CN112051843B - Path planning method and device based on order prediction, robot and storage medium - Google Patents
Path planning method and device based on order prediction, robot and storage medium Download PDFInfo
- Publication number
- CN112051843B CN112051843B CN202010790878.5A CN202010790878A CN112051843B CN 112051843 B CN112051843 B CN 112051843B CN 202010790878 A CN202010790878 A CN 202010790878A CN 112051843 B CN112051843 B CN 112051843B
- Authority
- CN
- China
- Prior art keywords
- merchant
- candidate
- target
- position point
- linear distance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000004590 computer program Methods 0.000 claims description 24
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 description 14
- 230000008569 process Effects 0.000 description 10
- 230000006870 function Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 230000004044 response Effects 0.000 description 4
- 230000032258 transport Effects 0.000 description 4
- 235000013361 beverage Nutrition 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 235000012054 meals Nutrition 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 235000011888 snacks Nutrition 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 240000005856 Lyophyllum decastes Species 0.000 description 1
- 235000013194 Lyophyllum decastes Nutrition 0.000 description 1
- 244000269722 Thea sinensis Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000013278 delphi method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012882 sequential analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
-
- 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]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Business, Economics & Management (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Aviation & Aerospace Engineering (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application is suitable for the technical field of path planning, and provides a path planning method, a path planning device, a robot and a storage medium based on order prediction. In the embodiment of the application, the browsing times of commodity pages of a first candidate merchant, the candidate position points of the first candidate merchant and the current position point of a robot in a preset time period are obtained; calculating a linear distance between the first candidate merchant and the robot according to the candidate position point and the current position point; selecting a first candidate merchant meeting preset conditions as a target merchant according to the browsing times and the linear distance; and planning a path according to the current position point and the target position point of the target merchant, so that the efficiency of the express delivery personnel for transporting the goods is improved.
Description
Technical Field
The application belongs to the technical field of path planning, and particularly relates to a path planning method, a path planning device, a robot and a storage medium based on order prediction.
Background
Along with the development of society, the takeaway industry is more and more common in people's life, and people are also more and more suitable for this kind of convenient and fast takeaway mode. In the take-away industry, the distribution of sellers and buyers is quite extensive, and therefore, there is a need to transport goods by using couriers. In real life, the express delivery personnel generally stay on standby in a certain place when not obtaining an order, and after the express delivery personnel obtains a real-time order, the express delivery personnel can go to a merchant position corresponding to the order to pick up goods, and in the process, the situation that the express delivery personnel walks for a certain distance and then receives another order often occurs. Under the condition, the courier can not receive different orders well, so that the cargo transportation efficiency is low.
Disclosure of Invention
The embodiment of the application provides a path planning method, a path planning device, a robot and a storage medium based on order prediction, which can solve the problem of lower efficiency when express delivery personnel transport goods.
In a first aspect, an embodiment of the present application provides a path planning method based on order estimation, including:
acquiring browsing times of commodity pages of a first candidate merchant, candidate position points of the first candidate merchant and current position points of a robot in a preset time period;
calculating a linear distance between the first candidate merchant and the robot according to the candidate position point and the current position point;
Selecting a first candidate merchant meeting preset conditions as a target merchant according to the browsing times and the linear distance;
and planning a path according to the current position point and the target position point of the target merchant.
Optionally, before selecting the first candidate merchant meeting the preset condition as the target merchant according to the browsing times and the linear distance, the method includes:
Obtaining merchant types of the first candidate merchants, determining type weight coefficients according to the merchant types, and giving the type weight coefficients to the browsing times; and/or obtaining the region attribute of the region where the first candidate merchant is located, determining an attribute weight coefficient according to the region attribute, and giving the attribute weight coefficient to the linear distance.
Optionally, before selecting the first candidate merchant meeting the preset condition as the target merchant according to the browsing times and the linear distance, the method further includes:
acquiring browsing time of a commodity interface of the first candidate merchant;
and determining a time weight coefficient according to the time difference value between the browsing time and a preset time threshold value, and giving the time weight coefficient to the browsing times.
Optionally, the selecting, according to the browsing times and the linear distance, the first candidate merchant meeting the preset condition as the target merchant includes:
When the difference value between the browsing times of the commodity pages of the first candidate merchant is larger than a preset first threshold value, selecting the first candidate merchant corresponding to the maximum commodity interface browsing times as a target merchant.
Optionally, the selecting, according to the browsing times and the linear distance, the first candidate merchant meeting the preset condition as the target merchant further includes:
And when the difference value between the browsing times of the commodity pages of the first candidate merchant is smaller than a preset second threshold value, selecting the first candidate merchant corresponding to the minimum linear distance as a first target merchant.
Optionally, after selecting the first candidate merchant corresponding to the minimum linear distance as the first target merchant, the method includes:
calculating a merchant linear distance between a second candidate merchant and the first target merchant, and selecting a second candidate merchant corresponding to the merchant linear distance smaller than a preset third threshold as the second target merchant; the second candidate merchant is a candidate merchant outside the first target merchant;
And determining the target position point according to the candidate position point of the first target merchant and the candidate position point of the second target merchant.
Optionally, before calculating the linear distance between the first candidate merchant and the robot according to the candidate location point and the current location point, the method includes:
calculating candidate merchant linear distances between the first candidate merchants according to the candidate position points of the first candidate merchants;
forming a candidate merchant set by a first candidate merchant with the linear distance of the candidate merchant smaller than a preset fourth threshold value;
And selecting a central position point from the position areas corresponding to the candidate merchant sets, and taking the central position point as the candidate position point of the candidate merchant sets.
In a second aspect, an embodiment of the present application provides a path planning apparatus based on order estimation, including:
The acquisition module is used for acquiring the browsing times of the commodity page of the first candidate merchant, the candidate position point of the first candidate merchant and the current position point of the robot in a preset time period;
the calculation module is used for calculating the linear distance between the first candidate merchant and the robot according to the candidate position point and the current position point;
The selecting module is used for selecting a first candidate merchant meeting preset conditions as a target merchant according to the browsing times and the linear distance;
And the path planning module is used for carrying out path planning according to the current position point and the target position point of the target merchant.
In a third aspect, an embodiment of the present application provides a robot, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements any one of the steps of the order-based estimated path planning method described above when the computer program is executed.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements any one of the steps of the path planning method based on order estimation.
In a fifth aspect, embodiments of the present application provide a computer program product for, when run on a robot, causing the robot to perform any of the order-based pre-estimation path planning methods of the first aspect above.
According to the method and the system for the goods transportation, the first candidate merchant which can generate orders at present and the browsing times corresponding to the first candidate merchant are obtained through obtaining the browsing times of the commodity pages of the first candidate merchant in a preset time period, the candidate position point of the first candidate merchant and the current position point of the robot are obtained, the linear distance between the first candidate merchant and the robot is calculated according to the obtained candidate position point and the current position point, the robot, namely an express person for goods transportation, can obtain the distance between the first candidate merchant and the current position of the robot through the calculated linear distance, so that the follow-up robot can obtain a target merchant suitable for the current robot to go to from the first candidate merchant according to the obtained browsing times and the linear distance, and then path planning is carried out according to the current position of the robot and the target position point of the target merchant, so that the robot goes to the target merchant which the customer will purchase and is relatively most suitable for the robot to go to in advance, and the efficiency of the whole goods transportation of the robot is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a first method for planning a path based on order estimation according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a second flow chart of a path planning method based on order estimation according to an embodiment of the present application;
FIG. 3 is a third flow chart of a path planning method based on order estimation according to an embodiment of the present application;
FIG. 4 is a fourth flowchart of a path planning method based on order estimation according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a path planning apparatus based on order estimation according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a robot according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application 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 application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Fig. 1 is a schematic flow chart of a path planning method based on order estimation in an embodiment of the present application, an execution subject of the method may be a robot, and an express person carries the robot to transport goods or the robot directly transports goods, and as shown in fig. 1, the path planning method based on order estimation may include the following steps:
Step S101, acquiring browsing times of commodity pages of a first candidate merchant, candidate position points of the first candidate merchant and current position points of a robot in a preset time period.
In this embodiment, since the merchant can provide the corresponding commodity page for the user to order the meal through the commercial website of the merchant or the affiliated take-away platform thereof, the number of times of browsing the commodity page of the merchant in a certain period of time can be determined by acquiring the link access number of the commercial website or the take-away platform of the merchant. The location points may be determined in an electronic map using positioning techniques. The first candidate merchants are merchants likely to generate orders, and can be two or more than two; the preset time period can be determined according to the dining time period of a large-scale user obtained by big data, the courier can request according to the current self situation or the custom requirement of the courier can be determined, and the preset time period can be determined by one or more combinations of the above modes, but is not limited herein; the candidate location points are location information of a first candidate merchant which may generate an order; the current position point is the current position information of the courier.
It can be understood that the robot carried by the express personnel processes the acquired information, so that the robot obtains the optimal path information and informs the express personnel to enable the express personnel to advance according to the optimal path information, thereby improving the possibility of acquiring orders and improving the efficiency of cargo transportation; the related information of the first candidate merchant and the position information of the courier can be acquired by the courier through the carried robot, and the courier judges the most likely generated order and the current walking direction according to the information; it is also possible that a server (e.g., a server of an express delivery platform/company) processes the above information and informs the courier of the optimal path information.
Optionally, the courier may request to determine the preset time period according to the current self situation, specifically: when the express personnel does not obtain the order, the express personnel may stay on a certain place or walk everywhere without the purpose, so that when the current express personnel does not obtain the order, a request instruction is sent to the robot, the robot can obtain the moment of the request instruction, and the preset time range including the moment is divided according to the moment, so that the preset time period is determined. For example, the timing of acquiring the request instruction is 11:12, if the time range is 5 minutes, the preset time period corresponds to 11:07 to 11:17.
The specific example is not limited, the merchant commercial website or the take-out platform determines the number of times of browsing the commodity page of the first candidate merchant by collecting the number of link accesses, and the number of times of browsing the commodity page of the first candidate merchant can be sent to a server of the robot or the unified scheduling robot through a preset protocol or a transmission path, meanwhile, the server of the robot or the unified scheduling robot can obtain the current position information of the first candidate merchant and the courier, and if the server obtains the related information, the related information is sent to the corresponding robot, so that the robot or the courier can process according to the related information. The merchant commercial website or the take-out platform may also process the number of times of browsing the commodity page of the first candidate merchant in advance, and perform a screening operation according to a preset rule, for example, screening out a part of first candidate merchants with fewer browsing times, and then transmitting the browsing times of the remaining first candidate merchants outwards.
Optionally, as shown in fig. 2, the step S102 includes:
Step S201, calculating candidate merchant linear distances between the first candidate merchants according to the candidate position points of the first candidate merchants.
Step S202, forming a candidate merchant set by the first candidate merchant with the linear distance smaller than a preset fourth threshold.
Step S203, selecting a central location point from the location areas corresponding to the candidate merchant set, and using the central location point as a candidate location point of the candidate merchant set.
In this embodiment, the relative distance between the first candidate merchants, that is, the above-mentioned first candidate merchant straight line distance, is calculated by using the obtained candidate location points of the first candidate merchants. When the distance between partial first candidate merchants is very close, the receiving cost can be greatly reduced, so that the judgment can be carried out according to the obtained linear distance between the first candidate merchants, and the partial first candidate merchants are aggregated. Therefore, if the candidate merchant linear distance between some first candidate merchants is smaller than the preset fourth threshold value, selecting the first candidate merchants with the candidate merchant linear distance smaller than the preset fourth threshold value from the first candidate merchants to form a candidate merchant set. The candidate location points of the first candidate merchants forming the candidate merchant set are processed to obtain a central location point in the candidate merchant set, the central location point is used as the candidate location point representing the candidate merchant set to be compared and selected, and at the moment, the candidate merchant set can be regarded as a first candidate merchant to carry out subsequent comparison judgment. The fourth threshold value can be set correspondingly according to the self requirement of the courier, and can also be set according to the result obtained by big data processing.
It can be understood that, because the merchant sits in different positions, the candidate merchant set obtained by the method can be one or two or more than two; while the first candidate merchant number in the candidate merchant set may be two or more. Because the target position point is required to be determined according to the candidate position point and the browsing times in the follow-up process, the browsing times of the commodity interfaces corresponding to the first candidate merchants in the candidate merchant set can be subjected to weighted average processing so as to deal with the follow-up processing judgment; the number of browsing the commodity interface corresponding to the first candidate merchant in the candidate merchant set may be selected as the number of browsing the commodity interface of the set, which is not limited herein.
Optionally, in the method for determining the central position point in the candidate merchant set, a maximum ordinate, a minimum ordinate, a maximum abscissa and a minimum abscissa can be obtained from candidate position points of each first candidate merchant in the candidate merchant set, the absolute value of the difference between the ordinate and the absolute value of the difference between the abscissa can be obtained through calculation, and the diameter with the largest absolute value of the difference between the absolute values is selected as the circle, and the central point of the diameter is the central position point in the candidate merchant set.
And step S102, calculating the linear distance between the first candidate merchant and the robot according to the candidate position point and the current position point.
In this embodiment, the candidate location points obtained in the above manners and the current location point of the courier calculate the straight line distance between each first candidate merchant and the courier, and provide a data basis for determining the target location point subsequently. Wherein the candidate location points include candidate location points of each first candidate merchant, and if the candidate merchant set is included in the current environment, the candidate location points include candidate location points of each first candidate merchant other than each first candidate merchant in the candidate merchant set, and include candidate location points representing the candidate merchant set.
Optionally, obtaining the merchant type of the first candidate merchant, determining a type weight coefficient according to the merchant type, and giving the type weight coefficient to the browsing times.
In this embodiment, the time for producing the goods and providing the goods to the courier is different according to different merchant types, so that the time for the courier to complete the single task is also different, and sometimes the phenomenon that the delivery time of the courier is overtime due to too slow delivery of the merchant may occur, and the customer may also wait for a certain type of merchant too long, so that the selectivity of the merchant is not high, so that the merchant type of the merchant can be considered, and the accuracy of the data is improved. The method comprises the steps of obtaining a merchant type of a first candidate merchant, determining a type weight coefficient of the merchant according to the obtained merchant type of the first candidate merchant, giving the type weight coefficient to the browsing times of a commodity interface corresponding to the first candidate merchant, and enabling the browsing times after weighted processing to participate in subsequent judging processing. Wherein the merchant type is defined according to different business items of each candidate merchant; the determination of the above-mentioned type of weight coefficient includes, but is not limited to, delta-film, sequential analysis, statistical methods, and the like.
For example, but not limited to, if there are two first candidate merchants currently being a milky tea store and a fried chicken store, respectively, one of the first candidate merchants may be a beverage type and one of the first candidate merchants may be a snack type, and the weight coefficient of the beverage type may be higher than the weight coefficient of the snack type because the beverage is faster to make, more similar to the selection of a wider population, and then the obtained type weight coefficient may be weighted to the browsing times of the first candidate merchants corresponding to each of the first candidate merchants.
Optionally, obtaining an area attribute of an area where the first candidate merchant is located, determining an attribute weight coefficient according to the area attribute, and giving the attribute weight coefficient to the linear distance.
In this embodiment, because the area attribute of the area where the first candidate merchant is located is different, the time for the courier to reach the first candidate merchant is also different, so that the time for the courier to complete the single task is also different, and sometimes the phenomenon that the courier is overtime due to too complicated route to the merchant may occur, so that the area attribute of the area where the first candidate merchant is located can be taken into consideration, so as to improve the accuracy of the data. The attribute weight coefficient of the first candidate merchant is determined according to the obtained regional attribute of the region where the first candidate merchant is located, the attribute weight coefficient is given to the linear distance of the express delivery personnel corresponding to the first candidate merchant, and the weighted linear distance participates in subsequent judgment processing. The regional attributes are defined according to road section distribution conditions, merchant distribution conditions, traffic conditions and the like of different regions; the determination of the attribute weight coefficient includes, but is not limited to, a Delphi method, a sequential relationship analysis method, a statistical method and the like; the type weight coefficient and the attribute weight coefficient may be combined with each other to improve the data accuracy of the browsing times and the linear distance, or only one weight coefficient may be used to improve the data accuracy, which is not limited herein.
For example, but not limited to, if two first candidate merchants are currently distributed in the area a and the area B, the sidewalk connected between roads in the area a is more, and the road in the area B is more overpass or underground passage due to heavy traffic, the first candidate merchant who wants to go to the area B needs to pass through overpass or underground passage, the road section is more complicated, and the time for the courier is more, so the weight coefficient distributed in the area a is higher than the weight coefficient distributed in the area B, and then the obtained attribute weight coefficient is weighted to the straight line distance of the first candidate merchant corresponding to each other.
Optionally, acquiring the browsing time of the commodity interface of the first candidate merchant. And determining a time weight coefficient according to the time difference value between the browsing time and a preset time threshold value, and giving the time weight coefficient to the browsing times.
In this embodiment, considering the timeliness of takeaway, that is, the rest time of most people is fixed, and the customer groups are office workers and students, the working or learning time of the two customer groups is generally limited, and no work or learning-related matters are usually done in the working time or the learning time. In addition, since the shops order according to the time sequence of the customer orders, and considering the time taken by the merchant to prepare goods according to the orders, the time for ordering the corresponding shops is delayed relative to the ordinary time as the rest time of a plurality of people is more approximate to the time for ordering the goods, and the situation that the courier delays the time is likely to occur. And therefore, acquiring the browsing time of the commodity interface of the first candidate merchant, wherein the browsing time corresponds to the number of times of browsing the commodity page of the merchant. When the browsing time is closer to the preset time threshold, that is, the rest time of the customer, the choice of the customer is relatively small, and the customer may not browse for many times, and an order is generated directly according to the current commodity page, so that a time weight coefficient can be determined based on the browsing time and the time difference between the preset time thresholds, and the time weight coefficient is given to the browsing times of the commodity interfaces corresponding to the first candidate merchants, and the weighted straight line distance participates in the subsequent judgment processing, so as to improve the accuracy of data.
It will be appreciated that the above-mentioned time difference and time weight coefficient are inversely related, and the relative change relationship is also nonlinear, that is, the closer the difference is to zero, the larger the time weight coefficient is, and the closer it is to the meal point, the greater the probability of generating an order on the current commodity page will be.
Optionally, the preset time threshold is typically a rest time of a plurality of customer groups, for example, 7 to 9 points, 11 to 12 points, 18 to 20 points, and the like, and at least one time threshold is set according to the rest time of the plurality of customer groups.
And step S103, selecting a first candidate merchant meeting preset conditions as a target merchant according to the browsing times and the linear distance.
In this embodiment, according to the browsing times of browsing the commodity pages of each first candidate merchant and the linear distance of each first candidate merchant relative to the current position of the courier, selecting a first candidate merchant meeting a preset condition, and taking the selected first candidate merchant as a target merchant.
Optionally, as shown in fig. 3, step S103 includes:
step 301, when the difference between the browsing times of the commodity pages of the first candidate merchant is greater than a preset first threshold, selecting the first candidate merchant corresponding to the maximum commodity interface browsing times as the target merchant.
In this embodiment, when the difference between the browsing times of the commodity pages of the first candidate merchants is greater, the larger the browsing times of the commodity pages are, the higher the expectation that the corresponding first candidate merchant generates an order will be, so when the difference between the browsing times of the commodity pages of the first candidate merchant is greater than a preset first threshold, the first candidate merchant corresponding to the largest commodity interface browsing times is selected as the target merchant. The first threshold value can be set correspondingly according to the self requirement of the courier, and can also be set according to the result obtained by big data processing.
Optionally, because more couriers may compete for order taking in the vicinity of the hot store, when the number of browsing the commodity page of the first candidate merchant is detected to be greater than the preset maximum threshold, the first candidate merchant is deleted from the candidate merchant list, and the first candidate merchant is not considered.
Optionally, considering that if the linear distance between the first candidate merchant and the current location of the courier is too far, the freight cost may become high, or deviate from the familiar area of the courier, so that the courier searching time becomes long, and a timeout phenomenon may occur. And when the linear distance between the first candidate merchant position and the current position of the courier is detected to be greater than a preset distance threshold, deleting the first candidate merchant from the candidate merchant list, wherein the first candidate merchant is not taken as a consideration category.
Optionally, as shown in fig. 4, step S103 further includes:
and S401, when the difference value between the browsing times of the commodity pages of the first candidate merchants is smaller than a preset second threshold value, selecting the first candidate merchant corresponding to the minimum linear distance as a first target merchant.
In this embodiment, when the difference between the browsing times of the commodity pages of the first candidate merchant is smaller, it is indicated that the probabilities of the corresponding first candidate merchant generating orders are approximately the same, so when the difference between the browsing times of the commodity pages of the first candidate merchant is detected to be smaller than the preset second threshold, the first candidate merchant with the smallest straight line distance between the first candidate merchant and the courier is selected as the first target merchant from the first candidate merchants with the difference between the browsing times of the commodity pages being smaller than the preset second threshold. It will be appreciated that the first target merchant, i.e., the target merchant, may participate in subsequent path planning based on the location point of the first target merchant. The second threshold value can be set correspondingly according to the self requirement of the courier, and can also be set according to the result obtained by big data processing.
Optionally, after selecting the first candidate merchant corresponding to the minimum linear distance as the first target merchant, the method includes:
Step S402, calculating a merchant linear distance between a second candidate merchant and the first target merchant, and selecting a second candidate merchant corresponding to the merchant linear distance smaller than a preset third threshold as the second target merchant; the second candidate merchant is a candidate merchant other than the first target merchant.
In this embodiment, candidate merchants except the first target merchant in the first candidate merchants are located as second candidate merchants, merchant linear distances between the second candidate merchants and the first target merchant are calculated, and when merchant linear distances between some second candidate merchants and the first target merchant are calculated to be smaller than a preset third threshold value, second candidate merchants corresponding to the merchant linear distances smaller than the preset third threshold value are selected from the second candidate merchants as second target merchants. It can be appreciated that the second candidate merchant may be one, or two or more than two; the second target merchant obtained based on the third threshold may be one or two or more. The third threshold value can be set correspondingly according to the self requirement of the courier, and can also be set according to the result obtained by big data processing.
Step S403, determining the target location point according to the candidate location point of the first target merchant and the candidate location point of the second target merchant.
In this embodiment, the target location point that finally participates in the path planning may be determined according to the candidate location point of the first target merchant and the candidate location point of the second target merchant. Specifically, the target location point may be obtained by calculating a mean value of the candidate location points of the first target merchant and the candidate location points of the second target merchant; the target location point may be defined as a set formed by the candidate location point of the first target merchant and the candidate location point of the second target merchant, and the subsequent path planning may be performed according to the sequence of the location points in the set, for example, the candidate location point of the first target merchant is the first location in the set, and the candidate location point of the first target merchant participates in the subsequent path planning.
And step S104, path planning is carried out according to the current position point and the target position point of the target merchant.
In this embodiment, path planning is performed according to the current location point of the courier and the target location point of the target merchant. The target location point may be a location point of a first candidate merchant meeting the preset condition, or may be a target location point determined according to the first target merchant and the second target merchant, and the subsequent path planning may be performed by selecting the location point of the first candidate merchant meeting the preset condition as the target location point from the obtained location points of the first candidate merchant. The path planning may be a straight path between location points, or may be performed by means of an electronic map of a third party.
According to the method and the system for the goods transportation, the first candidate merchant which can generate orders at present and the browsing times corresponding to the first candidate merchant are obtained through obtaining the browsing times of the commodity pages of the first candidate merchant in a preset time period, the candidate position point of the first candidate merchant and the current position point of the robot are obtained, the linear distance between the first candidate merchant and the robot is calculated according to the obtained candidate position point and the current position point, the robot, namely an express person for goods transportation, can obtain the distance between the first candidate merchant and the current position of the robot through the calculated linear distance, so that the follow-up robot can obtain a target merchant suitable for the current robot to go to from the first candidate merchant according to the obtained browsing times and the linear distance, and then path planning is carried out according to the current position of the robot and the target position point of the target merchant, so that the robot goes to the target merchant which the customer will purchase and is relatively most suitable for the robot to go to in advance, and the efficiency of the whole goods transportation of the robot is improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
Corresponding to the above-mentioned path planning method based on order estimation, fig. 5 is a schematic structural diagram of a path planning device based on order estimation in an embodiment of the present application, and as shown in fig. 5, the path planning device based on order estimation may include:
the acquiring module 501 is configured to acquire a browsing frequency of a commodity page of a first candidate merchant, a candidate location point of the first candidate merchant, and a current location point of a robot in a preset time period.
A calculating module 502, configured to calculate a linear distance between the first candidate merchant and the robot according to the candidate location point and the current location point.
And a selecting module 503, configured to select, according to the browsing times and the linear distance, a first candidate merchant that meets a preset condition as a target merchant.
And the path planning module 504 is configured to perform path planning according to the current location point and the target location point of the target merchant.
Optionally, the path planning device based on order prediction may further include:
The first determining module is used for obtaining the merchant type of the first candidate merchant, determining a type weight coefficient according to the merchant type and giving the type weight coefficient to the browsing times; and/or obtaining the region attribute of the region where the first candidate merchant is located, determining an attribute weight coefficient according to the region attribute, and giving the attribute weight coefficient to the linear distance.
Optionally, the path planning device based on order prediction may further include:
And the acquisition time module is used for acquiring the browsing time of the commodity interface of the first candidate merchant.
And the second determining module is used for determining a time weight coefficient according to the time difference value between the browsing time and a preset time threshold value and giving the time weight coefficient to the browsing times.
Optionally, the selecting module 503 may include:
The first selecting unit is used for selecting the first candidate merchant corresponding to the maximum commodity interface browsing frequency as the target merchant when the difference value between the commodity page browsing frequencies of the first candidate merchant is larger than a preset first threshold value.
Optionally, the selecting module 503 may further include:
and the second selecting unit is used for selecting the first candidate merchant corresponding to the minimum linear distance as the first target merchant when the difference value between the browsing times of the commodity pages of the first candidate merchant is smaller than a preset second threshold value.
Optionally, the selecting module 503 may further include:
A third selecting unit, configured to calculate a merchant linear distance between a second candidate merchant and the first target merchant, and select a second candidate merchant corresponding to the merchant linear distance being smaller than a preset third threshold as the second target merchant; the second candidate merchant is a candidate merchant other than the first target merchant.
And the determining unit is used for determining the target position point according to the candidate position point of the first target merchant and the candidate position point of the second target merchant.
Optionally, the path planning device based on order prediction may further include:
And the calculating distance module is used for calculating the candidate merchant linear distance between the first candidate merchants according to the candidate position points of the first candidate merchants.
And the composition set module is used for composing the first candidate merchant with the linear distance of the candidate merchant smaller than a preset fourth threshold value into a candidate merchant set.
And the third determining module is used for selecting a central position point from the position areas corresponding to the candidate merchant sets and taking the central position point as the candidate position point of the candidate merchant sets.
According to the method and the system for the goods transportation, the first candidate merchant which can generate orders at present and the browsing times corresponding to the first candidate merchant are obtained through obtaining the browsing times of the commodity pages of the first candidate merchant in a preset time period, the candidate position point of the first candidate merchant and the current position point of the robot are obtained, the linear distance between the first candidate merchant and the robot is calculated according to the obtained candidate position point and the current position point, the robot, namely an express person for goods transportation, can obtain the distance between the first candidate merchant and the current position of the robot through the calculated linear distance, so that the follow-up robot can obtain a target merchant suitable for the current robot to go to from the first candidate merchant according to the obtained browsing times and the linear distance, and then path planning is carried out according to the current position of the robot and the target position point of the target merchant, so that the robot goes to the target merchant which the customer will purchase and is relatively most suitable for the robot to go to in advance, and the efficiency of the whole goods transportation of the robot is improved.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and modules described above may refer to corresponding procedures in the foregoing system embodiments and method embodiments, which are not described herein again.
Fig. 6 is a schematic structural diagram of a robot according to an embodiment of the present application. For convenience of explanation, only portions relevant to the embodiments of the present application are shown.
As shown in fig. 6, the robot 6 of this embodiment includes: at least one processor 600 (only one shown in fig. 6), a memory 601 connected to the processor 600, and a computer program 602 stored in the memory 601 and executable on the at least one processor 600, such as a path planning program based on order predictions. The processor 600 executes the computer program 602 to implement the steps of the above-described embodiments of the order-based estimated path planning method, such as steps S101 to S104 shown in fig. 1. Or the processor 600, when executing the computer program 602, performs the functions of the modules in the apparatus embodiments described above, for example, the functions of the modules 501 to 504 shown in fig. 5.
Illustratively, the computer program 602 may be partitioned into one or more modules that are stored in the memory 601 and executed by the processor 600 to perform the present application. The one or more modules may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 602 in the robot 6. For example, the computer program 602 may be divided into an acquisition module 501, a calculation module 502, a selection module 503, and a path planning module 504, where the specific functions of the modules are as follows:
The acquiring module 501 is configured to acquire a browsing frequency of a commodity page of a first candidate merchant, a candidate location point of the first candidate merchant, and a current location point of a robot in a preset time period;
A calculating module 502, configured to calculate a linear distance between the first candidate merchant and the robot according to the candidate location point and the current location point;
A selecting module 503, configured to select a first candidate merchant according to the browsing times and the linear distance, where the first candidate merchant meets a preset condition, as a target merchant;
And the path planning module 504 is configured to perform path planning according to the current location point and the target location point of the target merchant.
The robot 6 may include, but is not limited to, a processor 600, a memory 601. It will be appreciated by those skilled in the art that fig. 6 is merely an example of robot 6 and is not meant to be limiting of robot 6, and may include more or fewer components than shown, or may combine certain components, or may include different components, such as input and output devices, network access devices, buses, etc.
The Processor 600 may be a central processing unit (Central Processing Unit, CPU), the Processor 600 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 601 may in some embodiments be an internal storage unit of the robot 6, such as a hard disk or a memory of the robot 6. The memory 601 may also be an external storage device of the robot 6 in other embodiments, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the robot 6. Further, the memory 601 may also include both an internal memory unit and an external memory device of the robot 6. The memory 601 is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs, etc., such as program code of the computer program. The memory 601 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/robot and method may be implemented in other ways. For example, the apparatus/robot embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a camera device/robot, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (9)
1. A path planning method based on order prediction, comprising:
acquiring browsing times of commodity pages of a first candidate merchant, candidate position points of the first candidate merchant and current position points of a robot in a preset time period;
obtaining merchant types of the first candidate merchants, determining type weight coefficients according to the merchant types, and giving the type weight coefficients to the browsing times; and/or obtaining the region attribute of the region where the first candidate merchant is located, determining an attribute weight coefficient according to the region attribute, and giving the attribute weight coefficient to the linear distance; wherein the merchant type is a type representing the items operated by each candidate merchant; the regional attributes are road section distribution conditions, merchant distribution conditions and traffic conditions of different regions;
calculating a linear distance between the first candidate merchant and the robot according to the candidate position point and the current position point;
Selecting a first candidate merchant meeting preset conditions as a target merchant according to the browsing times and the linear distance;
and planning a path according to the current position point and the target position point of the target merchant.
2. The order-based pre-estimation path planning method according to claim 1, further comprising, before selecting a first candidate merchant meeting a preset condition as a target merchant according to the browsing times and the linear distance:
acquiring browsing time of a commodity interface of the first candidate merchant;
and determining a time weight coefficient according to the time difference value between the browsing time and a preset time threshold value, and giving the time weight coefficient to the browsing times.
3. The method for planning a path based on order estimation as set forth in claim 1, wherein selecting a first candidate merchant meeting a preset condition as a target merchant according to the browsing times and the linear distance comprises:
When the difference value between the browsing times of the commodity pages of the first candidate merchant is larger than a preset first threshold value, selecting the first candidate merchant corresponding to the maximum commodity interface browsing times as a target merchant.
4. A method for planning a path based on order estimation as set forth in any one of claims 1 to 3, wherein selecting a first candidate merchant meeting a preset condition as a target merchant according to the browsing times and the linear distance, further comprises:
And when the difference value between the browsing times of the commodity pages of the first candidate merchant is smaller than a preset second threshold value, selecting the first candidate merchant corresponding to the minimum linear distance as a first target merchant.
5. The order-based pre-estimation path planning method according to claim 4, wherein after selecting a first candidate merchant corresponding to a minimum linear distance as the first target merchant, comprising:
calculating a merchant linear distance between a second candidate merchant and the first target merchant, and selecting a second candidate merchant corresponding to the merchant linear distance smaller than a preset third threshold as the second target merchant; the second candidate merchant is a candidate merchant outside the first target merchant;
And determining the target position point according to the candidate position point of the first target merchant and the candidate position point of the second target merchant.
6. The order-based pre-estimated path planning method of claim 1 or 5, comprising, prior to calculating a linear distance between the first candidate merchant and the robot from the candidate location point and the current location point:
calculating candidate merchant linear distances between the first candidate merchants according to the candidate position points of the first candidate merchants;
forming a candidate merchant set by a first candidate merchant with the linear distance of the candidate merchant smaller than a preset fourth threshold value;
And selecting a central position point from the position areas corresponding to the candidate merchant sets, and taking the central position point as the candidate position point of the candidate merchant sets.
7. A path planning apparatus based on order prediction, comprising:
The acquisition module is used for acquiring the browsing times of the commodity page of the first candidate merchant, the candidate position point of the first candidate merchant and the current position point of the robot in a preset time period;
The first determining module is used for obtaining the merchant type of the first candidate merchant, determining a type weight coefficient according to the merchant type and giving the type weight coefficient to the browsing times; and/or obtaining the region attribute of the region where the first candidate merchant is located, determining an attribute weight coefficient according to the region attribute, and giving the attribute weight coefficient to the linear distance; wherein the merchant type is a type representing the items operated by each candidate merchant; the regional attributes are road section distribution conditions, merchant distribution conditions and traffic conditions of different regions;
the calculation module is used for calculating the linear distance between the first candidate merchant and the robot according to the candidate position point and the current position point;
The selecting module is used for selecting a first candidate merchant meeting preset conditions as a target merchant according to the browsing times and the linear distance;
And the path planning module is used for carrying out path planning according to the current position point and the target position point of the target merchant.
8. A robot comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, realizes the steps of a path planning method based on order prediction as claimed in any one of claims 1 to 6.
9. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of a method of order based path planning according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010790878.5A CN112051843B (en) | 2020-08-07 | 2020-08-07 | Path planning method and device based on order prediction, robot and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010790878.5A CN112051843B (en) | 2020-08-07 | 2020-08-07 | Path planning method and device based on order prediction, robot and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112051843A CN112051843A (en) | 2020-12-08 |
| CN112051843B true CN112051843B (en) | 2024-06-18 |
Family
ID=73602781
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010790878.5A Active CN112051843B (en) | 2020-08-07 | 2020-08-07 | Path planning method and device based on order prediction, robot and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112051843B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115082151A (en) * | 2022-06-19 | 2022-09-20 | 詹华 | Path planning method for taking pet for walk |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107274209A (en) * | 2017-05-18 | 2017-10-20 | 北京京东尚科信息技术有限公司 | The method and apparatus for predicting advertising campaign sales data |
| CN109299883A (en) * | 2018-10-12 | 2019-02-01 | 北京顺丰同城科技有限公司 | A kind of empty driving dispatching method and device |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9946804B2 (en) * | 2013-08-19 | 2018-04-17 | Business Objects Software Ltd | Displaying historical data associated with data values within business intelligence reports |
| CN107748923A (en) * | 2016-08-29 | 2018-03-02 | 北京三快在线科技有限公司 | Order processing method, apparatus and server |
| CN106651046A (en) * | 2016-12-29 | 2017-05-10 | 天津靠谱点科技有限公司 | Courier order sending method and system for delivering commodities of multiple merchants |
| CN108320036A (en) * | 2017-01-18 | 2018-07-24 | 百度在线网络技术（北京）有限公司 | A kind of method, apparatus and equipment in online booking room |
| CN107392412B (en) * | 2017-06-05 | 2021-10-12 | 北京星选科技有限公司 | Order scheduling method and device |
| CN108648045A (en) * | 2018-04-22 | 2018-10-12 | 扬州工业职业技术学院 | A kind of e-commerce order management system |
| CN108694544B (en) * | 2018-05-15 | 2021-03-19 | 北京顺丰同城科技有限公司 | Order scheduling processing method and device |
| JP6854966B2 (en) * | 2018-05-21 | 2021-04-07 | 北京極智嘉科技有限公司 | Order processing methods and equipment, servers and storage media |
| CN110231044B (en) * | 2019-06-10 | 2020-09-04 | 北京三快在线科技有限公司 | Path planning method and device |
| CN110335077A (en) * | 2019-07-02 | 2019-10-15 | 上海三菱电梯有限公司 | Merchant recommendation method and device |
| CN110967019A (en) * | 2019-11-28 | 2020-04-07 | 深圳优地科技有限公司 | Method for planning local path of robot and robot |
-
2020
- 2020-08-07 CN CN202010790878.5A patent/CN112051843B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107274209A (en) * | 2017-05-18 | 2017-10-20 | 北京京东尚科信息技术有限公司 | The method and apparatus for predicting advertising campaign sales data |
| CN109299883A (en) * | 2018-10-12 | 2019-02-01 | 北京顺丰同城科技有限公司 | A kind of empty driving dispatching method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112051843A (en) | 2020-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20200402144A1 (en) | Graphical user interface for object discovery and mapping in open systems | |
| US20210049548A1 (en) | Multi-phase consolidation optimization tool | |
| US20230169448A1 (en) | Delivery prediction generation system | |
| US10366435B2 (en) | Vehicle data system for rules based determination and real-time distribution of enhanced vehicle data in an online networked environment | |
| CN108885762B (en) | Method and system for allocating price discovery mechanism in data market | |
| US10648826B2 (en) | Providing stop recommendations based on a travel path and transaction data | |
| US20220335359A1 (en) | System and method for comparing enterprise performance using industry consumer data in a network of distributed computer systems | |
| US11966933B2 (en) | System and method for correlating and enhancing data obtained from distributed sources in a network of distributed computer systems | |
| CN112051843B (en) | Path planning method and device based on order prediction, robot and storage medium | |
| CN110009154B (en) | Refund prediction method and device, terminal equipment and storage medium | |
| AU2015218443B2 (en) | Itinerary estimation device and method, program, and information classification method | |
| CN112634062B (en) | Hadoop-based data processing method, device, equipment and storage medium | |
| CN110751501B (en) | Commodity shopping guide method, device, equipment and storage medium | |
| CN112328901A (en) | Service recommendation method based on cloud computing and block chain finance and cloud computing platform | |
| EP4092603A1 (en) | Inventory item prediction and listing recommendation | |
| CN113761398B (en) | Information recommendation method and device, electronic equipment and storage medium | |
| CN111680941A (en) | Premium recommendation method, device, equipment and storage medium | |
| CN115170244A (en) | Cold start recommendation method and device for new product, electronic equipment and medium | |
| CN109308331B (en) | Patent transaction recommendation method and device | |
| CN112990779A (en) | Method, device, equipment and storage medium for scoring candidate address | |
| CN109934604B (en) | Sales data processing method and system, storage medium and electronic equipment | |
| CN113159877A (en) | Data processing method, device, system and computer readable storage medium | |
| CN112070521A (en) | Dynamic metric based product assessment | |
| CN111833085A (en) | Method and device for calculating price of article | |
| CN111126118A (en) | Information processing method and apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |