CN113256388A - Method for predicting commodities to be sold on unmanned vehicle - Google Patents
Method for predicting commodities to be sold on unmanned vehicle Download PDFInfo
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- CN113256388A CN113256388A CN202110662015.4A CN202110662015A CN113256388A CN 113256388 A CN113256388 A CN 113256388A CN 202110662015 A CN202110662015 A CN 202110662015A CN 113256388 A CN113256388 A CN 113256388A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0639—Item locations
Abstract
The disclosure relates to the technical field of unmanned driving, and provides a method for forecasting a commodity to be sold on an unmanned vehicle. The method is applied to unmanned equipment, namely automatic driving equipment or an unmanned vehicle. According to the method, key information, namely the end point and the time of reaching the end point of a behavior track, is acquired by analyzing the behavior data of a user, based on the key information and the operation time and the operation area of an unmanned vehicle, the four data are matched according to a preset rule to form a task table, cells which are not matched with the track end point and the reaching time of the user in the table are removed, the remaining cells form a target task table, corresponding user figures are counted according to the areas in the target task table, commodities to be sold are matched based on the main figures, different commodities to be sold are respectively distributed to the unmanned vehicle in each area to be operated in each time period, and therefore the problem that the user cannot know the commodities of the unmanned vehicle at the destination in advance is solved.
Description
Technical Field
The disclosure relates to the technical field of unmanned driving, in particular to a method for forecasting commodities to be sold on an unmanned vehicle and equipment for realizing the method.
Background
An unmanned vehicle, also called an automatic vehicle, an unmanned vehicle or a wheeled mobile robot, is an integrated and intelligent new-era technical product integrating multiple elements such as environment perception, path planning, state recognition, vehicle control and the like, and achieves the purpose of unmanned driving by equipping the vehicle with intelligent software and various sensing devices.
The actual operation scene of unmanned vehicle includes unmanned taxi, unmanned retail vehicle to and unmanned delivery car etc. to unmanned retail vehicle, in the actual operation scene, it sells the place and sells goods and all can change according to actual crowd's condition and the regional allotment condition of vehicle, in order to obtain maximize operation income, when this kind of change appears, potential user will be unable to learn change information, more can't browse in advance which goods will be sold to unmanned vehicle, lead to the user to run off. In practice, the single user is often a user who touches the unmanned vehicle on the spot, and the touch is random. In addition, the touring that is operating sells the commodity that unmanned car sells, also not necessarily reach user's true demand, lead to unmanned car can't attract more users.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method for predicting a product to be sold on an unmanned vehicle, so as to solve the problem and issue that a user cannot know the product to be sold on the unmanned vehicle in advance in the prior art.
In a first aspect of the embodiments of the present disclosure, a method for forecasting a commodity to be sold on an unmanned vehicle includes the following steps:
determining a plurality of operation time periods of the unmanned vehicle and at least one operation area in each operation time period;
acquiring track end point data in a system of a user terminal and predicted arrival time data corresponding to the track end point at a certain time period;
rejecting data of which the track end point does not fall into an operation area and data of which the predicted arrival time does not fall into an operation time period;
based on the removed data, establishing a task form in the time period, wherein the form comprises each operation time period information and at least one operation area information corresponding to each operation time period, and the operation area information comprises an operation area, a track end included in the geographical position of the operation area and user information corresponding to the included track end;
removing operation areas with track end points not meeting preset requirements from the task forms, and forming target task forms after removal;
extracting portrait information of a user corresponding to a track end point in the operation area aiming at each operation area in the target task form; counting main portrait information based on the portrait information, and matching corresponding commodities to be sold according to the main portrait information;
distributing corresponding commodities to be sold to the unmanned vehicles to be operated in the operation areas in the target task list, and updating the commodities to be sold to user terminals corresponding to the operation areas in the target task list so as to forecast information of the commodities to be sold on the unmanned vehicles of the users;
in another aspect, the system includes: navigation software, motion monitoring software, vehicle boarding software, behavior statistics software, calendar information or attendance software;
in another aspect, the primary representation information includes user representation information having a maximum number of user representation information;
in another aspect, the method further comprises the steps of: establishing a binding relationship between the unmanned vehicle and the user terminal;
in another aspect, the plurality of operational time periods are within a day, or within a week, or within a month;
on the other hand, the plurality of operation time periods comprise time periods with the same duration and time periods with different durations;
in another aspect, the portrait information of the user includes: age, occupation, gender, and historical shopping information;
on the other hand, in the task table, if a plurality of track end points of the same user are included in the same time period and the plurality of track end points are located in different operation areas, the commodities to be sold in the plurality of operation areas are recommended to the user at the same time; and if the track end points of the users comprise a plurality of track end points which are respectively positioned in different time periods, recommending the corresponding commodities to be sold to the users according to the sequence of the time periods.
In another aspect, the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the forecasting method when executing the computer program.
In another aspect, the present invention further provides a computer-readable storage medium, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the forecasting method.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: by analyzing the user behavior, the destination and the time of reaching the unmanned vehicle are obtained, user preferences in each time period and each region are counted according to the region and the time, so that commodities to be sold can be distributed according to the counting result, and the user is informed in advance, so that the user can know the situation of selling the commodities by the unmanned vehicle when the user reaches the destination in advance.
Drawings
To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a schematic flow diagram of an embodiment of the present invention;
FIG. 2 is a task form diagram of an embodiment of the present invention;
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
The unmanned vehicle described in this application, automatic driving vehicle promptly for sell goods, unmanned vehicle take the automatic driving mode to go according to predetermined orbit or go with the mode of patrolling a journey on the road, and the user can look over the goods that unmanned vehicle sold at the current time through software such as APP or little letter applet. When a user needs to go to a certain destination, for example, the user needs to go to a company for work in the next morning, or needs to go to a school in the morning, or runs to a set place half an hour later, the situation of selling commodities of an unmanned vehicle at a future time or moment needs to be known in advance, but the selling information of the unmanned vehicle is updated in real time on software, so that the requirement of the user for browsing the commodities in advance cannot be met; or updated in advance according to a predetermined period, which does not match with the arrival time of the user, still results in that the user cannot accurately and timely browse the goods for sale.
As shown in fig. 1, the method for forecasting the commodities to be sold on the unmanned vehicle includes the following steps:
firstly, an operation office of the unmanned vehicle arranges operation time periods of each unmanned vehicle every day and operation areas where the unmanned vehicles are located in each operation time period in advance according to actual conditions, wherein the operation time periods generally comprise three operation time periods of morning, noon and evening, the duration of each time period is two hours, the operation areas can be multiple or one, and when the operation time periods are multiple, the unmanned vehicles need to drive through the two operation areas within two hours; the mobile phone of the consumer is used as a user terminal, and needs to establish a binding relationship with an operation platform of the unmanned vehicle in advance.
Then, the operation platform acquires a track end point in a system of the user terminal and an expected arrival time corresponding to the track end point at a certain time period, and the acquisition at the certain time period may specifically be that the unmanned vehicle acquires the track end point and the expected arrival time of the current day before the vehicle starts every morning or acquires the track end point and the expected arrival time of the user before the vehicle starts to receive every noon.
Then, data of which the track end point does not fall into an operation area and data of which the predicted arrival time does not fall into an operation time period are removed, the operation platform establishes a task form in the time period based on the removed data, the form comprises information of each operation time period and at least one operation area in each time period, and the information of the operation areas comprises the operation areas, the track end points included in the geographical positions of each operation area and user information corresponding to the included track end points.
Then, removing operation areas with the track end points not meeting the requirement of the preset number from the task form so as to avoid wasting operation resources, and forming a target task form after removal;
FIG. 2 is a schematic diagram of a task form that has been created. The unmanned vehicle starts to count the track terminal and the estimated arrival time of the users 1-12 with the established binding relationship at 6:00 in the morning; rejecting users 11 whose predicted arrival time is not in the unmanned vehicle operation time period, and users 12 whose track end point is not in the operation area A-C, wherein the unmanned vehicle operation time period can be the time period of the day, and can also span one day or one week; task forms are created and formed for the remaining users 1-10. If the preset number requirement is greater than or equal to 2 for the number of places (namely track end points) in the area B with the operating time of 11:00-13:00 and the area A with the operating time of 17:00-19:00, the two areas in the table are removed to form a target task form because the preset number requirement is not met.
Extracting portrait information of a user corresponding to a track end point in the operation area aiming at each operation area in the target task form; counting main portrait information based on the portrait information, and matching corresponding commodities to be sold for each area in the target task form according to the main portrait information;
distributing corresponding commodities to be sold to unmanned vehicles to be operated in the operation area in the target task list, and updating the commodities to be sold to user terminals corresponding to the operation area in the target task list so as to inform the users of information to be sold of the unmanned vehicles;
in addition, the user terminal system includes: navigation software, motion monitoring software, vehicle boarding software, behavior statistics software, calendar information or attendance software; the software can accurately collect action data of the user, such as the terminal point of an action track and the predicted time of reaching the terminal point, and report the data to the unmanned vehicle operation platform, and before the platform acquires the data, the platform can inquire whether the user terminal allows to acquire the action data in advance.
The main portrait information comprises user portrait information with the largest number of user portrait information, so that the commodity to be sold finally pushed by the unmanned vehicle can meet the requirements of most people.
On the other hand, the plurality of operation time periods comprise time periods with the same duration and time periods with different durations; generally, the operation time periods and the operation areas of the unmanned vehicles are arranged in units of days, wherein the operation time periods are three in each day, and each time period is 2 hours, and can also be 1 hour or 3 hours. The operation time period of the unmanned vehicle can be arranged in week units, and the operation time period and the operation area of the next week are arranged on the last day of each week.
For the portrait information of the user, the age, occupation, sex and historical shopping information of the user can be included; since the unmanned vehicle is provided with a plurality of sensors, the image information of the user can be collected, and therefore the portrait information of the user can comprise a plurality of biological characteristics.
It should be noted that, in the task table, there is a special case that if the trajectory end points of the same user include multiple trajectory end points in the same time period, and the multiple trajectory end points are located in different operation areas, the finally obtained commodities to be sold in the multiple operation areas are recommended to the user at the same time; and if the track end points of the users comprise a plurality of track end points which are respectively positioned in different time periods, recommending the corresponding commodities to be sold to the users according to the sequence of the time periods.
The step of updating the goods to be sold to the user terminal corresponding to the operation area in the target task form to inform the user of the information of selling the goods by the unmanned vehicle may be performed immediately after the goods to be sold are distributed to the unmanned vehicle, or may be performed when the current time is a certain time period away from the operation time period of the unmanned vehicle, for example, after 6:00 goods sold in each time period and each area are distributed to each unmanned vehicle operated on the same day, the user terminal is immediately informed of the goods to be sold, or the user is informed at a time half an hour or 1 hour ahead of the initial operation time 13:00 in the afternoon. For unmanned vehicles with high operation efficiency, particularly high goods-loading speed, a second notification mode can be adopted.
In another aspect, the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the forecasting method when executing the computer program. In another aspect, the present invention further provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the forecasting method.
The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.
Claims (10)
1. A method for forecasting commodities to be sold on an unmanned vehicle is characterized by comprising the following steps:
determining a plurality of operation time periods of the unmanned vehicle and at least one operation area in each operation time period;
acquiring track end point data in a system of a user terminal and predicted arrival time data corresponding to the track end point at a certain time period;
rejecting data of which the track end point does not fall into an operation area and data of which the predicted arrival time does not fall into an operation time period;
based on the removed data, establishing a task form in the time period, wherein the form comprises each operation time period information and at least one operation area information corresponding to each operation time period, and the operation area information comprises an operation area, a track end included in the geographical position of the operation area and user information corresponding to the included track end;
removing operation areas with track end points not meeting preset requirements from the task forms, and forming target task forms after removal;
extracting portrait information of a user corresponding to a track end point in the operation area aiming at each operation area in the target task form; counting main portrait information based on the portrait information, and matching corresponding commodities to be sold according to the main portrait information;
and distributing corresponding commodities to be sold to the unmanned vehicles to be operated in the operation areas in the target task list, and updating the commodities to be sold to the user terminals corresponding to the operation areas in the target task list so as to forecast the information of the commodities to be sold on the unmanned vehicles of the users.
2. The method of claim 1, wherein the system comprises: navigation software, motion monitoring software, vehicle boarding software, behavior statistics software, calendar information or attendance software.
3. The method of claim 2, wherein the primary representation information includes a maximum number of user representation information.
4. The method of claim 3, further comprising the step of: and establishing a binding relationship between the unmanned vehicle and the user terminal.
5. The method of claim 4, wherein the plurality of operational time periods are within a day, or within a week, or within a month.
6. The method of claim 5, wherein the plurality of operating time periods comprise time periods of the same duration and time periods of different durations.
7. The method of claim 6, wherein the user's portrait information comprises: age, occupation, gender, and historical shopping information.
8. The method according to any one of claims 1 to 5, wherein in the task table, if the track end points of the same user are multiple in the same time period and the multiple track end points are located in different operation areas, the commodities to be sold in the multiple operation areas are recommended to the user at the same time; and if the track end points of the users comprise a plurality of track end points which are respectively positioned in different time periods, recommending the corresponding commodities to be sold to the users according to the sequence of the time periods.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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