formed by the travel data visualization method according to the present invention;
FIG. 10 is a schematic diagram of a chord chart formed by the travel data visualization method of the present invention;
FIG. 11 is a schematic view of a parallel coordinate view formed by the travel data visualization method according to the present invention;
fig. 12 is a schematic diagram of an order form view formed by the travel data visualization method of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides travel data visualization equipment, and referring to fig. 1, fig. 1 is a schematic structural diagram of an equipment hardware operating environment related to an embodiment of the travel data visualization equipment.
As shown in fig. 1, the travel data visualization apparatus 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. Wherein a communication bus 1002 is used to enable connective 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, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory device separate from the processor 1001 described above.
It will be understood by those skilled in the art that the hardware configuration of the travel data visualization device shown in fig. 1 does not constitute a limitation of the travel data visualization device, and may comprise more or less components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a readable storage medium, may include therein an operating system, a network communication module, a user interface module, and a travel data visualization program. The operating system is a program for managing and controlling the travel data visualization equipment and software resources, and supports the operation of the network communication module, the user interface module, the travel data visualization program 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 for connecting to a background server and communicating with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; the processor 1001 may call the travel data visualization program stored in the memory 1005 and perform the following operations:
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.
Further, the step of generating a visual view of travel data of the main body according to the amount information of each sub-main body, the travel position information, 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.
Further, the step of 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 includes:
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.
Further, 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.
Further, the step of generating a chord graph from the subject location information and the plurality of occupancy 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.
Further, the step of generating a source-destination comparison view based on the 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.
Further, the step of clustering each travel order according to the position data in each travel order to generate a plurality of travel position information includes:
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.
Further, the step of classifying each of the amount data and generating sub-main-body amount information of each sub-main-body in the main body includes:
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.
The specific implementation of the travel data visualization apparatus of the present invention is substantially the same as that of each embodiment of the travel data visualization method described below, and is not described herein again.
The invention further provides a travel data visualization method.
Referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of a travel data visualization method according to the present invention.
While a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order different from that shown or described herein. Specifically, the travel data visualization method in this embodiment includes:
step S10, collecting a plurality of travel orders of a main body, wherein the travel orders at least comprise money data, position data and time data;
the travel data visualization method in the embodiment is applied to a control center of a server or a client, and is suitable for visualizing the travel data of a main body through the control center of the server or the client, so that the travel times and the travel expenses of each sub-main body in the main body can be analyzed according to the visualized travel data. If the method is applied to the server, the server is in communication connection with the client, and the client transmits the collected travel data to the server for visualization processing. And if the application and the control center of the client side are applied, the control center performs visual processing on the trip data uploaded through the client side interface. Considering that the sub-subjects in the subject are distributed in different regions, the method is preferably applied to a server, namely, the server is used for visualizing the travel data of the subject. And, the main body may be an enterprise, a government or an institution, and each department in the enterprise, the government or the institution is each sub-main body in the main body. The embodiment preferably takes an enterprise and departments thereof as examples for explanation, and the visualized travel data of the embodiment can be used for analyzing the travel times and travel expenses of all departments in the enterprise, analyzing the travel positions of all employees in the enterprise, providing travel car sharing suggestions for all employees according to the travel positions, and setting a regular bus station, a regular bus route and the like. The trip data is derived from trip traffic reimbursement vouchers provided by employees in the enterprise, such as invoices for taking public traffic such as taxies, subways and buses, and comprises information such as time, addresses and money. Taking a traffic reimbursement voucher of each trip of the staff as a trip order, and collecting the trip orders of the staff in the enterprise; and each collected travel order at least comprises money data, position data and time data so as to respectively reflect money divided by each trip of the staff, the starting position and the end position of the trip, and the starting time and the arrival time of the trip.
Step S20, classifying each of the amount data to generate sub-body amount information of each sub-body in the body;
further, the amount data included in each travel order is classified according to the department where the employee is located, and the department amount information of each department is obtained. The department amount information at least comprises the name of each department, total trip amount, total trip order quantity and average order amount of each department, so that the total trip cost of each department, the trip times of employees of each department and the average trip cost of each department can be represented through the information in a visual view. Specifically, the step of classifying the amount data to generate the department amount information of each department of the enterprise includes:
step S21, determining sub-main body attributes of a plurality of travel orders in the main body, dividing the 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;
step S22, counting the number of data items of each sub-main body containing 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;
step S23, 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;
step S24, generating the sub-subject name, the total amount of travel, the amount of travel orders, and the average value of order amounts of each sub-subject as sub-subject amount information of each sub-subject.
Furthermore, the travel order of each employee includes, in addition to the specific travel information, information including attributes of the employee in the enterprise, such as the employee's job number, department name, and the like. Reading the department names in the trip orders as the department attributes of the employees in the enterprise, comparing the department attributes of the employees to determine the employees with the same department attributes, further dividing the respective amount data of the employees with the same department attributes into a class, and obtaining the department amount data of each department in the enterprise after the division of the amount data in the trip orders of the employees is completed.
Further, counting and counting the department amount data contained in each department to obtain the number of data items of the department amount data contained in each department, wherein one item of the department amount data is from one trip order, and the number of the data items of the department amount data is the quantity of the trip orders of the department. And adding the sum of the amount data of each department, and adding the trip amount represented by the amount data of each department in each department to obtain the total trip amount of each department. And calculating the total amount of travel of each department and the respective travel order quantity, and dividing the total amount of travel of each department by the respective travel order quantity to obtain an order amount average value of each department and reflect the average cost of each department on the travel order. And further generating the department name, the total trip amount, the trip order quantity and the order amount average value of each department as the department amount information. The travel expense condition of each department is reflected through the visual display of the amount information of each department.
Step S30, clustering each travel order according to the position data in each travel order to generate a plurality of travel position information;
furthermore, the travel orders are clustered according to the position data in the travel orders, the travel orders with similar positions are gathered into one category, and a plurality of travel position information is obtained according to the positions in the clusters. Specifically, the step of clustering each travel order according to the position data in each travel order to generate a plurality of travel position information includes:
step S31, respectively calculating the position similarity of each travel order on the position according to the position data in each travel order;
step S32, clustering each of the travel orders according to the position similarity of each of the travel orders in position, and generating a plurality of travel position information.
Further, a preset clustering algorithm for clustering, such as a geo-cluster [3] algorithm, is preset. And clustering the position information in each travel order by a preset clustering algorithm, and determining the position similarity of each travel order on the position. The position similarity reflects the distance between the daily travel positions of the employees. If the position similarity of each travel order on the travel position is high, the staff with the travel order is close to the travel position in daily life, such as from a cell a to an office building b.
Furthermore, the travel orders are clustered according to the position similarity of the travel orders on the positions, and the travel orders with the position similarity higher than a preset threshold value are gathered into one category to obtain a plurality of travel order categories. Further, a plurality of travel position information items are specified from the position data included in the travel orders of the respective categories. One category trip order corresponds to one trip position information and represents one geographic position, wherein the range of the geographic position is determined by the clustering precision of a clustering algorithm, such as clustering for a 500m range or clustering for a 1000m range.
Step S40, generating a travel data visualization view of the main body according to the money amount information of each sub-body, the travel location information, and the time data in each travel order.
Further, after obtaining department amount information and a plurality of trip position information of each department in the enterprise, generating a trip data visualization view of the enterprise according to the time data, the department amount information and the plurality of position information in the trip order of each employee. And reflecting the department travel conditions of all departments, the residence place gathering conditions of all employees and the travel time of all employees by the travel data visual view. And then, providing basis for the travel expense management and control of each department according to the visual view, and providing suggestions for the travel of the staff.
According to the travel data visualization method, the deep and efficient analysis of the main body data is realized in a mode of visualizing the main body travel data. Firstly, collecting a plurality of travel orders of a main body, wherein each travel order at least comprises money data, position data and time data; classifying the money data to obtain sub-main body money information of each sub-main body in the main body; clustering the travel orders according to the position data in the travel orders, gathering the travel orders with similar positions into one category, and obtaining a plurality of travel position information according to the positions in the clusters; and generating a travel data visual view of the main body according to the amount information of each sub-main body, a plurality of travel position information and time data in each travel order. The total travel cost, the average travel cost and the like of each sub-main body are represented by the sub-main body amount information in the visual view, so that the automatic collection of the cost is realized, and the collection efficiency is improved. And the travel demand information of the positions and the time among the employees in the main body is reflected by the travel position information and the time data in the travel orders, so that a common travel suggestion is provided for the employees, the public travel resource is saved, the employees can conveniently and rapidly travel, the travel cost of the main body is reduced, and the multidirectional deep analysis of the travel data of the main body is realized.
Further, based on the first embodiment of the travel data visualization method, a second embodiment of the travel data visualization method is provided.
The second embodiment of the travel data visualization method is different from the first embodiment of the travel data visualization method in that the step of generating a travel data visualization view of the subject according to the amount information of each sub-subject, the travel location information, and the time data in each travel order includes:
step S31, 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;
in the process of generating the travel data visualization view, according to the amount information of each department, a department table view is generated, and a department name, a total travel amount, a travel order amount, and an order amount average value in the amount information of each department are arranged according to a preset format as four data indexes to generate the department table view, which is specifically shown in fig. 4 a and fig. 5. The department table view supports the sorting of various data indexes, and if the data indexes are sorted from high to ground according to the quantity of travel orders, the first row in the table displays the departments with the most travel times. And meanwhile, specific travel orders of all departments are checked, and the checked content comprises the order condition of each department in each time period every day and specific travel information of each travel order. The order condition of each department in each time period is presented in a time axis view mode, and the specific travel information of each travel order is presented in an order form view mode. Clicking the department displayed in the department table view to respectively generate a time axis view and an order table view of the time period order condition and the specific trip information of the department; implementations generate a timeline view and an order form view based on a department form view to view specific travel orders in a department. Specifically, the step of generating a timeline view and an order form view based on the department form view includes:
step S311, 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;
step S312, obtaining a target travel order quantity in the money amount information of the sub-main body 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 the travel time of each travel order corresponding to the target travel order quantity;
step 313, 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 for the time axis view is received;
step S314, obtaining the travel information of each travel order corresponding to the screening time period in the target sub-body, and generating a position order form view of the travel information of each travel order.
Further, when there is a need to view a specific travel order for a certain department, the department is selected from the department table view, and the selecting operation triggers a department selecting instruction to be sent to the server. When the server receives the department selection instruction sent based on the department table view, the server determines a target department corresponding to the department selection instruction, namely determines a department required to be viewed through selection operation selection. And then, the department amount information of the target department is searched, and the travel order quantity representing the travel times is read from the department amount information and is used as the target travel order quantity. And then, searching the travel time of each travel order forming the target travel order quantity, dividing each travel order forming the target travel order quantity according to each travel time, dividing the travel orders with the same travel date into a class, forming daily order distribution information of a target department, and representing the distribution condition of the travel order quantity along with the date. And classifying the travel orders in each date according to the time points in the travel time to form time order distribution information and represent the distribution situation of the travel order quantity in each hour.
Further, daily order distribution information and chronological order distribution information are generated as a timeline view. The timeline view exists in the form of a two-layer timeline, as shown in fig. 4, section C, and fig. 7. The time axis below the time axis view is represented by daily order distribution information, and represents daily travel order quantity of the selected target department; the time axis above represents the order distribution information, and represents the hourly travel order quantity of the target department. The date screening is carried out on the lower time axis, and the travel order quantity of each time period in the screening date is displayed on the upper time axis, so that the distribution condition of the travel orders of the target department in the selected date is reflected, namely the change condition of the travel order quantity of each hour in the date along with the time.
Further, the timeline view also supports the selection of time periods on the upper timeline to view specific travel information of various travel orders within the time periods. Specifically, a screening instruction is triggered by the selection operation of the time in the upper time axis, and when the server receives the screening instruction, the server determines the screening time period corresponding to the screening instruction, that is, determines the time period selected by the selection operation in the upper time axis. And then, searching for a travel order generated by the target department in the screening time period, and acquiring travel information from each searched travel order, wherein the travel information at least comprises the work number of the staff from which the travel order is sourced, departments, starting time, arrival time, starting position, end position, travel tool type and the like. Generating travel information composed of the information in each travel order as an order form view, specifically referring to part H in fig. 4 and as shown in fig. 12; note that Company-Center and Home-Center in the order form view are used to characterize the start and end positions.
Step S32, 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;
further, after the time axis view and the order form view are generated, a map view, a chord graph, a source-destination comparison view, a parallel coordinate view and a projection view are respectively generated according to the travel position information and the time data in each travel order. The method comprises the following steps of generating a map view, a chord graph, a source-destination comparison view, a parallel coordinate view and a projection view according to a plurality of travel position information and time data in each travel order, wherein the steps of generating the map view, the chord graph, the source-destination comparison view, the parallel coordinate view and the projection view respectively comprise:
step S321, dividing the travel position information into main position information and living position information according to time data in each travel order, and generating flow direction information between each of the living position information and the main position information;
step S322, 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;
understandably, the daily trips of employees have different directional characteristics at different times, such as a directional characteristic in the morning from a residential location to a business location, and a visit characteristic in the evening from the business location to the residential location. And continuously dividing the plurality of travel position information after obtaining the plurality of travel position information for each travel order distance through the position data and the time data in each travel order. And dividing the plurality of travel position information into enterprise position information and a plurality of residence position information according to the time data in each travel order and by combining the initial position or the final position in each position data. When the time data is morning time, the travel position information corresponding to the initial position is residence position information, and the travel position information corresponding to the terminal position is enterprise position information; and when the time data is night time, the travel position information corresponding to the initial position is enterprise position information, and the travel position information corresponding to the terminal position is residence position information.
Further, generating flow direction information between each of the plurality of residential position information and the enterprise position information according to the time data; generating flow direction information of a plurality of pieces of living position information respectively pointing to enterprise position information for morning time, and representing that the staff flow from the living positions to the enterprise positions; and generating flow direction information of the enterprise position information respectively pointing to the plurality of living position information for the night time, and representing that the staff flows from the enterprise position to the living position. Meanwhile, a preset map is preset, and the generated enterprise location information, the plurality of living location information and the flow direction information are added to the preset map to generate a map view representing the gathering points of the employees, which is specifically shown in fig. 4, part D, and fig. 8. Circles in section D, 480, represent business location information, circles such as 62, 251, 51, 99, etc., represent residential location information, numbers in each circle represent the number of location data in a clustered travel order, and selection of a circle may display the location data of the clustered travel order within the circle. In addition, each position represented by the position data in the clustered travel orders can be added to the map view so as to conveniently view the actual position of each employee.
Further, in order to represent the travel route and the exit condition of the staff, optimal route information between positions represented by the plurality of residence position information and positions represented by the enterprise position information can be obtained from a preset map, and each optimal route information is added to a corresponding position of the map view to represent the travel route. And simultaneously acquiring congestion information of the travel route represented by the optimal travel information at different dates and different time periods, and generating road condition simulation tracks at different dates and different time periods to be added to a map view according to congestion conditions of different road sections represented by the congestion information so as to represent travel road conditions at different dates and different time periods. And then set up the route of going and the stop of regular bus through trip route and trip road conditions, combine the trip time data that corresponds with a plurality of position information of living simultaneously, set up the stop of regular bus, the trip of the staff of being convenient for.
Step S323, generating a chord graph according to the subject position information and the plurality of living position information, and generating a source-destination comparison view based on the chord graph;
step S324, obtaining order attributes of each travel order, and constructing a parallel coordinate view according to the order attributes of each travel order;
step S325, projecting each travel order according to the position data and the time data in each travel order, and generating a projection drawing.
Further, the business location information and the plurality of living location information are generated into a chord graph, the chord graph exists in a specific shape, such as a circle or a regular polygon, and the business location information and the plurality of living location information form a part of the shape, such as one eighth of the circle or one side of the regular polygon. The enterprise position information and the plurality of living position information are distributed at different time points of the time period, and represent the travel order conditions of the position information and the plurality of living position information in the time period. Through selection of a plurality of pieces of living position information in the chord graph, a source-destination comparison view between the selected living position information and the enterprise position information can be generated, and the travel time of each travel order between the selected living position information and the enterprise position information is reflected. For the trip orders with similar trip time, car sharing suggestions can be provided for the employees generating the trip orders, and the problem that the employees are difficult to trip quickly due to limited public trip resources in the same section is avoided.
Further, obtaining order attributes of each travel order, wherein the order attributes comprise starting time, ending time, order amount, starting place, ending place and the like; the type of order attribute of each travel order is constructed into a parallel coordinate view, which is specifically referred to section G in fig. 4 and shown in fig. 11. Each axis in the parallel coordinate view represents a type of order attributes, connecting lines between different axes represent order attributes of the travel orders, and the attributes can be filtered by selecting the axes.
Further, the position data and the time data included in each travel order are processed through a preset projection algorithm, such as t-SNE, to implement projection of each travel order and generate a projection diagram, which is specifically shown in fig. 4, part B, and fig. 6. One point in the projection graph represents a travel order, and the closer the position of the point is, the higher the probability that the travel order starts to a similar place at a similar time is.
And step S33, generating the department 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 enterprise.
After a department form view, a time axis view, an order form view, a map view, a chord graph, a source-destination comparison view, a parallel coordinate view and a projection graph are obtained, the views are jointly generated into a travel data visualization view of an enterprise, specifically as shown in fig. 4, so that a basis is provided for travel cost control of each department and setting of a regular bus, and a basis is provided for travel car sharing suggestions of employees.
The embodiment generates a travel data visualization view comprising a department table view, a time axis view, an order table view, a map view, a chord graph, a source-destination comparison view, a parallel coordinate view and a projection graph, so as to reflect the travel cost use condition of each department in an enterprise, and the travel position and travel time of each employee; the trip expenses of all departments are conveniently controlled according to the visual view of trip data, a regular bus route, a stop point, a stop time point and the like are set, a car sharing suggestion is provided for employees, and the deep analysis of trip data of the enterprise employees is realized.
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 third embodiment of the travel data visualization method differs from the first or second embodiment of the travel data visualization method in that the step of generating a chord chart from the subject position information and the plurality of living position information includes:
step S3231, constructing the subject position information into a subject position graph with a preset shape, and constructing a plurality of living position information into living position graphs with preset shapes respectively;
step S3232, splicing the main body position graph and the living position graphs into a chord graph frame, wherein the main body position graph comprises first trip order distribution information corresponding to the main body position information in a preset time period, and the living position graphs comprise second trip order distribution information corresponding to the living position information in the preset time period;
step S3233, respectively associating the first travel order distribution information and the second travel order distribution information according to the travel order corresponding to the subject position information and the travel orders corresponding to the living position information, so as to form an association relationship between the subject position information and the living position information in the chord graph frame, so as to obtain the chord graph.
In the process of generating the chord graph, the embodiment constructs the enterprise position information into an enterprise position graph in a preset shape, and constructs the plurality of living position information into living position graphs in preset shapes respectively. The preset shape is consistent with the shape of the chord graph, if the chord graph exists in a circle, the enterprise position image and the living position images form circular arcs, and each of the enterprise position image and the living position images respectively occupies equal parts of the circle to form the circle together. And splicing the enterprise position image formed by the enterprise position information and a plurality of enterprise position images formed by a plurality of residence position information to form a chord graph frame. Each section of circular arc forming and chord graph frame is provided with a preset time period which is expressed by the arc length scale of each section of circular arc, and the time range of the preset time period can be set according to requirements, such as setting 7 points from 20 points to the second point. Adding the time data of the trip orders corresponding to the enterprise position information into a preset time period of the enterprise position graph to form first trip order distribution information corresponding to the enterprise position information in the enterprise position graph within the preset time period, and representing the distribution condition of each trip order of the enterprise within the preset time period. And meanwhile, adding the time data of the travel orders corresponding to the resident position information into the preset time period of each resident position graph to form second travel order distribution information corresponding to the resident position information in the preset time period in each resident position graph, and representing the distribution situation of the travel orders of each residential place in the preset time period.
Further, the first travel order distribution information and the second travel order distribution information are respectively associated according to the travel orders corresponding to the enterprise location information and the travel orders corresponding to the living location information. In the first trip order distribution information and the plurality of second trip order distribution information, the same trip order is associated according to the real time and the end time, forming an association relationship between the enterprise position information and the plurality of living position information in the chord graph frame, representing the trip time between the enterprise position and the living position in the trip order, and obtaining a final chord graph, specifically referring to section F in fig. 4 and fig. 10.
Further, after the chord chart is generated, a source-destination comparative view may be generated based on the chord chart. Specifically, the step of generating the source-destination comparison view based on the chord graph comprises the following steps:
step S3234, when receiving a comparison instruction sent based on the chord chart, determining target living position information corresponding to the comparison instruction;
step S3235, 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 subject position information according to the corresponding incidence relation.
Further, after the chord chart is generated, the selection of the plurality of dwell position information can be realized through the selection of the circular arcs in the chord chart, and the dwell positions of the travel orders required to be viewed are represented. And the selection operation triggers a comparison instruction to the server, and after receiving the comparison instruction sent based on the chord graph, the server determines the target living position information corresponding to the comparison instruction, namely determines the selected arc. Further, an association relationship corresponding to the target living location information in the chord graph is searched, and a source-destination comparison view between the target living location information and the enterprise location information is generated according to a trip order between the target living location information and the enterprise location information represented by the association relationship in a preset time period, specifically please refer to part E in fig. 4, and fig. 9. In the source and destination comparison view, the upper axis represents enterprise position information, and the lower axis represents target living position information; a plurality of time scales are arranged on the shaft and represent different times; and a connecting line between the two shafts represents the starting time and the ending time of a certain travel order between the target living position information and the enterprise position information. The higher the density of the connecting lines is, the more the quantity of the travel orders representing the target living position information and the enterprise position information in a certain period of time is, the travel orders have similarity in time and position, and therefore the users generating the probabilistic travel orders can be recommended to share the car.
The embodiment generates enterprise position information and a plurality of residence position information into a chord graph, and generates a source-destination comparison view based on the chord graph; the travel order distribution situation between the enterprise and the plurality of living positions and the order situation between the enterprise and the living positions in each time period are shown, the employees with similar travel positions and travel time are convenient to search, car sharing suggestions are provided for the employees, public travel resources are saved, and the car sharing success rate and the employee travel efficiency are improved.
The invention further provides a travel data visualization device.
Referring to fig. 3, fig. 3 is a functional module schematic diagram of a first embodiment of a travel data visualization apparatus according to the present invention. The trip data visualization device comprises:
the system comprises an acquisition module 10, a display module and a display module, wherein the acquisition module is used for acquiring a plurality of travel orders of a main body, and the travel orders at least comprise money data, position data and time data;
a classification module 20, configured to classify each of the amount data, and generate sub-main-body amount information of each sub-main body in the main body;
the clustering module 30 is configured to cluster the travel orders according to the position data in the travel orders to generate a plurality of travel position information;
a generating module 40, configured to generate a travel data visualization view of the main body according to the amount information of each sub-main body, the plurality of travel position information, and the time data in each travel order.
Further, the generating module 40 further includes:
the first generation unit is used for 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;
the second generating unit is used for 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 a third generating unit, configured to generate 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 as a travel data visualization view of the main body.
Further, the second generating unit is further configured to:
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.
Further, the first generating unit is further configured to:
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.
Further, the second generating unit is further configured to:
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.
Further, the second generating unit is further configured to:
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.
Further, the clustering module 30 further includes:
the first calculating unit is used for respectively calculating the position similarity of each travel order on the position according to the position data in each travel order;
and the clustering unit is used for clustering the travel orders according to the position similarity of the travel orders on the positions to generate a plurality of travel position information.
Further, the classification module 20 further includes:
the determining unit is used for determining sub-main body attributes of the plurality of travel orders in the main body, dividing money data of the travel orders with the same sub-main body attributes into one class, and generating sub-main body money data of each sub-main body in the main body;
the statistical unit is used for 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;
the second calculating unit is used for 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 a fifth generating unit, configured to generate the sub-subject name, the total amount of travel, the amount of travel orders, and the average value of order amounts of each sub-subject as sub-subject amount information of each sub-subject.
The specific implementation of the travel data visualization apparatus of the present invention is substantially the same as that of each embodiment of the travel data visualization method, and is not described herein again.
In addition, the embodiment of the invention also provides a readable storage medium.
The readable storage medium stores a travel data visualization program, and the travel data visualization program, when executed by the processor, implements the steps of the travel data visualization method as described above.
The readable storage medium of the present invention may be a computer readable storage medium, and the specific implementation manner of the readable storage medium of the present invention is substantially the same as that of each embodiment of the travel data visualization method, and details are not repeated herein.
The present invention is described in connection with the accompanying drawings, but the present invention is not limited to the above embodiments, which are only illustrative and not restrictive, and those skilled in the art can make various changes without departing from the spirit and scope of the invention as defined by the appended claims, and all changes that come within the meaning and range of equivalency of the specification and drawings that are obvious from the description and the attached claims are intended to be embraced therein.

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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