CN110874666A - Method, system, equipment and storage medium for distributing articles - Google Patents
Method, system, equipment and storage medium for distributing articles Download PDFInfo
- Publication number
- CN110874666A CN110874666A CN201811012150.9A CN201811012150A CN110874666A CN 110874666 A CN110874666 A CN 110874666A CN 201811012150 A CN201811012150 A CN 201811012150A CN 110874666 A CN110874666 A CN 110874666A
- Authority
- CN
- China
- Prior art keywords
- delivery
- time
- probability
- user
- distribution
- 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.)
- Pending
Links
Images
Classifications
-
- 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"
-
- 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
- G06Q10/0838—Historical data
Abstract
The invention discloses a method, a system, equipment and a storage medium for delivering articles, wherein the method for delivering comprises the following steps: acquiring historical distribution time corresponding to a user; establishing a probability prediction model according to the historical distribution time, wherein the probability prediction model is used for predicting the probability that a user can receive articles in different time periods; and recommending different time periods corresponding to the probability exceeding the set threshold value to delivery personnel when delivering the articles. When the articles are delivered, different time periods corresponding to the probability exceeding the set threshold are recommended to delivery personnel, and the delivery time of the articles is determined according to the recommended different time periods to determine delivery, so that the delivery efficiency is improved, the delivery time of each article is shortened, and the workload of the delivery personnel is reduced.
Description
Technical Field
The invention relates to the technical field of logistics management, in particular to a method, a system, equipment and a storage medium for delivering articles.
Background
At present, in a delivery express link, for a large item to be delivered, a delivery person generally communicates with a user one day in advance (such as a mode of making a call or sending a short message), and the delivery is arranged after confirming a proper delivery time; for general-size articles, after arriving at a cell, a delivery person generally directly climbs stairs or goes upstairs by an elevator, and strikes a destination to deliver the articles, if the person does not answer after striking the door for many times (no person may exist at home), the delivery person calls the user to communicate the next appropriate delivery time, so that delivery failure is caused, and meanwhile, the procedures of going upstairs, going downstairs, making a phone call to confirm and the like of the delivery person waste a lot of delivery time, so that the existing express delivery has the problems of long delivery time of each article, low delivery efficiency, workload of the delivery person and the like.
Disclosure of Invention
The invention aims to solve the technical problems that in the prior art, delivery time of each article is long, delivery efficiency is low, and workload of delivery personnel is high in express delivery, and aims to provide a method, a system, equipment and a storage medium for delivering articles.
The invention solves the technical problems through the following technical scheme:
the invention provides a method for distributing articles, which comprises the following steps:
acquiring historical distribution time corresponding to a user;
wherein the historical delivery time comprises the time when the delivery of the goods is successful, the time when the delivery of the goods is failed and/or the time when the goods cannot be received fed back by a user;
establishing a probability prediction model according to the historical distribution time, wherein the probability prediction model is used for predicting the probability that a user can receive articles in different time periods;
and recommending different time periods corresponding to the probability exceeding the set threshold value to delivery personnel when delivering the articles.
Preferably, the step of recommending to the distribution staff the different time periods corresponding to the probability exceeding the set threshold value includes:
and determining the delivery time of the articles according to different time periods corresponding to the recommended probability exceeding the set threshold.
Preferably, the recommending, to the delivery person, a different time period corresponding to the probability of exceeding a set threshold value when the item is delivered includes:
sending a delivery notification to the user when the delivery person is ready to deliver the item at the delivery point;
judging whether feedback information sent by a user is received or not, if not, acquiring a predicted time period for a delivery person to reach a delivery destination corresponding to an article, and acquiring a first probability corresponding to the predicted time period;
judging whether the first probability is greater than the set threshold, and if the first probability is greater than the set threshold, recommending different time periods corresponding to the probability of exceeding the set threshold to the distribution staff comprises:
recommending the predicted time period and other different time periods corresponding to the probability exceeding the set threshold value to distribution personnel;
the step of determining the delivery time of the item according to the recommended different time periods corresponding to the probability exceeding the set threshold comprises:
determining the delivery time of the articles according to the recommended predicted time period and other different time periods corresponding to the probability exceeding the set threshold;
if the first probability is less than or equal to the set threshold, the recommending different time periods corresponding to the probability of exceeding the set threshold to the distribution personnel includes:
recommending different time periods corresponding to the probability exceeding a set threshold value to distribution personnel;
the step of determining the delivery time of the item according to the recommended different time periods corresponding to the probability exceeding the set threshold comprises:
and determining the delivery time of the articles according to different time periods corresponding to the probability that the recommendation exceeds the set threshold.
Preferably, the step of sending the delivery notification to the user further includes:
and judging whether feedback information which is sent by the user and cannot receive the articles is received, and if so, determining the delivery time of the articles with the user.
Preferably, the step of sending the delivery notification to the user further includes:
and judging whether feedback information which is sent by a user and can receive the articles is received, if so, determining the distribution time according to the time corresponding to the feedback information which is sent by the user and can receive the articles.
Preferably, the step of establishing a probabilistic predictive model based on the historical delivery times further comprises:
acquiring at least one of a delivery address of an article, position information of a user and a delivery distance spending time of a delivery person;
the step of establishing a probabilistic predictive model based on the historical delivery times further comprises:
and establishing the probability prediction model according to at least one of the delivery address of the item, the position information of the user and the delivery distance spending time of the delivery personnel and the historical delivery time.
The invention also provides a distribution system of the articles, which comprises a distribution time acquisition module, an establishment model module and a recommendation module;
the distribution time acquisition module is used for acquiring historical distribution time corresponding to the user;
wherein the historical delivery time comprises the time when the delivery of the goods is successful, the time when the delivery of the goods is failed and/or the time when the goods cannot be received fed back by a user;
the model building module is used for building a probability prediction model according to the historical distribution time, and the probability prediction model is used for predicting the probability that a user can receive articles in different time periods;
the recommending module is used for recommending different time periods corresponding to the probability exceeding a set threshold value to distribution personnel when distributing articles.
Preferably, the delivery system further comprises a delivery time determining module;
the distribution time determining module is used for determining the distribution time of the articles according to different time periods corresponding to the recommended probability exceeding the set threshold.
Preferably, the distribution system further comprises a distribution notification sending module, a first judging module, a first obtaining module and a second judging module;
the delivery notice sending module is used for sending a delivery notice to a user when a delivery person prepares to deliver the goods at a delivery point;
the first judging module is used for judging whether feedback information sent by a user is received or not, and if not, the first obtaining module is called;
the first obtaining module is used for obtaining a prediction time period of a delivery person reaching a delivery destination corresponding to an article and obtaining a first probability corresponding to the prediction time period;
the second judging module is used for judging whether the first probability is greater than the set threshold, and if the first probability is greater than the set threshold, the recommending module is called to recommend the predicted time period and other different time periods corresponding to the probabilities exceeding the set threshold to distribution personnel;
the delivery time determining module is further configured to determine a delivery time of the item according to the recommended predicted time period and other different time periods corresponding to the probabilities exceeding the set threshold;
when the second judging module judges that the first probability is smaller than or equal to the set threshold, calling the recommending module to recommend different time periods corresponding to the probability exceeding the set threshold to distribution personnel;
the distribution time determining module is further used for determining the distribution time of the articles according to different time periods and users corresponding to the probability that the recommendation exceeds the set threshold.
Preferably, the first determining module is further configured to determine whether feedback information that the user sends the feedback information that the article cannot be received is received, and if so, determine the delivery time of the article with the user.
Preferably, the first determining module is further configured to determine whether feedback information that is sent by a user and is capable of receiving the article is received, and if so, determine the delivery time according to a time corresponding to the feedback information that is sent by the user and is capable of receiving the article.
Preferably, the distribution system further comprises a second obtaining module;
the second acquisition module is used for acquiring at least one of a delivery address of the article, position information of a user and time spent on a delivery journey of a delivery person;
the model building module is further used for building the probability prediction model according to at least one of the distribution address of the article, the position information of the user and the distribution journey spending time of the distribution personnel and the historical distribution time.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the distribution method of the article.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of distribution of an article as described above.
The positive progress effects of the invention are as follows:
in the invention, based on the data such as the historical delivery success time of the user, the historical delivery failure time, the time which is fed back by the user and can not receive the articles and the like, a probability prediction model is established to obtain the probability that the user can receive the articles in different time periods; when the articles are delivered, different time periods corresponding to the probability exceeding the set threshold value are recommended to delivery personnel, and the delivery time of the articles is determined according to the recommended different time periods to determine delivery, so that the delivery efficiency is improved, the delivery time of each article is shortened, and the workload of the delivery personnel is reduced.
Drawings
Fig. 1 is a flowchart of a method for dispensing an article according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of a method for dispensing an article according to embodiment 2 of the present invention.
Fig. 3 is a block diagram of a system for dispensing articles according to embodiment 3 of the present invention.
Fig. 4 is a block diagram of a system for dispensing articles according to embodiment 4 of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a method for delivering an article according to embodiment 5 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, the method for dispensing an article of the present embodiment includes:
s101, obtaining historical distribution time corresponding to a user;
the historical delivery time comprises the time when the delivery of the goods is successful, the time when the delivery of the goods is failed and/or the time when the goods cannot be received, which is fed back by a user;
s102, establishing a probability prediction model according to historical distribution time;
wherein the probability prediction model is used for predicting the probability that the user can receive the item in different time periods;
and S103, recommending different time periods corresponding to the probability of exceeding the set threshold value to the delivery personnel when delivering the articles.
The threshold value is generally set to 80%, but the threshold value is not limited to 80%, and may be other values, and may be adjusted adaptively according to actual conditions.
In the embodiment, a probability prediction model is established based on data such as the historical delivery success time of the user, the historical delivery failure time, and the time which is fed back by the user and cannot receive the articles, so that the probability that the user can receive the articles in different time periods is obtained; when the articles are delivered, different time periods corresponding to the probability exceeding the set threshold value are recommended to delivery personnel, and the delivery time of the articles is determined according to the recommended different time periods to determine delivery, so that the delivery efficiency is improved, the delivery time of each article is shortened, and the workload of the delivery personnel is reduced.
Example 2
As shown in fig. 2, the method for distributing articles in this embodiment is a further improvement of embodiment 1, specifically:
step S102 includes:
s1020, acquiring at least one of a delivery address of the article, position information of a user and time spent by a delivery distance of a delivery person;
the position information of the user is historical position information of the user and current real-time position information of the user, which are acquired by the distribution personnel when the distribution personnel distribute the articles in the past, and the position information can be acquired only by obtaining the consent or the authorization of the user in advance.
Step S102 includes:
s1021, establishing a probability prediction model according to at least one of the delivery address of the article, the position information of the user, the delivery distance spending time of the delivery personnel and historical delivery time.
Step S103 includes before:
s10301, when a delivery person prepares to deliver the goods at a delivery point, sending a delivery notice to a user; s10302, judging whether feedback information sent by a user is received or not, if not, acquiring a prediction time period when a delivery person reaches a delivery destination corresponding to the article, and acquiring a first probability corresponding to the prediction time period;
s10303, judging whether the first probability is larger than a set threshold, and if the first probability is larger than the set threshold, continuing to the step S1031; if the first probability is less than or equal to the set threshold, continuing with S1032;
s1031, recommending a prediction time period and other different time periods corresponding to the probability exceeding the set threshold value to the delivery personnel;
s1032, recommending different time periods corresponding to the probability exceeding the set threshold value to the distribution personnel;
wherein step S103 includes step S1031 and step S1032.
In addition, step S1021 further includes:
and establishing a probability prediction model according to data such as at least one of the delivery address of the article, the position information of the user and the time spent on the delivery route of the delivery personnel, historical delivery time, the mode of sending feedback information by the user (such as a mobile phone, a computer terminal or a telephone), the time of sending the feedback information by the user, the historical delivery route of the delivery personnel and the like.
Step S1031 is followed by:
s1041, determining the delivery time of the articles according to the recommended prediction time period and other different time periods corresponding to the probability exceeding the set threshold;
step S1032 further includes:
s1042, determining the delivery time of the articles according to different time periods corresponding to the probability that the recommendation exceeds the set threshold and the user.
Step S10301 is followed by:
s103011, judging whether feedback information which is sent by the user and cannot receive the goods is received, and if yes, determining the distribution time of the goods with the user.
S103012, determines whether or not the feedback information of the receivable item transmitted by the user is received, and if so, determines the delivery time based on the time corresponding to the feedback information of the receivable item transmitted by the user.
After the delivery time is determined, the delivery personnel deliver the items according to the determined delivery time.
In the embodiment, a probability prediction model is established based on data such as the historical delivery success time of the user, the historical delivery failure time, and the time which is fed back by the user and cannot receive the articles, so that the probability that the user can receive the articles in different time periods is obtained; when the articles are delivered, a delivery notice is sent to the user and whether feedback information sent by the user is received or not is judged, if not, different time periods corresponding to the probability exceeding the set threshold value are recommended to delivery personnel, and the delivery time of the articles is determined according to the recommended different time periods; if feedback information which is sent by the user and cannot receive the articles is received, determining the delivery with the delivery time of the articles determined by the user; if the feedback information which is sent by the user and can receive the articles is received, the distribution time is determined according to the time corresponding to the feedback information which is sent by the user and can receive the articles, so that the distribution efficiency is improved, the distribution time of each article is shortened, and the workload of distribution personnel is reduced.
Example 3
As shown in fig. 3, the distribution system of the article of the present embodiment includes a distribution time acquisition module 1, an establishment model module 2, and a recommendation module 3.
The distribution time acquisition module 1 is used for acquiring historical distribution time corresponding to a user;
the historical delivery time comprises the time when the delivery of the goods is successful, the time when the delivery of the goods is failed and/or the time when the goods cannot be received, which is fed back by a user;
the model establishing module 2 is used for establishing a probability prediction model according to historical distribution time, and the probability prediction model is used for predicting the probability that a user can receive articles in different time periods;
the recommending module 3 is used for recommending different time periods corresponding to the probability of exceeding the set threshold to the delivery personnel when delivering the articles.
The threshold value is generally set to 80%, but the threshold value is not limited to 80%, and may be other values, and may be adjusted adaptively according to actual conditions.
In the embodiment, a probability prediction model is established based on data such as the historical delivery success time of the user, the historical delivery failure time, and the time which is fed back by the user and cannot receive the articles, so that the probability that the user can receive the articles in different time periods is obtained; when the articles are delivered, different time periods corresponding to the probability exceeding the set threshold value are recommended to delivery personnel, and the delivery time of the articles is determined according to the recommended different time periods to determine delivery, so that the delivery efficiency is improved, the delivery time of each article is shortened, and the workload of the delivery personnel is reduced.
Example 4
As shown in fig. 4, the distribution system of the articles of this embodiment is a further improvement of embodiment 3, specifically:
the delivery system of the article of this embodiment further includes a delivery time determination module 4, a delivery notice sending module 5, a first judgment module 6, a first acquisition module 7, a second judgment module 8, and a second acquisition module 9.
The second obtaining module 9 is used for obtaining at least one of a delivery address of the article, position information of the user and time spent on a delivery route of a delivery person;
the position information of the user is historical position information of the user and current real-time position information of the user, which are acquired by the distribution personnel when the distribution personnel distribute the articles in the past, and the position information can be acquired only by obtaining the consent or the authorization of the user in advance.
The modeling module 2 is further configured to build a probabilistic predictive model based on at least one of a delivery address of the item, location information of the user, and a delivery distance spent by a delivery person, and a historical delivery time.
The delivery time determining module 4 is configured to determine the delivery time of the item according to different time periods corresponding to the recommended probability of exceeding the set threshold.
A delivery notice sending module 5 for sending a delivery notice to the user when the delivery person prepares to deliver the article at the delivery point;
the first judging module 6 is used for judging whether feedback information sent by a user is received or not, and if not, the first obtaining module 7 is called;
the first obtaining module 7 is used for obtaining a predicted time period for a delivery person to reach a delivery destination corresponding to an article, and obtaining a first probability corresponding to the predicted time period;
the second judging module 8 is configured to judge whether the first probability is greater than a set threshold, and if the first probability is greater than the set threshold, invoke the recommending module 3 to recommend the predicted time period to the delivery staff and other different time periods corresponding to the probabilities of exceeding the set threshold;
in addition, the model building module 2 is further configured to build a probability prediction model according to data such as at least one of a delivery address of an article, location information of a user, and a delivery distance spent time of a delivery person, historical delivery time, a manner in which the user sends feedback information (e.g., via a mobile phone, a computer, or a telephone), a time when the user sends feedback information, and a historical delivery route of the delivery person.
The delivery time determining module 4 is further configured to determine a delivery time of the item according to the recommended predicted time period and other different time periods corresponding to the probabilities exceeding the set threshold;
when the second judging module 8 judges that the first probability is smaller than or equal to the set threshold, calling a recommending module to recommend different time periods corresponding to the probability exceeding the set threshold to the distribution personnel;
the delivery time determining module 4 is further configured to determine the delivery time of the item according to different time periods corresponding to the probability that the recommendation exceeds the set threshold.
In addition, the first judging module 6 is further configured to judge whether feedback information that the user sends the feedback information that the article cannot be received is received, and if the feedback information is received, determine the delivery time of the article with the user.
The first judging module 6 is further configured to judge whether feedback information that is sent by the user and is capable of receiving the article is received, and if yes, determine the delivery time according to a time corresponding to the feedback information that is sent by the user and is capable of receiving the article.
After the delivery time is determined, the delivery personnel deliver the items according to the determined delivery time.
In the embodiment, a probability prediction model is established based on data such as the historical delivery success time of the user, the historical delivery failure time, and the time which is fed back by the user and cannot receive the articles, so that the probability that the user can receive the articles in different time periods is obtained; when the articles are delivered, a delivery notice is sent to the user and whether feedback information sent by the user is received or not is judged, if not, different time periods corresponding to the probability exceeding the set threshold value are recommended to delivery personnel, and the delivery time of the articles is determined according to the recommended different time periods; if feedback information which is sent by the user and cannot receive the articles is received, determining the delivery with the delivery time of the articles determined by the user; if the feedback information which is sent by the user and can receive the articles is received, the distribution time is determined according to the time corresponding to the feedback information which is sent by the user and can receive the articles, so that the distribution efficiency is improved, the distribution time of each article is shortened, and the workload of distribution personnel is reduced.
Example 5
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention. The electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the article distribution method in any embodiment of the embodiment 1 or 2. The electronic device 30 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM)321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
The processor 31 executes various functional applications and data processing, such as a method of distributing an article in any one of embodiments 1 or 2 of the present invention, by executing the computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via network adapter 36. As shown in FIG. 5, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 6
The present embodiment provides a computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the steps in the method of distribution of an article in any one of embodiments 1 or 2.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps of implementing the method of distribution of an article in any of embodiments 1 or 2, when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (14)
1. A method of dispensing an item, the method comprising:
acquiring historical distribution time corresponding to a user;
wherein the historical delivery time comprises the time when the delivery of the goods is successful, the time when the delivery of the goods is failed and/or the time when the goods cannot be received fed back by a user;
establishing a probability prediction model according to the historical distribution time, wherein the probability prediction model is used for predicting the probability that a user can receive articles in different time periods;
and recommending different time periods corresponding to the probability exceeding the set threshold value to delivery personnel when delivering the articles.
2. A method of dispensing items as recited in claim 1, wherein said step of recommending to the dispensing personnel different time periods corresponding to said probability of exceeding a set threshold comprises:
and determining the delivery time of the articles according to different time periods corresponding to the recommended probability exceeding the set threshold.
3. A method of dispensing items as recited in claim 2, wherein said step of recommending to the dispensing personnel different time periods corresponding to said probability of exceeding a set threshold comprises:
sending a delivery notification to the user when the delivery person is ready to deliver the item at the delivery point;
judging whether feedback information sent by a user is received or not, if not, acquiring a predicted time period for a delivery person to reach a delivery destination corresponding to an article, and acquiring a first probability corresponding to the predicted time period;
judging whether the first probability is greater than the set threshold, and if the first probability is greater than the set threshold, recommending different time periods corresponding to the probability of exceeding the set threshold to the distribution staff comprises:
recommending the predicted time period and other different time periods corresponding to the probability exceeding the set threshold value to distribution personnel;
the step of determining the delivery time of the item according to the recommended different time periods corresponding to the probability exceeding the set threshold comprises:
determining the delivery time of the articles according to the recommended predicted time period and other different time periods corresponding to the probability exceeding the set threshold;
if the first probability is less than or equal to the set threshold, the recommending different time periods corresponding to the probability of exceeding the set threshold to the distribution personnel includes:
recommending different time periods corresponding to the probability exceeding a set threshold value to distribution personnel;
the step of determining the delivery time of the item according to the recommended different time periods corresponding to the probability exceeding the set threshold comprises:
and determining the delivery time of the articles according to different time periods corresponding to the probability that the recommendation exceeds the set threshold.
4. A method of dispensing of items as recited in claim 3, wherein said step of sending a dispensing notification to a user is further followed by:
and judging whether feedback information which is sent by the user and cannot receive the articles is received, and if so, determining the delivery time of the articles with the user.
5. A method of dispensing of items as recited in claim 3, wherein said step of sending a dispensing notification to a user is further followed by:
and judging whether feedback information which is sent by a user and can receive the articles is received, if so, determining the distribution time according to the time corresponding to the feedback information which is sent by the user and can receive the articles.
6. A method of dispensing of items as recited in claim 1, wherein said step of building a probabilistic predictive model based on said historical dispensing times is preceded by the step of:
acquiring at least one of a delivery address of an article, position information of a user and a delivery distance spending time of a delivery person;
the step of establishing a probabilistic predictive model based on the historical delivery times further comprises:
and establishing the probability prediction model according to at least one of the delivery address of the item, the position information of the user and the delivery distance spending time of the delivery personnel and the historical delivery time.
7. The distribution system of the article is characterized by comprising a distribution time acquisition module, an establishment model module and a recommendation module;
the distribution time acquisition module is used for acquiring historical distribution time corresponding to the user;
wherein the historical delivery time comprises the time when the delivery of the goods is successful, the time when the delivery of the goods is failed and/or the time when the goods cannot be received fed back by a user;
the model building module is used for building a probability prediction model according to the historical distribution time, and the probability prediction model is used for predicting the probability that a user can receive articles in different time periods;
the recommending module is used for recommending different time periods corresponding to the probability exceeding a set threshold value to distribution personnel when distributing articles.
8. The delivery system of claim 7, wherein said delivery system further comprises a delivery time determination module;
the distribution time determining module is used for determining the distribution time of the articles according to different time periods corresponding to the recommended probability exceeding the set threshold.
9. The article distribution system according to claim 8, further comprising a distribution notification sending module, a first judging module, a first acquiring module, and a second judging module;
the delivery notice sending module is used for sending a delivery notice to a user when a delivery person prepares to deliver the goods at a delivery point;
the first judging module is used for judging whether feedback information sent by a user is received or not, and if not, the first obtaining module is called;
the first obtaining module is used for obtaining a prediction time period of a delivery person reaching a delivery destination corresponding to an article and obtaining a first probability corresponding to the prediction time period;
the second judging module is used for judging whether the first probability is greater than the set threshold, and if the first probability is greater than the set threshold, the recommending module is called to recommend the predicted time period and other different time periods corresponding to the probabilities exceeding the set threshold to distribution personnel;
the delivery time determining module is further configured to determine a delivery time of the item according to the recommended predicted time period and other different time periods corresponding to the probabilities exceeding the set threshold;
when the second judging module judges that the first probability is smaller than or equal to the set threshold, calling the recommending module to recommend different time periods corresponding to the probability exceeding the set threshold to distribution personnel;
the distribution time determining module is further used for determining the distribution time of the articles according to different time periods and users corresponding to the probability that the recommendation exceeds the set threshold.
10. The system for delivering articles as claimed in claim 9, wherein the first determining module is further configured to determine whether feedback information sent by the user that the articles cannot be received is received, and if so, determine the delivery time of the articles with the user.
11. The article distribution system of claim 9, wherein the first determining module is further configured to determine whether feedback information of the receivable articles sent by the user is received, and if so, determine the distribution time according to a time corresponding to the feedback information of the receivable articles sent by the user.
12. The system for dispensing items of claim 7, wherein the system further comprises a second acquisition module;
the second acquisition module is used for acquiring at least one of a delivery address of the article, position information of a user and time spent on a delivery journey of a delivery person;
the model building module is further used for building the probability prediction model according to at least one of the distribution address of the article, the position information of the user and the distribution journey spending time of the distribution personnel and the historical distribution time.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of dispensing of an article as claimed in any one of claims 1 to 6.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of distribution of an article according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811012150.9A CN110874666A (en) | 2018-08-31 | 2018-08-31 | Method, system, equipment and storage medium for distributing articles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811012150.9A CN110874666A (en) | 2018-08-31 | 2018-08-31 | Method, system, equipment and storage medium for distributing articles |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110874666A true CN110874666A (en) | 2020-03-10 |
Family
ID=69715514
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811012150.9A Pending CN110874666A (en) | 2018-08-31 | 2018-08-31 | Method, system, equipment and storage medium for distributing articles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110874666A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111898958A (en) * | 2020-08-18 | 2020-11-06 | 新石器慧义知行智驰(北京)科技有限公司 | Unmanned distribution vehicle, distribution method and medium |
| CN111950803A (en) * | 2020-08-24 | 2020-11-17 | 上海寻梦信息技术有限公司 | Logistics object delivery time prediction method and device, electronic equipment and storage medium |
| CN112801586A (en) * | 2021-01-29 | 2021-05-14 | 青岛海信智慧生活科技股份有限公司 | Logistics tail distribution method and device and computing equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2835773A1 (en) * | 2013-08-07 | 2015-02-11 | ZF Friedrichshafen AG | Delivery forecasting system |
| CN107766962A (en) * | 2016-08-17 | 2018-03-06 | 北京京东尚科信息技术有限公司 | A kind of method and apparatus for recommending the optimal dispatching period |
| CN108364085A (en) * | 2018-01-02 | 2018-08-03 | 拉扎斯网络科技（上海）有限公司 | A kind of take-away distribution time prediction technique and device |
-
2018
- 2018-08-31 CN CN201811012150.9A patent/CN110874666A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2835773A1 (en) * | 2013-08-07 | 2015-02-11 | ZF Friedrichshafen AG | Delivery forecasting system |
| CN107766962A (en) * | 2016-08-17 | 2018-03-06 | 北京京东尚科信息技术有限公司 | A kind of method and apparatus for recommending the optimal dispatching period |
| CN108364085A (en) * | 2018-01-02 | 2018-08-03 | 拉扎斯网络科技（上海）有限公司 | A kind of take-away distribution time prediction technique and device |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111898958A (en) * | 2020-08-18 | 2020-11-06 | 新石器慧义知行智驰(北京)科技有限公司 | Unmanned distribution vehicle, distribution method and medium |
| CN111950803A (en) * | 2020-08-24 | 2020-11-17 | 上海寻梦信息技术有限公司 | Logistics object delivery time prediction method and device, electronic equipment and storage medium |
| CN112801586A (en) * | 2021-01-29 | 2021-05-14 | 青岛海信智慧生活科技股份有限公司 | Logistics tail distribution method and device and computing equipment |
| CN112801586B (en) * | 2021-01-29 | 2022-12-27 | 青岛海信智慧生活科技股份有限公司 | Logistics tail distribution method and device and computing equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112165691B (en) | Content delivery network scheduling method, device, server and medium | |
| CN109152061B (en) | Channel allocation method, device, server and storage medium | |
| EP2864899B1 (en) | Systems and methods for managing message delivery based on message priority | |
| CN110874666A (en) | Method, system, equipment and storage medium for distributing articles | |
| CN113162848B (en) | Method, device, gateway and medium for realizing block chain gateway | |
| CN104598241A (en) | Window monitoring method and system | |
| CN110751458B (en) | Business approval method, device and system | |
| CN102422275A (en) | Method, computer program product, and apparatus for enabling task aggregation in an enterprise environment | |
| CN110839084A (en) | Session management method, device, equipment and medium | |
| CN102035684A (en) | Failure notification method and system | |
| KR101416280B1 (en) | Event handling system and method | |
| CN104657841A (en) | Express item delivery method, delivery processing method, express cabinet terminal and service system | |
| CN112860342A (en) | Method, device, equipment, system and storage medium for microservice configuration | |
| CN110753002B (en) | Traffic scheduling method and device | |
| CN112860504A (en) | Monitoring method and device, computer storage medium and electronic equipment | |
| US8719371B1 (en) | Systems and methods for managing message delivery based on device activity, user behavior, or usage patterns | |
| CN113704058A (en) | Business model monitoring method and device and electronic equipment | |
| CN104756463B (en) | For being delivered to the system and method for supporting the mobile device of multi-user | |
| CN112910987A (en) | Message pushing method, system, device, equipment and storage medium | |
| CN113542424B (en) | Data processing method, device, equipment and readable storage medium | |
| US10291490B1 (en) | System and method for collecting data from low-availability devices | |
| CN114612212A (en) | Business processing method, device and system based on risk control | |
| CN113656239A (en) | Monitoring method and device for middleware and computer program product | |
| CN112925623A (en) | Task processing method and device, electronic equipment and medium | |
| CN111669445A (en) | Information subscription method and device of arbitration system |
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 |