CN114036358A - Data analysis method, device and equipment for self-driving tour and storage medium - Google Patents
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Abstract
The application relates to a data analysis method, a device, equipment and a storage medium for self-driving tour. The method comprises the following steps: setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour; acquiring target traffic data corresponding to a target address in a target date; and determining target index information corresponding to the preset index according to the target traffic data, and determining the target self-driving tour type of each user according to the target index information.
Description
Technical Field
The present application relates to the field of traffic information technologies, and in particular, to a method, an apparatus, a device, and a storage medium for analyzing data of a self-driving tour.
Background
In recent years, with the rapid development of socioeconomic technology, self-driving travel is becoming a mainstream travel mode. In order to further analyze the data of the self-driving tour, the current technical scheme generally obtains a trip starting point and a trip end point of the self-driving tour of the user through user input or a positioning device, and determines the trip length of the self-driving tour of the user; however, the technical scheme can only determine the travel length of the self-driving tour, and the corresponding travel length is obtained for each user individual; when the travel lengths of the self-driving travels of a plurality of users need to be acquired, corresponding travel starting points and travel end points of the self-driving travels need to be acquired for different users, and the operation process is complicated.
Therefore, how to analyze the self-driving tour data to improve comprehensiveness and convenience of the self-driving tour data analysis is a technical problem that needs to be solved by those skilled in the art at present.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a device and a storage medium for analyzing data of self-driving tour, which can analyze the data of self-driving tour to improve comprehensiveness and convenience of the data analysis of self-driving tour.
A method of data analysis for self-driving travel, the method comprising:
setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
acquiring target traffic data corresponding to a target address in a target date;
and determining target index information corresponding to the preset index according to the target traffic data, and determining the target self-driving tour type of each user according to the target index information.
In one embodiment, the obtaining target traffic data corresponding to a target address within a target date includes:
acquiring regional traffic data corresponding to an exit traffic data table in a target region; wherein the target address is affiliated with the target area;
and screening the regional traffic data according to the vehicle type, the target address, the target date, the minimum travel distance and the minimum travel time length to obtain the target traffic data of the target address in the target date.
In one embodiment, the determining, according to the target traffic data, target index information corresponding to the preset index and determining, according to the target index information, a target self-driving tour type corresponding to each user respectively includes:
determining the number of strokes of each vehicle, the attribution of the stroke end point and the stopping time according to the target traffic data;
and determining the target self-driving tour type corresponding to each user according to the number of the trips, the attribution of the trip end point and the stopping time corresponding to each vehicle.
In one embodiment, the determining the target self-driving tour type corresponding to each user according to the number of journeys, the attribution of the journey end point and the stopping time corresponding to each vehicle respectively comprises:
if the number of the strokes is 1 and the attribution of the stroke end point and the attribution of the stroke starting point are in the same attribution, determining that the corresponding target self-driving tour type of the user is the local self-driving night tour;
if the number of the strokes is 1, determining that the attribution of the stroke end point is not the same as the attribution of the stroke start point, and determining that the corresponding target self-driving tour type of the user is the cross-city self-driving night tour;
if the number of the strokes is 2, the stay time is longer than the preset time length, and the attribution of the stroke end point and the attribution of the stroke starting point are in the same attribution, determining that the corresponding target self-driving tour type of the user is the local self-driving daily tour;
if the number of the strokes is 2, the stop time is longer than the preset time length, the attribution of the stroke end point and the attribution of the stroke starting point are not in the same attribution, and the target self-driving tour type of the corresponding user is determined to be the cross-city one-day tour.
In one embodiment, the method further comprises:
and calculating the average stay time and the average driving distance respectively corresponding to different self-driving tour types according to the target index information.
In one embodiment, the method further comprises:
acquiring self-driving tour information of the same time period in a plurality of different years; wherein the self-driving tour information includes the number of each self-driving tour type, the average stay time and the average travel distance of each self-driving tour type;
and analyzing the self-driving tour information to determine the development trend of different self-driving tour types.
In one embodiment, the method further comprises:
and determining the attractive force degree of each travel end point by using each travel end point of each self-driving travel type and the average residence time and the average driving distance corresponding to each self-driving travel type.
A data analysis apparatus for self-driving travel, the apparatus comprising:
the setting module is used for setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
the acquisition module is used for acquiring target traffic data corresponding to a target address in a target date;
and the analysis module is used for determining target index information corresponding to the preset index according to the target traffic data and determining the target self-driving tour type of each user according to the target index information.
A data analysis apparatus for self-driving travel, comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
acquiring target traffic data corresponding to a target address in a target date;
and determining target index information corresponding to the preset index according to the target traffic data, and determining the target self-driving tour type of each user according to the target index information.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
acquiring target traffic data corresponding to a target address in a target date;
and determining target index information corresponding to the preset index according to the target traffic data, and determining the target self-driving tour type of each user according to the target index information.
According to the self-driving tour data analysis method, the self-driving tour data analysis device, the self-driving tour data analysis equipment and the self-driving tour data analysis storage medium, a plurality of self-driving tour types are set according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; therefore, after the target index information is determined according to the target traffic data corresponding to the target address in the target date, the target self-driving tour type of each user can be determined more accurately according to the target index information; and the target self-driving tour type corresponding to each user is determined by directly utilizing the target index information determined according to the target traffic data by acquiring the target traffic data corresponding to the target address in the target date, and the self-driving tour type corresponding to each user can be determined for a plurality of users respectively, so that the comprehensiveness and convenience of the data analysis of the self-driving tour can be improved.
Drawings
FIG. 1 is a diagram of an exemplary embodiment of a data analysis method for self-driving tour;
FIG. 2 is a schematic flow chart illustrating a method for analyzing data of a self-driving tour according to an embodiment;
FIG. 3 is a flow diagram illustrating the determination of target traffic data according to one embodiment;
FIG. 4 is a flow diagram illustrating the determination of a target self-driving tour type for each user in one embodiment;
fig. 5 is a block diagram showing a configuration of a data analysis device for the self-driving tour in one embodiment;
fig. 6 is an internal configuration diagram of a data analysis device for the self-driving tour in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The data analysis method for the self-driving tour provided by the application can be applied to the application environment shown in fig. 1. The environment relates to a data analysis device 102 for self-driving tour and a traffic data collection device 104 of a traffic data station, wherein the data analysis device 102 for self-driving tour is in communication connection with the traffic data collection device 104. The traffic data acquisition device 104 may be a data acquisition device disposed at each highway intersection, and is configured to acquire traffic data corresponding to the highway intersection and send the traffic data to the data analysis device 102 of the self-driving tour, and the traffic data acquisition device 104 may also be configured to acquire and summarize traffic data of the corresponding highway intersections acquired by other traffic data acquisition devices 104, and then send the summarized traffic data to the data analysis device 102 of the self-driving tour. The data analysis device 102 for the self-driving tour acquires all traffic data from the traffic data acquisition device 104, screens out target traffic data from all the acquired traffic data according to target dates and target addresses, analyzes the target traffic data to obtain corresponding target index information, and determines the target self-driving tour type corresponding to each user according to the target index information. The data analysis device 102 for the self-driving tour may be, but is not limited to, various personal computers and notebook computers, and the data analysis device 102 for the self-driving tour may also be implemented by an independent server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a data analysis method for self-driving tour is provided, which is described by taking the method as an example applied to the data analysis device 102 for self-driving tour in fig. 1, and includes the following steps:
step 202, setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving travel.
Specifically, the preset indexes are information indexes representing self-driving tour, and different self-driving tour types can be distinguished according to the index information of the preset indexes. In actual operation, the preset indexes are generally multiple, and multiple different self-driving tour types are set according to different combination conditions of the index information of the multiple preset indexes. When the information corresponding to the preset index is a character, setting a corresponding self-driving tour type according to the information type of the preset index; and when the information corresponding to the preset index is a numerical value, setting a corresponding self-driving tour type according to the data range of the preset index.
In this embodiment, the preset indexes may include the number of trips, the starting point of the trip, the ending point of the trip, the stopping time, and the like, and the types of the self-driving tour include a local self-driving day tour, a local self-driving night tour, a cross-city self-driving day tour, and a cross-city self-driving night tour; the local self-driving daily tour refers to a self-driving tour that a travel starting point and a travel ending point of a travel trip are in the same city and come and go to the travel starting point and the travel ending point in the same day; the local self-driving overnight tour refers to a self-driving tour that the travel starting point and the travel ending point of a travel trip are in the same city, and do not go to and return from the travel starting point and the travel ending point in the same day; the cross-city self-driving daily tour refers to a self-driving tour that the travel starting point and the travel ending point of a travel trip are not in the same city, and the travel starting point and the travel ending point are traveled to and fro in the same day; the self-driving overnight tour across the cities refers to the self-driving tour that the travel starting point and the travel ending point of the travel journey are not in the same city, and do not go to and fro in the same day.
And step 204, acquiring target traffic data corresponding to the target address in the target date.
In this embodiment, the target address is generally an address divided according to administrative regions, for example, the target address may be a provincial administrative region, such as south cloud province, beijing city, etc., a city administrative region, such as kunming city, lijiang city, etc., or a ground administrative region, such as different regions in kunming city, which is not limited in this embodiment. The target date is a certain date on which the self-driving tour data analysis is needed, and the target self-driving tour type of the user within 24 hours of a certain day is determined. When the data of the self-driving tour in a period of time needs to be analyzed, specifically, each day in the period of time is respectively used as a target date to perform data analysis of the self-driving tour; the specific duration of a period of time is not limited in this embodiment, and may be, for example, holidays such as two days on weekends, three days on small and long holidays, five one, eleven, and spring festival; more specifically, when the data of the self-driving tour in the holiday of seven days of national celebration needs to be analyzed, each day of seven days of national celebration needs to be set as a target date respectively for the data analysis of the self-driving tour, and a corresponding analysis result is obtained.
When the target traffic data is acquired, all traffic data including a target address and a target date can be acquired, and then the corresponding target traffic data is screened from all traffic data after the target date and the target address are determined. All traffic data are acquired according to preset fields, and the preset fields include, but are not limited to, an entry stop name, an exit date and time, an entry date and time, and the like. In addition, in other embodiments, the target date and the target address may be determined first, and then the corresponding traffic data may be collected according to the target date, the target address and the preset field, where the collected traffic data is the target traffic data.
And step 206, determining target index information corresponding to the preset indexes according to the target traffic data, and determining target self-driving tour types corresponding to the users respectively according to the target index information.
Specifically, after the target traffic data is obtained, the target traffic data is analyzed, and target index information corresponding to a preset index is determined according to the target traffic data. For example, the number of trips, the start of the trip, the end of the trip, the stop time, and the like corresponding to each user are determined from the target traffic data. And then determining the target self-driving tour type corresponding to each user according to the corresponding relation between the target index information and the preset index information of different self-driving tour types and the corresponding relation between each target index information and each user.
More specifically, in actual operation, traffic data corresponding to the same license plate is traffic data of the same user, and target index information determined according to the traffic data corresponding to the same license plate is target index information of the corresponding user. Determining a travel starting point and a travel end point respectively corresponding to different users according to the target index information, so that whether the self-driving tour of the corresponding user is a local self-driving tour or a cross-city self-driving tour can be determined according to the travel starting point and the travel end point; and determining the travel time lengths corresponding to different users respectively according to the target index information, determining whether the self-driving tour of the corresponding user is a day tour or a night tour, and integrating the target index information to determine the target self-driving tour type corresponding to each user respectively.
According to the data analysis method for the self-driving tour, provided by the embodiment of the invention, a plurality of self-driving tour types are set according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; therefore, after the target index information is determined according to the target traffic data corresponding to the target address in the target date, the target self-driving tour type of each user can be determined more accurately according to the target index information; and the target self-driving tour type corresponding to each user is determined by directly utilizing the target index information determined according to the target traffic data by acquiring the target traffic data corresponding to the target address in the target date, and the self-driving tour type corresponding to each user can be determined for a plurality of users respectively, so that the comprehensiveness and convenience of the data analysis of the self-driving tour can be improved.
On the basis of the foregoing embodiment, this embodiment further describes and optimizes the technical solution, and specifically, in this embodiment, acquiring the target traffic data corresponding to the target address within the target date includes:
acquiring regional traffic data corresponding to an exit traffic data table in a target region; wherein, the target address belongs to the target area;
and screening the regional traffic data according to the vehicle type, the target address, the target date, the minimum travel distance and the minimum travel time length to obtain the target traffic data of the target address in the target date.
Specifically, the target area is an area with a larger range including the target address, that is, the target address belongs to the target area; by acquiring traffic data of each high-speed intersection in the target area, area traffic data corresponding to the target area, that is, traffic data of which the area traffic data includes a target address, is acquired. More specifically, when collecting traffic data, the corresponding traffic data is collected according to a preset field in the exit passage data table. As shown in table 1, an exit passage data table is provided for the present embodiment.
TABLE 1 Exit passage data sheet
In this embodiment, after the regional traffic data of the target region is acquired, the regional traffic data may be further preprocessed, where the preprocessing includes format conversion and deletion of missing values for the regional traffic data; the format conversion refers to converting the traffic data of the same preset field in the regional traffic data into a uniform unit format; the missing value refers to a situation that the traffic data corresponding to the preset field is not acquired at a certain moment or the traffic data corresponding to the preset field at a certain moment is lost, so that the traffic data corresponding to the preset field at the moment is empty, and therefore when the missing value in the regional traffic data is detected, the missing value is deleted; and the preprocessed regional traffic data is subsequently utilized for analysis, so that the convenience and accuracy of the data analysis of the self-driving tour can be further improved.
As shown in fig. 3, a schematic flow chart for determining target traffic data according to this embodiment is provided. After obtaining the regional traffic data, repeatedly cleaning and screening the regional traffic data according to a preset field, and determining target traffic data which accord with the self-driving tour definition; the method specifically comprises the following steps:
and step 302, screening the regional traffic data, reserving traffic data corresponding to vehicles of 7 seats or less, and deleting the traffic data corresponding to the common license plate. In general, the types of vehicles include cars, trucks, buses, trailers, motorcycles, and the like. In the embodiment, the car is determined to be the type of the car according with the self-driving tour definition, so that regional traffic data with preset fields of 'export charging car type code' and 'export car type code' are screened, and 7 or less cars are screened out; and screening the regional traffic data corresponding to the area with the preset field of 'exit actual vehicle license plate number', deleting the license plate number with the first character not being a character in the license plate number and the traffic data of the vehicle corresponding to the license plate number containing the 'alarm' character in the license plate number, and obtaining the traffic data meeting the vehicle type requirement.
And step 304, screening the regional traffic data, and deleting the traffic data of the non-target address.
In actual operation, regional traffic data with preset fields of 'exit station name' and 'entrance station name' is obtained, whether an exit station or an entrance station corresponding to each vehicle belongs to a target address is judged according to the regional traffic data corresponding to the obtained preset fields, if yes, the traffic data is the traffic data corresponding to the target address, and if not, the traffic data is not the traffic data corresponding to the target address, and therefore the traffic data is deleted.
And step 306, screening the regional traffic data, and deleting the traffic data of the non-target date.
The target date refers to a date on which data analysis of the self-driving tour is required, and the target date in this embodiment is a time of a certain day; more specifically, the target date is the time from 0:00:00 to 23:59:59 of a certain day, and specifically, the traffic data corresponding to the entry time and the exit date and time in the preset fields of the regional traffic data are obtained, and the regional traffic data with the entry time or the exit time within the target date are screened out, so that the traffic data meeting the requirement of the target time is obtained.
And 308, screening the regional traffic data, and deleting the traffic data corresponding to the travel distance smaller than the minimum travel distance.
It should be noted that, a trip with a trip distance exceeding 10km is generally defined as a trip of a self-driving tour, so that a minimum trip distance needs to be determined, and whether the trip distance of traffic data corresponding to each vehicle is greater than the minimum trip distance is judged; if the travel distance is greater than the minimum travel distance, the traffic data corresponding to the vehicle is in accordance with the definition of self-driving travel; if the traffic data is less than the preset threshold, the traffic data corresponding to the vehicle does not accord with the definition of the self-driving tour, and therefore the traffic data corresponding to the vehicle is deleted. In the embodiment, whether the travel distance corresponding to each vehicle is greater than 10km is judged by acquiring traffic data corresponding to a preset field of 'total mileage charged' in regional traffic data; if so, indicating that the traffic data corresponding to the vehicle conforms to the definition of the self-driving tour; if not, the traffic data does not accord with the definition of the self-driving tour, and therefore the traffic data corresponding to the vehicle is deleted.
And 310, screening the regional traffic data, and deleting the traffic data with the travel time length smaller than the minimum travel time length.
It should be noted that, a trip with a trip time exceeding 6 minutes is generally defined as a trip of the self-driving tour, that is, the minimum trip time of the self-driving tour is 6 minutes, and the output value of the self-driving tour needs to be greater than 0.1. In actual operation, acquiring regional traffic data with preset fields of 'entrance time' and 'exit date and time' in the regional traffic data, calculating the travel time length of the traffic data corresponding to each vehicle according to the exit time and the entrance time, and judging whether the calculated travel time length corresponding to each vehicle is greater than the minimum travel time length; if yes, the traffic data corresponding to the vehicle is in accordance with the definition of self-driving travel; if not, the traffic data corresponding to the vehicle is not in accordance with the definition of the self-driving tour, and therefore the traffic data corresponding to the vehicle is deleted.
Therefore, according to the method of the embodiment, the regional traffic data corresponding to the exit traffic data table in the target region are obtained, and the regional traffic data are screened according to the preset field, so that the target traffic data meeting the self-driving tour definition can be quickly and conveniently determined from a large amount of regional traffic data, and the efficiency of data analysis of the self-driving tour can be improved.
In addition, the daily data volume of the traffic data can reach the level of tens of millions, and in actual operation, processing tools such as Clickhouse and PostgreSQL can be used for data analysis. The ClickHouse is a column-type storage Database (DBMS), which is mainly used for on-line analytical processing query (OLAP) and can generate an analysis data report in real time by using SQL query. Specifically, the acquired regional traffic data is stored in Clickhouse, and when new traffic data is stored in a warehouse every time, the Clickhouse calculates whether the same license plate (the same vehicle) has a back-and-forth operation or not through a fog view. Since ClickHouse is a columnar database, its computation speed is several times faster than a traditional line database. The PostgreSQL is an object-relational database management system (ordms) of free software with very complete characteristics, and is an object-relational database management system based on postthres, version 4.2, developed by the computer system of the university of california. PostgreSQL supports most SQL standards and offers many other modern features such as complex queries, foreign keys, triggers, views, transaction integrity, multi-version concurrency control, etc. Another part of data computation is based on Apache Spark, which is a fast, general-purpose computing engine designed specifically for large-scale data processing. The data storage is PostgreSQL partition table; determining target traffic data according to data synchronization; data layered calculation, namely determining target index information according to the target traffic data, and landing to PostgreSQL; determining the target self-driving tour type corresponding to each user according to the target index information; and finally counting output results to the app layer landing PostgreSQL.
As can be seen, the efficiency of data analysis can be improved by using processing tools such as Clickhouse and PostgreSQL to perform data analysis, and other processing tools can be used in actual operation, which is not limited in this embodiment.
In one embodiment, determining target index information corresponding to a preset index according to target traffic data, and determining a target self-driving tour type corresponding to each user according to the target index information includes:
and determining the number of the strokes of each vehicle, the attribution of the stroke end and the stopping time according to the target traffic data.
Specifically, after the target traffic data is determined, the target traffic data is analyzed to determine the number of strokes corresponding to each vehicle. The number of trips refers to one-way trip from the starting point of the trip to the ending point of the trip by the user within a certain target date.
In the present embodiment, the method of determining the number of strokes of a vehicle includes: determining entry stations corresponding to all vehicles respectively, wherein the entry stations are the travel starting points of the self-driving tour of the user; judging the number of times of the same license plate number appearing at the entrance station, if the number of times of the same license plate number appearing at the entrance station is 1, indicating that the number of the strokes of the vehicle in the target date is 1; if the number of occurrences is 2, it indicates that the number of strokes of the vehicle within the target date is 2.
The purpose of determining the attribution of the trip end point is to judge whether the trip start point and the trip end point of the user belong to the same target address, so as to determine whether the self-driving tour corresponding to the user is a local self-driving tour or a cross-city self-driving tour.
Specifically, the dwell time refers to the time interval from the arrival at the end of the journey to the departure from the end of the journey again; the method comprises the steps of obtaining the departure time of a vehicle at a trip end point and the corresponding departure time when the vehicle departs from the trip end point again, and determining the staying time of a user at the trip end point according to the time difference between the departure time and the departure time of the vehicle.
And determining the target self-driving tour type corresponding to each user according to the number of the strokes, the attribution of the stroke end and the stopping time corresponding to each vehicle.
Specifically, for each user, the target self-driving tour type of the corresponding user is determined according to the combination condition of the target index information of the user.
Fig. 4 is a schematic flowchart illustrating a process of determining a target self-driving tour type of each user according to an embodiment of the present invention. As a preferred embodiment, determining the target self-driving tour type corresponding to each user according to the number of trips, the attribution of the trip end point and the stop time corresponding to each vehicle respectively comprises:
if the number of the strokes is 1 and the attribution of the stroke end point and the attribution of the stroke starting point are in the same attribution, determining that the corresponding target self-driving tour type of the user is local self-driving night tour;
if the number of the strokes is 1, determining that the target self-driving tour type of the corresponding user is cross-city self-driving night tour if the attribution of the stroke end point and the attribution of the stroke starting point are not in the same attribution;
if the number of the strokes is 2, the stay time is longer than the preset time, and the attribution of the stroke end point and the attribution of the stroke starting point are in the same attribution, determining that the corresponding target self-driving tour type of the user is local self-driving daily tour;
if the number of the strokes is 2, the stop time is longer than the preset time length, the attribution of the stroke end point and the attribution of the stroke starting point are not in the same attribution, and the target self-driving tour type of the corresponding user is determined to be cross-city daily tour.
Specifically, in this embodiment, target index data such as the number of trips, the end of trips, the stay time, and the like of the user are acquired; then judging whether the number of the strokes is 1 or not; if the number of the trips is 1, the user is one-way trip in the target date, and the user stays overnight at the end of the trip; then, whether the home locations of the travel starting point and the travel end point are the same home location is further judged, if yes, the user is represented to travel in the same home location, and therefore the target self-driving travel type of the user is determined to be local self-driving overnight travel; if the target self-driving tour type is not the same attribution, the user is not in the same attribution for going out, and therefore the target self-driving tour type of the user is determined to be self-driving over-the-ground city and night tour;
if not, judging whether the number of the strokes is 2; if the number is 2, the user is shown to come and go to the travel starting point and the travel ending point in the target date; then further judging whether the staying time of the user is longer than a preset time length; the preset time duration is generally set according to the average travel time or the minimum travel time, and the preset time duration is preferably set to be 6 hours in the embodiment, namely, when the staying time of the user at the travel end is determined to be greater than or equal to 6 hours, the user is determined to travel at the travel end; then, whether the home locations of the travel starting point and the travel end point are the same home location is further judged, if yes, the user is represented to travel in the same home location, and therefore the target self-driving travel type of the user is determined to be local self-driving daily travel;
if the target self-driving tour type is not the same attribution, the user is not in the same attribution for going out, and therefore the target self-driving tour type of the user is determined to be self-driving one-day tour across the city; and if the stay time of the user is less than the preset time length, the user is indicated to only pass through the travel end point, and the user is judged not to be self-driving tour.
Therefore, in the embodiment, the self-driving tour type of the user can be determined according to the number of the trips, the attribution of the trip end point and the stay time, and the determination process is convenient, fast and accurate.
On the basis of the foregoing embodiment, the present embodiment further describes and optimizes the technical solution, and specifically, in the present embodiment, the method further includes:
and counting the average stay time and the average driving distance respectively corresponding to different self-driving tour types according to the target index information.
Specifically, the average stay time refers to an average value of stay times of users of the same self-driving tour type at the stroke end; for example, for a self-driving tour type of a local self-driving daily tour, the stay time of each user of the self-driving tour type at the trip end is respectively obtained, and then the average stay time corresponding to the self-driving tour type is calculated by using each stay time.
Specifically, the average travel distance refers to an average distance of travel trips of users of the same self-driving travel type; for example, for a self-driving tour type of local self-driving daily tour, the travel trips of each user of the self-driving tour type are respectively acquired, and then the average travel distance corresponding to the self-driving tour type is calculated by using the travel trips.
It should be noted that the corresponding average stay time and average travel distance may be determined for a certain driving tour type, or the corresponding average stay time and average travel distance may be determined for each driving tour type, which is not limited in this embodiment.
Therefore, the average stay time and the average driving distance corresponding to different driving types are further determined, and the difference of the driving types can be conveniently and intuitively determined.
In addition, in actual operation, after the average stay time and the average driving distance corresponding to each driving type are determined, the determined information can be displayed by using the preset display device, namely, each driving type and the average stay time and the average driving distance corresponding to each driving type are visually displayed by using the preset display device, so that the convenience of checking the driving types and the differences can be further improved.
On the basis of the foregoing embodiment, the present embodiment further describes and optimizes the technical solution, and specifically, in the present embodiment, the method further includes:
acquiring self-driving tour information of the same time period in a plurality of different years; the self-driving tour information comprises the number of each driving tour type, the average stay time and the average driving distance of each driving tour type;
and analyzing the driving information of each driver to determine the development trend of different driving types.
Specifically, the self-driving tour information of the same time period in different years refers to self-driving tour information of different years and the same date, for example, self-driving tour information of labor sections of each 4 consecutive years in 2018-2021. The self-driving tour information comprises the number of each driving tour type, for example, the number of four self-driving tour types of 5 months and 1 day of each year in 4 consecutive years in 2018-2021 year, and the average stay time and the average driving distance corresponding to the four self-driving tour types of each year are obtained.
In a specific embodiment, the self-driving information of each year is synthesized and analyzed by acquiring the self-driving information of target dates of each year, for example, the number of different self-driving types of mid-autumn festival of each year is compared to determine whether the number of different self-driving types increases with the increase of the year or the number of different self-driving types decreases with the increase of the year; or, the average stay time according to the type of the self-driving tour in each different year, for example, whether the average stay time of the local self-driving daily tour in each different year is increased or decreased year by year, and the like. And determining the development trend of different self-driving tour types according to the change condition of the number of the self-driving tour types and the change condition of the average stay time and the average driving distance of each self-driving tour type.
The embodiment analyzes the self-driving tour information, determines the same-proportion expansion conditions of different self-driving tour types, and determines the development trends of different self-driving tour types, so as to judge the development conditions of the tourism industry; and the number, the range and the duration of the self-driving tour can be effectively monitored according to the analysis result when the weekend or holiday comes.
On the basis of the foregoing embodiment, the present embodiment further describes and optimizes the technical solution, and specifically, in the present embodiment, the method further includes:
and determining the attraction degree of each travel end point by using each travel end point of each target self-driving travel type and the average stay time and the average driving distance corresponding to each target self-driving travel type.
Specifically, in this embodiment, after determining the target self-driving tour type corresponding to each user and obtaining the trip end point, the average stopping time, and the average driving distance corresponding to each target self-driving tour type, the corresponding attraction degree of the trip end point is determined according to the average stopping time and the average driving distance. It is understood that, in general, the longer the average stopping time and the longer the average travel distance correspond to the end of the trip, the greater the attraction force indicating the end of the trip.
The embodiment can determine the driving destination of the hot self by further determining the attraction degree of the travel destination, thereby guiding local scenic spots/villages to recruit, enlarge the scale, assist the village travel development, and provide a basis for the decision of the subsequent development of the travel industry by combining with the village revivification plan.
It should be understood that, although the steps in the flowcharts related to the above embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in each flowchart related to the above embodiments may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
In one embodiment, as shown in fig. 5, there is provided a data analysis device for self-driving tour, including: a setting module 502, an obtaining module 504, and an analyzing module 506, wherein:
a setting module 502, configured to set multiple self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
an obtaining module 504, configured to obtain target traffic data corresponding to a target address in a target date;
and the analysis module 506 is configured to determine target index information corresponding to the preset index according to the target traffic data, and determine a target self-driving tour type of each user according to the target index information.
In one embodiment, the obtaining module includes:
the acquisition submodule is used for acquiring regional traffic data corresponding to the exit traffic data table in the target region; wherein, the target address belongs to the target area;
and the screening submodule is used for screening the regional traffic data according to the vehicle type, the target address, the target date, the minimum travel distance and the minimum travel time length to obtain the target traffic data of the target address in the target date.
In one embodiment, the analysis module comprises:
the first determining submodule is used for determining the number of the strokes of each vehicle, the attribution of the stroke end and the stopping time according to the target traffic data;
and the second determining submodule is used for determining the target self-driving tour type corresponding to each user according to the number of the strokes, the attribution of the stroke end point and the stopping time corresponding to each vehicle.
In one embodiment, the second determination submodule includes:
the first determining unit is used for determining that the self-driving tour type of the corresponding user is local self-driving overnight tour when the number of the strokes is 1 and the attribution of the stroke end point and the attribution of the stroke start point are in the same attribution;
the second determining unit is used for determining that the self-driving tour type of the corresponding user is self-driving overnight tour across cities when the number of the trips is 1 and the attribution of the trip end point and the attribution of the trip start point are not in the same attribution;
the third determining unit is used for determining that the corresponding type of the self-driving tour of the user is local self-driving one-day tour when the number of the strokes is 2, the stay time is longer than the preset time length, and the attribution of the stroke end point and the attribution of the stroke starting point are in the same attribution;
and the fourth determining unit is used for determining that the corresponding type of the self-driving tour of the user is cross-city one-day tour when the number of the trips is 2, the stay time is longer than the preset time length, and the attribution of the trip end point and the attribution of the trip start point are not in the same attribution.
In one embodiment, a data analysis device for self-driving tour further includes:
and the statistical module is used for counting the average stay time and the average driving distance respectively corresponding to different self-driving tour types according to the target index information.
In one embodiment, a data analysis device for self-driving tour further includes:
the first analysis module is used for acquiring self-driving tour information of the same time period in a plurality of different years; the self-driving tour information comprises the number of each driving tour type, the average stay time and the average driving distance of each driving tour type;
and the second analysis module is used for analyzing the driving information of each driving tour and determining the development trend of different driving tour types.
In one embodiment, a data analysis device for self-driving tour further includes:
and the third analysis module is used for determining the attraction degree of each travel end point by using each travel end point of each driving type and the average stay time and the average driving distance corresponding to each driving type.
For specific limitations of the data analysis device for the self-driving tour, reference may be made to the above limitations of the data analysis method for the self-driving tour, and details thereof are not repeated here. All or part of the modules in the data analysis device for the self-driving tour can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the data analysis equipment for the self-driving tour, and can also be stored in a memory in the data analysis equipment for the self-driving tour in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a self-driving tour data analysis device is provided, and the self-driving tour data analysis device may be a terminal, and the internal structure of the self-driving tour data analysis device may be as shown in fig. 6. The data analysis equipment for the self-driving tour comprises a processor, a memory, a communication interface, a display screen and an input device which are connected through a system bus. Wherein the processor of the data analysis device for self-driving travel is configured to provide computational and control capabilities. The memory of the data analysis device for the self-driving tour comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the data analysis device for the self-driving tour is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a self-driving tour data analysis method. The display screen of the data analysis equipment for the self-driving tour can be a liquid crystal display screen or an electronic ink display screen, and the input device of the data analysis equipment for the self-driving tour can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the data analysis equipment for the self-driving tour, and an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the structure shown in fig. 6 is a block diagram of only a portion of the structure related to the present application, and does not constitute a limitation on the self-driving tour data analysis apparatus to which the present application is applied, and a particular self-driving tour data analysis apparatus may include more or fewer components than those shown in the figure, or may combine some components, or have a different arrangement of components.
In one embodiment, there is provided a data analysis device for self-driving travel, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the following steps when executing the computer program:
setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
acquiring target traffic data corresponding to a target address in a target date;
and determining target index information corresponding to the preset index according to the target traffic data, and determining the target self-driving tour type of each user according to the target index information.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
acquiring target traffic data corresponding to a target address in a target date;
and determining target index information corresponding to the preset index according to the target traffic data, and determining the target self-driving tour type of each user according to the target index information.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of analyzing data for self-driving travel, the method comprising:
setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
acquiring target traffic data corresponding to a target address in a target date;
and determining target index information corresponding to the preset index according to the target traffic data, and determining the target self-driving tour type of each user according to the target index information.
2. The method of claim 1, wherein the obtaining of the target traffic data corresponding to the target address within the target date comprises:
acquiring regional traffic data corresponding to an exit traffic data table in a target region; wherein the target address is affiliated with the target area;
and screening the regional traffic data according to the vehicle type, the target address, the target date, the minimum travel distance and the minimum travel time length to obtain the target traffic data of the target address in the target date.
3. The method of claim 1, wherein the determining target index information corresponding to the preset index according to the target traffic data and determining a target self-driving tour type corresponding to each user according to the target index information comprises:
determining the number of strokes of each vehicle, the attribution of the stroke end point and the stopping time according to the target traffic data;
and determining the target self-driving tour type corresponding to each user according to the number of the trips, the attribution of the trip end point and the stopping time corresponding to each vehicle.
4. The method of claim 3, wherein the determining the target self-driving tour type corresponding to each user according to the number of trips, the attribution of the trip end point and the stopping time corresponding to each vehicle comprises:
if the number of the strokes is 1 and the attribution of the stroke end point and the attribution of the stroke starting point are in the same attribution, determining that the corresponding target self-driving tour type of the user is the local self-driving night tour;
if the number of the strokes is 1, determining that the attribution of the stroke end point is not the same as the attribution of the stroke start point, and determining that the corresponding target self-driving tour type of the user is the cross-city self-driving night tour;
if the number of the strokes is 2, the stay time is longer than the preset time length, and the attribution of the stroke end point and the attribution of the stroke starting point are in the same attribution, determining that the corresponding target self-driving tour type of the user is the local self-driving daily tour;
if the number of the strokes is 2, the stop time is longer than the preset time length, the attribution of the stroke end point and the attribution of the stroke starting point are not in the same attribution, and the target self-driving tour type of the corresponding user is determined to be the cross-city one-day tour.
5. The method of claim 1, further comprising:
and calculating the average stay time and the average driving distance respectively corresponding to different self-driving tour types according to the target index information.
6. The method of claim 5, further comprising:
acquiring self-driving tour information of the same time period in a plurality of different years; wherein the self-driving tour information includes the number of each self-driving tour type, the average stay time and the average travel distance of each self-driving tour type;
and analyzing the self-driving tour information to determine the development trend of different self-driving tour types.
7. The method of claim 3, further comprising:
and determining the attractive force degree of each travel end point by using each travel end point of each self-driving travel type and the average residence time and the average driving distance corresponding to each self-driving travel type.
8. A data analysis device for self-driving travel, the device comprising:
the setting module is used for setting a plurality of self-driving tour types according to index information of preset indexes; the self-driving tour types comprise a local self-driving one-day tour, a local self-driving night tour, a cross-city self-driving one-day tour and a cross-city self-driving night tour; the preset index is an information index representing self-driving tour;
the acquisition module is used for acquiring target traffic data corresponding to a target address in a target date;
and the analysis module is used for determining target index information corresponding to the preset index according to the target traffic data and determining the target self-driving tour type of each user according to the target index information.
9. A data analysis device for self-driving travel, comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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