CN112381293A - Intelligent express delivery distribution system and method based on big data - Google Patents

Intelligent express delivery distribution system and method based on big data Download PDF

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CN112381293A
CN112381293A CN202011269867.9A CN202011269867A CN112381293A CN 112381293 A CN112381293 A CN 112381293A CN 202011269867 A CN202011269867 A CN 202011269867A CN 112381293 A CN112381293 A CN 112381293A
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express
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delivery
home
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姚少弟
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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
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0835—Relationships between shipper or supplier and carriers
    • G06Q10/08355—Routing methods

Abstract

The invention discloses an intelligent express delivery distribution system and method based on big data, which comprises the following steps: a central processing unit, a database, a time recording module, a data scanning module, a data calling module, a data analysis module, a suggestion sending module, an information confirming module, a position extracting unit, a map importing unit, a modeling unit and a path planning unit, the daily delivery time of a delivery person and the delivery time of different recipients are analyzed through big data retrieval, whether the recipients are at home or not is predicted, the recommendation information is pushed to the delivery staff according to the prediction result, the delivery staff decides whether to confirm whether the addressee needs to deliver the goods to the home or not by telephone after knowing the approximate situation, the times of missing of the delivery staff and the addressee and calling of the delivery staff are reduced, when the delivery to home is confirmed, the map of the area where the receiver is located is imported through the map import unit, the two-dimensional model is established, the optimal path is planned for the deliverer according to the distance, and the delivery time of the deliverer is saved.

Description

Intelligent express delivery distribution system and method based on big data
Technical Field
The invention relates to the technical field of big data, in particular to an intelligent express delivery distribution system and method based on big data.
Background
With the development of economy, the quantity of online shopping of people is increased sharply, so that the quantity of express is increased, the express delivery is divided into regions, a delivery person in one region often staggers the time of a receiver when arriving at the delivery region, the receiver is informed by a telephone to confirm whether the express is to be delivered to home or placed in a temporary storage place when delivering the express every time, the time for delivering the express is wasted, the number of times of calling by the delivery person can be reduced if the receiver is at home when delivering the express can be predicted, the time for delivering the express can be saved, in addition, as one delivery person needs to deliver a plurality of express, after the receiver is confirmed to be at home and the corresponding receiver needs to be delivered to home, the optimal path is planned, the time for delivering the express can be reduced, and the processing progress of commodity transportation can be accelerated.
Therefore, an intelligent express delivery distribution system and method based on big data are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an intelligent express delivery distribution system and method based on big data, and aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent express delivery system based on big data, comprising: the system comprises a central processing unit, a database, a time recording module, a data scanning module, a data calling module, a data analysis module, a suggestion sending module, an information confirmation module, a position extraction unit, a map importing unit, a modeling unit and a path planning unit, wherein the output ends of the time recording module and the data scanning module are connected with the input end of the central processing unit, the output ends of the central processing unit and the map importing unit are connected with the input end of the database, the output ends of the database and the data scanning module are connected with the input end of the data calling module, the output end of the data calling module is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the suggestion sending module, the output end of the suggestion sending module is connected with the input end of the information confirmation module, the database, the output end of the modeling unit is connected with the input end of the position extraction unit, the output end of the position extraction unit is connected with the input end of the path planning unit, and the central processing unit is used for serially connecting all the module units through a bus, so that data transmission among all the modules is realized.
Furthermore, the time recording module, the data scanning module, the data retrieving module and the data analyzing module are used for predicting and analyzing whether a corresponding receiver is at home when a distributor arrives at a receiving area so as to confirm that the receiver does not need to send goods to the home or not in combination with the database, and the map importing unit, the position extracting unit, the modeling unit and the path planning unit are used for planning an optimal distribution path for the distributor in combination with the database after confirming the number of the express items needing to be sent to the home and predicting whether the receiver is at home when the distributor sends the items, so that the number of times of calling confirmation by the distributor is reduced, the consumed time of accessories is saved, and the cost is saved to a certain extent.
Further, the time of delivery of the deliverer and the time of taking goods by different recipients recorded in the time recording module are collected and transmitted to the database through the central processing unit, the express to be delivered is scanned through the data scanning module and the express information is sent to the database, the express information is matched with the information in the database to confirm the average time of taking goods by the recipient, after the information of the recipient is confirmed, the time record in the database is called through the data calling module, the average time of taking goods by the corresponding recipient is calculated through the data analyzing module, whether the recipient is at home or not is analyzed according to the calculation result, the suggestion sending module sends suggestion information to the deliverer, the deliverer determines whether the call is needed to confirm whether the recipient needs to send goods to home or not according to the suggestion information, and finally the number of the express which needs to send goods to home is confirmed through the information confirming module, the database is used for calling and analyzing the big data, the average pickup time of the receiver is calculated, whether the receiver is at home or not is predicted and analyzed, if the average pickup time of the receiver is too long, the fact that the receiver is not at home often can be estimated, and a distributor does not need to make a call for confirmation.
Further, under the condition that the consignee needs to be delivered to the home, an optimal delivery path is planned for the deliverer through the map importing unit, the modeling unit, the position extracting unit and the path planning unit: after a distributor telephone confirms that a receiver needs to deliver goods to home, the address information in the database is extracted by the position extraction unit, the map of the area where the receiver is located is imported into the database through the map import unit, the two-dimensional model of the area is established in the modeling unit, the position coordinates of the receiver address are marked, the distance between the distributor and a delivery place and the distance between the delivery places are calculated, and finally, the optimal delivery path is planned for the distributor through the path planning unit, so that the time consumed by delivery is reduced, and the efficiency of delivering express is improved.
An intelligent express delivery method based on big data is characterized in that: the method comprises the following steps:
s1: scanning and delivering the express through a data scanning module to confirm the information of a receiver;
s2: calling previous pickup time records corresponding to the recipients in a database;
s3: calculating the average goods taking time of the corresponding receiver and predicting whether the corresponding receiver is at home;
s4: sending the recommendation information to the distributor according to the prediction result;
s5: after receiving the suggestion information, the distributor decides whether to confirm whether the receiver needs to send goods to home or not by telephone;
s6: confirming the number of express items needing to be delivered to home, extracting position information of the express items in a database, importing a map of an area where a receiver is located into the database, and establishing a two-dimensional model;
s7: and planning an optimal delivery path for the deliverer.
Further, in steps S1-S2: the data scanning module scans delivered express and confirms the receiving information, and the time recording module records the time set P ═ P of different express delivered by the deliverer1，P2，...，PnThe time set for taking the mail by different addressees is Q ═ Q1，Q2，...，QnTransmitting the data in the time recording module to a database through a central processing unit, matching the data with the scanned addressee information to confirm the addressee, and calling a previous time recording set of the corresponding addressee in the database by a data calling module: qi＝{q1，q2，...，qnAnd f, correspondingly taking delivery time set of express deliveryIs Pi＝{p1，p2，...，pnRetrieving these data facilitates subsequent calculation of the average recipient time after recipient identification, helping to determine if the recipient is often at home at the fitting stage.
Further, in step S3: according to the formula
Figure BDA0002777327950000031
Calculating the average required pickup time t of a receiver, wherein qjIndicating the pickup time, p, of one of the parcelsjThe delivery time corresponding to the express is shown, and the time range required by the pickup is set to be (0, t)i) Or (t)jInfinity), if the average required picking time is 0<t<tiPredicting that a receiver is at home when a distributor delivers express; if the average required pickup time t is more than tjThe method comprises the steps of predicting that a receiver is not at home when a distributor delivers express, calculating the time difference between an accessory time point and an accessory taking time point to form the time required by the corresponding receiver to take the express, and then calculating the average time required by taking the express, so as to provide specific data support for predicting whether the receiver is at home in the accessory stage.
Further, in steps S4-S5: and sending the suggestion information to a distributor according to the prediction result, selecting whether the distributor confirms whether the addressee is at home or not by telephone according to the suggestion information, and selecting to send the express to the addressee home or temporarily store the express in an express cabinet after confirmation.
Further, in step S6: the number m of the express items needing to be delivered to home is counted in the information confirmation module, the position data of the corresponding express items in the database are extracted through the position extraction unit, the map of the area where the corresponding addressee of the express items needing to be delivered to home is located is led into the database through the map leading-in unit, and modeling is carried out in the modeling unit: establishing a two-dimensional model by taking the place of a distributor as an original point, and setting delivery position coordinates of express delivery needing to be delivered to home as { (x)1，y1)，(x2，y2)，...，(xm，ym) The paths of delivery are shared
Figure BDA0002777327950000032
And determining the number of the delivered paths so as to more accurately and practically plan the delivered optimal paths.
Further, in step S7: planning an optimal delivery path for a deliverer in a path planning unit: the distribution route has
Figure BDA0002777327950000033
Bar according to formula
Figure BDA0002777327950000034
Calculating the linear distance L, x between the location of the distributor and one express delivery position needing to deliver goods to the homei、yiThe horizontal and vertical coordinates of one express delivery position are shown, and the linear distances from the position of the distributor to the corresponding express delivery position are integrated into
Figure BDA0002777327950000035
According to the formula
Figure BDA0002777327950000041
Calculating the straight-line distance D between the express delivery positions, and calculating the sum of the distances of each path: l + D, setting the minimum sum of distances as (L + D)minAnd the path with the minimum distance sum is the optimal distribution path, the optimal distribution path is sent to the distributor, the distributor carries out express distribution according to the path, and according to the optimal path accessories, the efficiency of distributing the express is improved, and certain time and cost are saved.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention predicts whether the corresponding addressee is at home or not when the deliverer delivers the express through calling and analyzing the big data, and sends the predicted result and the suggestion information to the deliverer through the suggestion sending module so as to ensure that the deliverer can select whether to confirm the addressee by telephone or not: if the prediction result is that the receiver is at home, the information of 'predicting the receiver is at home and recommending the telephone confirmation of the distributor' is sent to the distributor through the recommending and sending module, and the distributor confirms whether the receiver is at home or not and whether the receiver needs to be delivered to home or not through the telephone after receiving the information; if the prediction result is that the addressee is not at home, the information of 'predicting that the addressee is not at home and suggesting that the distributor places the express at the temporary storage point' is sent to the distributor through the suggestion sending module, and the distributor places the express of the addressee at the temporary storage point after receiving the information, so that the number of times of calling by the distributor is reduced, and the time wasted for confirming the condition of the addressee is saved.
2. After the number of the express items needing to be delivered to home is confirmed through the information confirmation module, the position data corresponding to the express items in the database are extracted through the position extraction unit, the map of the area where the corresponding addressee of the express items needing to be delivered to home is led into the database through the map leading-in unit, modeling is carried out in the modeling unit, the distance from the location where the distributor is located to the express delivery point and the distance between the different express delivery points are calculated according to the data in the database, a path with the shortest sum of straight-line distances is obtained, the path is the optimal path and is sent to the distributor, the distributor carries out distribution according to path display, and the efficiency of distributing the express items is improved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a system diagram of a big data intelligent express delivery system and method of the present invention;
FIG. 2 is a method step diagram of a big data based intelligent courier delivery system and method of the present invention;
fig. 3 is an exemplary diagram of a distribution path of the intelligent express delivery distribution system and method based on big data according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1-3, the present invention provides the following technical solutions: an intelligent express delivery system based on big data, a central processing unit, a database, a time recording module, a data scanning module, a data retrieving module, a data analyzing module, a suggestion sending module, an information confirming module, a position extracting unit, a map importing unit, a modeling unit and a path planning unit, wherein the output ends of the time recording module and the data scanning module are connected with the input end of the central processing unit, the output ends of the central processing unit and the map importing unit are connected with the input end of the database, the output ends of the database and the data scanning module are connected with the input end of the data retrieving module, the output end of the data retrieving module is connected with the input end of the data analyzing module, the output end of the data analyzing module is connected with the input end of the suggestion sending module, the output end of the suggestion sending module is connected with the input end of the information confirming module, and the output ends of, the output end of the position extraction unit is connected with the input end of the path planning unit, and the central processing unit is used for serially connecting all the module units through the bus, so that data transmission among all the modules is convenient to realize.
The map importing unit, the position extracting unit, the modeling unit and the path planning unit are used for planning an optimal distribution path for a distributor in combination with the database after the number of the express items needing to be delivered to the home is confirmed, and predicting whether the receiver is at home when the distributor delivers the items can reduce the number of times of telephone call confirmation of the distributor and save the consumed time and cost of accessories.
The time of delivery of a distributor recorded in a time recording module and the time of taking goods by different recipients are collected and transmitted to a database through a central processing unit, the express to be delivered is scanned through a data scanning module and sent to the database, the average time of taking goods of the recipients is confirmed by matching with the information in the database, after the information of the recipients is confirmed, the time record in the database is called through a data calling module, the average time of taking goods of the corresponding recipients is calculated through a data analysis module, whether the recipients are at home is analyzed according to the calculation result, suggestion information is sent to the distributor through a suggestion sending module, the distributor determines whether the recipients need to be sent home or not through a telephone according to the suggestion information, finally, the number of the express which needs to be sent home is confirmed through an information confirming module, and the calling and analysis of big data are carried out through the database, and calculating the average pickup time of the receiver, predicting and analyzing whether the receiver is at home or not, and if the average pickup time of the receiver is too long, estimating that the receiver is not at home frequently and a distributor does not need to make a call for confirmation.
Under the condition that the consignee needs to be delivered to home, an optimal delivery path is planned for the deliverer through the map importing unit, the modeling unit, the position extracting unit and the path planning unit: after the telephone of the distributor confirms that the addressee needs to deliver goods to home, the address information in the database is extracted by using the position extraction unit, the map of the area where the addressee is located is imported into the database through the map import unit, the two-dimensional model of the area is established in the modeling unit, the coordinates of the addressee position are marked, the distance between the distributor and the delivery location and the distance between the delivery location are calculated, and finally the optimal distribution path is planned for the distributor through the path planning unit.
An intelligent express delivery method based on big data is characterized in that: the method comprises the following steps:
s1: scanning and delivering the express through a data scanning module to confirm the information of a receiver;
s2: calling previous pickup time records corresponding to the recipients in a database;
s3: calculating the average goods taking time of the corresponding receiver and predicting whether the corresponding receiver is at home;
s4: sending the recommendation information to the distributor according to the prediction result;
s5: after receiving the suggestion information, the distributor decides whether to confirm whether the receiver needs to send goods to home or not by telephone;
s6: confirming the number of express items needing to be delivered to home, extracting position information of the express items in a database, importing a map of an area where a receiver is located into the database, and establishing a two-dimensional model;
s7: and planning an optimal delivery path for the deliverer.
In steps S1-S2: the data scanning module scans delivered express and confirms the receiving information, and the time recording module records the time set P ═ P of different express delivered by the deliverer1，P2，...，PnThe time set for taking the mail by different addressees is Q ═ Q1，Q2，...，QnTransmitting the data in the time recording module to a database through a central processing unit, matching the data with the scanned addressee information to confirm the addressee, and calling a previous time recording set of the corresponding addressee in the database by a data calling module: qi＝{q1，q2，...，qnThe delivery time set of the corresponding express is Pi＝{p1，p2，...，pn}。
In step S3: according to the formula
Figure BDA0002777327950000061
Calculating the average required pickup time t of a receiver, wherein qjIndicating the pickup time, p, of one of the parcelsjThe delivery time corresponding to the express is shown, and the time range required by the pickup is set to be (0, t)i) Or (t)jInfinity), if the average required picking time is 0<t<tiPredicting that a receiver is at home when a distributor delivers express; if the average required pickup time t is more than tjAnd predicting that the receiver is not at home when the distributor delivers the express, calculating the time difference between the fitting time point and the pickup time point to form the time required by the corresponding receiver to pick up the express, and then calculating the average time required by pickup to provide specific data support for predicting whether the receiver is at home in the fitting stage.
In steps S4-S5: and sending the suggestion information to a distributor according to the prediction result, selecting whether the distributor confirms whether the addressee is at home or not by telephone according to the suggestion information, and selecting to send the express to the addressee home or temporarily store the express in an express cabinet after confirmation.
In step S6: the number m of the express items needing to be delivered to home is counted in the information confirmation module, the position data of the corresponding express items in the database are extracted through the position extraction unit, the map of the area where the corresponding addressee of the express items needing to be delivered to home is located is led into the database through the map leading-in unit, and modeling is carried out in the modeling unit: establishing a two-dimensional model by taking the place of a distributor as an original point, and setting delivery position coordinates of express delivery needing to be delivered to home as { (x)1，y1)，(x2，y2)，...，(xm，ym) The paths of delivery are shared
Figure BDA0002777327950000062
And determining the number of the delivered paths can more accurately and practically plan the optimal paths for delivering the express.
In step S7: planning an optimal delivery path for a deliverer in a path planning unit: the distribution route has
Figure BDA0002777327950000071
Bar according to formula
Figure BDA0002777327950000072
Calculating the linear distance L, x between the location of the distributor and one express delivery position needing to deliver goods to the homei、yiThe horizontal and vertical coordinates of one express delivery position are shown, and the linear distances from the position of the distributor to the corresponding express delivery position are integrated into
Figure BDA0002777327950000073
According to the formula
Figure BDA0002777327950000074
Calculating the straight-line distance D between the express delivery positions, and calculating the sum of the distances of each path: l + D, setting the minimum sum of distances as (L + D)minThe path with the minimum distance sum is the optimal distribution path, and the optimal distribution path is sent toAnd the deliverer carries out express delivery according to the path, and the efficiency of delivering the express can be improved according to the optimal path fitting, certain time and cost are saved, and the progress of commodity transportation is accelerated.
The first embodiment is as follows: after setting the time range required for picking up the express items as (0, 1) or (4, infinity) to scan the delivered express items and confirm the addressee information, the data calling module calls the delivery time set of the corresponding picked up express items in the database to be Pi1, { 09: 00, 09: 30, 10: 00, 10: 20, 14: 00, 14: 10 corresponding to the time record set of previous pickup by the receiver: qi1, { 09: 35, 10: 00, 11: 30, 12: 00, 14: 40, 15: 00} according to the formula
Figure BDA0002777327950000075
Calculating the average required pickup time t of the receiver as 57.5 (minutes)<And 1 (hour), predicting that the receiver is at home, sending information of 'predicting that the receiver is at home and suggesting a distributor to confirm by telephone' to the distributor, and calling by the distributor after receiving the information to confirm whether the receiver is at home or not and whether the express delivery needs to be delivered to home or not.
Example two: the information confirmation module counts the number m of the express items needing to be delivered to home to be 3, and the delivery route
Figure BDA0002777327950000076
(bar), establishing a two-dimensional model by taking the site A where the distributor is located as an original point, setting express delivery positions as a point B (-10 ), a point C (20, 30) and a point D (50, 50) respectively, and according to a formula
Figure BDA0002777327950000077
Calculating the linear distance from the location A of the distributor to the express delivery location: AB ≈ 14.14(m), AC ≈ 36.06(m), AD ≈ 70.71(m), according to the formula
Figure BDA0002777327950000078
Calculating the linear distance between the express delivery positions: BC ≈ 50(m), BD ≈ 84.85(m), CD ≈ 36.06(m), path 1: sum of linear distances of A → B → C → D is AB + BC + CD ≈ 100.2(m), route 2: the sum of the linear distances of A → B → D → C is AB + BD + DC ≈ 135.05(m), route 3: the sum of the linear distances of A → C → B → D is AC + CB + BD ≈ 170.91(m), path 4: the sum of the linear distances of A → C → D → B is AC + CD + DB ≈ 156.97(m), path 5: the sum of the linear distances of A → D → B → C is AD + DB + BC ≈ 205.56(m), path 6: sum of straight distances A → D → C → B is AD + DC + CB ≈ 156.77(m), minimum sum of distances (L + D)minAnd 100.2(m), route 1 is selected for distribution.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The utility model provides an intelligence express delivery system based on big data which characterized in that: the method comprises the following steps: the system comprises a central processing unit, a database, a time recording module, a data scanning module, a data calling module, a data analysis module, a suggestion sending module, an information confirmation module, a position extraction unit, a map importing unit, a modeling unit and a path planning unit, wherein the output ends of the time recording module and the data scanning module are connected with the input end of the central processing unit, the output ends of the central processing unit and the map importing unit are connected with the input end of the database, the output ends of the database and the data scanning module are connected with the input end of the data calling module, the output end of the data calling module is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the suggestion sending module, the output end of the suggestion sending module is connected with the input end of the information confirmation module, the database, the output end of the modeling unit is connected with the input end of the position extraction unit, and the output end of the position extraction unit is connected with the input end of the path planning unit.
2. The intelligent express delivery distribution system based on big data according to claim 1, wherein: the time recording module, the data scanning module, the data retrieving module and the data analyzing module are used for predicting and analyzing whether a corresponding receiver is at home when a distributor arrives at a receiving area so as to confirm that the receiver does not need to send goods to the home or not in combination with the database, and the map importing unit, the position extracting unit, the modeling unit and the path planning unit are used for planning an optimal distribution path for the distributor in combination with the database after confirming the number of the express delivery needing to be sent to the home.
3. The intelligent express delivery distribution system based on big data according to claim 1, wherein: the time of delivery of the delivery personnel and the time of picking of goods by different recipients recorded in the time recording module are transmitted to the database through the central processing unit, scanning the express to be delivered through the data scanning module and sending the express information to the database, matching the express information with the information in the database to confirm the average pick-up time of the receiver, after the information of the addressee is confirmed, the time record in the database is called through the data calling module, the average goods taking time of the corresponding receiver is calculated through the data analysis module, whether the receiver is at home or not is analyzed according to the calculation result, and sending suggestion information to a distributor through the suggestion sending module, determining whether a receiver needs to be sent home or not by a telephone according to the suggestion information by the distributor, and finally determining the number of express items needing to be sent home through the information determining module.
4. The intelligent express delivery distribution system based on big data according to claim 1, wherein: under the condition that the consignee needs to be delivered to home, an optimal delivery path is planned for the deliverer through the map importing unit, the modeling unit, the position extracting unit and the path planning unit: after the telephone of the distributor confirms that the addressee needs to deliver goods to home, the address information in the database is extracted by the position extraction unit, the map of the area where the addressee is located is imported into the database through the map import unit, a two-dimensional model of the area is established in the modeling unit, the coordinates of the addressee address are marked, the distance between the distributor and the delivery location and the distance between the delivery locations are calculated, and finally, the optimal distribution path is planned for the distributor through the path planning unit.
5. An intelligent express delivery method based on big data is characterized in that: the method comprises the following steps:
s1: scanning and delivering the express through a data scanning module to confirm the information of a receiver;
s2: calling previous pickup time records corresponding to the recipients in a database;
s3: calculating the average goods taking time of the corresponding receiver and predicting whether the corresponding receiver is at home;
s4: sending the recommendation information to the distributor according to the prediction result;
s5: after receiving the suggestion information, the distributor decides whether to confirm whether the receiver needs to send goods to home or not by telephone;
s6: confirming the number of express items needing to be delivered to home, extracting position information of the express items in a database, importing a map of an area where a receiver is located into the database, and establishing a two-dimensional model;
s7: and planning an optimal delivery path for the deliverer.
6. The intelligent express delivery method based on big data according to claim 5, wherein the method comprises the following steps: in steps S1-S2: the data scanning module scans delivered express and confirms the receiving information, and the time recording module records the time set P ═ P of different express delivered by the deliverer1，P2，...，PnThe time set for taking the mail by different addressees is Q ═ Q1，Q2，...，QnTransmitting the data in the time recording module to a database through a central processing unit, matching the data with the scanned addressee information to confirm the addressee, and then calling the dataAnd calling a time record set of the corresponding receiver in the database for taking the previous file by the block: qi＝{q1，q2，...，qnThe delivery time set of the corresponding express is Pi＝{p1，p2，...，pn}。
7. The intelligent express delivery method based on big data according to claim 6, wherein the method comprises the following steps: in step S3: according to the formula
Figure FDA0002777327940000021
Calculating the average required pickup time t of the corresponding addressees, wherein qjIndicating the pickup time, p, of one of the parcels corresponding to the recipientjThe delivery time corresponding to the express is shown, and the time range required by the pickup is set to be (0, t)i) Or (t)jInfinity), if the average required picking time is 0<t<tiPredicting that the receiver is at home when the deliverer delivers the express; if the average required pickup time t is more than tjAnd predicting that the receiver is not at home when the deliverer delivers the express delivery.
8. The intelligent express delivery method based on big data according to claim 5, wherein the method comprises the following steps: in steps S4-S5: and sending the suggestion information to a distributor according to the prediction result, selecting whether the distributor confirms whether the addressee is at home or not by telephone according to the suggestion information, and selecting to send the express to the addressee home or temporarily store the express in an express cabinet after confirmation.
9. The intelligent express delivery method based on big data according to claim 5, wherein the method comprises the following steps: in step S6: the number m of the express items needing to be delivered to home is counted in the information confirmation module, the position data of the corresponding express items in the database are extracted through the position extraction unit, the map of the area where the corresponding addressee of the express items needing to be delivered to home is located is led into the database through the map leading-in unit, and modeling is carried out in the modeling unit: establishing two dimensions with the location of the dispenser as the originThe model sets the delivery position coordinate of the express which needs to be delivered to home as { (x)1，y1)，(x2，y2)，...，(xm，ym) The paths of delivery are shared
Figure FDA0002777327940000031
And (3) strips.
10. The intelligent express delivery method based on big data according to claim 9, wherein: in step S7: planning an optimal delivery path for a deliverer in a path planning unit: the distribution route has
Figure FDA0002777327940000032
Bar according to formula
Figure FDA0002777327940000033
Calculating the linear distance L, x between the location of the distributor and one express delivery position needing to deliver goods to the homei、yiThe horizontal and vertical coordinates of one express delivery position are shown, and the linear distances from the position of the distributor to the corresponding express delivery position are integrated into
Figure FDA0002777327940000034
According to the formula
Figure FDA0002777327940000035
Calculating the straight-line distance D between the express delivery positions, and calculating the sum of the distances of each path: l + D, setting the minimum sum of distances as (L + D)minAnd the path with the minimum distance sum is the optimal distribution path, the optimal distribution path is sent to a distributor, and the distributor carries out express distribution according to the path.
CN202011269867.9A 2020-11-13 2020-11-13 Intelligent express delivery distribution system and method based on big data Pending CN112381293A (en)

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* Cited by examiner, † Cited by third party
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CN112884420A (en) * 2021-04-07 2021-06-01 谭媚 Intelligent logistics inquiry system and method
CN113283830A (en) * 2021-04-29 2021-08-20 北京京东振世信息技术有限公司 Waybill information sequence generation method, waybill information sequence generation device, waybill information sequence generation equipment and computer readable medium
CN114881580A (en) * 2022-07-11 2022-08-09 深圳市元美供应链管理有限公司 E-commerce logistics distribution and management system and method based on intelligent supply chain
CN115907586A (en) * 2022-12-10 2023-04-04 武汉洋洪电子商务有限公司 Internet of things-based E-commerce cargo intelligent distribution management method and system

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112884420A (en) * 2021-04-07 2021-06-01 谭媚 Intelligent logistics inquiry system and method
CN112884420B (en) * 2021-04-07 2022-05-17 贵州梵途科技(集团)有限公司 Intelligent logistics inquiry system and method
CN113283830A (en) * 2021-04-29 2021-08-20 北京京东振世信息技术有限公司 Waybill information sequence generation method, waybill information sequence generation device, waybill information sequence generation equipment and computer readable medium
CN113283830B (en) * 2021-04-29 2024-04-09 北京京东振世信息技术有限公司 Method, device, equipment and computer readable medium for generating waybill information sequence
CN114881580A (en) * 2022-07-11 2022-08-09 深圳市元美供应链管理有限公司 E-commerce logistics distribution and management system and method based on intelligent supply chain
CN115907586A (en) * 2022-12-10 2023-04-04 武汉洋洪电子商务有限公司 Internet of things-based E-commerce cargo intelligent distribution management method and system
CN115907586B (en) * 2022-12-10 2023-09-15 广东济群药业有限公司 E-commerce goods intelligent distribution management method and system based on Internet of things

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