CN115081983A - Order dispatching method, device, equipment and storage medium - Google Patents
Order dispatching method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application provides a method, a device, equipment and a storage medium for dispatching orders, wherein the method comprises the following steps: collecting service information of online distribution personnel; analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine service portraits of all online distributors; and acquiring a destination position to be delivered, and determining a delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person. The problem that customer satisfaction is low due to the fact that human resources cannot be reasonably utilized in the prior art can be solved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for dispatching a order.
Background
In the diversified data era, with the improvement of the service quality requirement and timeliness of people, various list dispatching systems are developed endlessly, such as list distribution systems for take-out, express delivery, designated driving, rescue, vehicle inspection and the like. Because different services need to be delivered by different delivery personnel, under the premise that the number of the delivery personnel is limited, the same delivery personnel can be selected by each delivery system at the same time to deliver different services, so that human resources cannot be reasonably utilized, and the customer satisfaction is low.
Disclosure of Invention
In view of this, embodiments of the present application provide a method, an apparatus, a device, and a storage medium for dispatching orders, so as to solve the problem in the prior art that customer satisfaction is low due to incapability of reasonably utilizing human resources.
A first aspect of an embodiment of the present application provides a method for dispatching orders, where the method includes:
collecting service information of online distribution personnel;
analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine service portraits of all online distributors;
and acquiring a destination position to be delivered, and determining a delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person.
A second aspect of the embodiments of the present application provides an order dispatching device, where the device includes:
the collection module is used for collecting service information of online distribution personnel;
the analysis module is used for analyzing the service information based on a pre-constructed dispatcher service portrait analysis model and determining the service portrait of each online dispatcher;
and the determining module is used for acquiring the end position to be delivered and determining the delivery personnel for delivering the order to be delivered according to the service drawing, the service information and the end position of each online delivery personnel.
A third aspect of the embodiments of the present application provides an order dispatching device, which includes a memory, a processor, and a computer program stored in the memory and operable on the on-line claim settlement device, where the processor, when executing the computer program, implements the steps of the order dispatching method provided in the first aspect.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the method for dispatching provided by the first aspect.
Compared with the prior art, the method for dispatching the orders has the advantages that the service information of online distribution personnel is collected; analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine service portraits of all online distributors; and acquiring a destination position to be delivered, and determining a delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person. The service information of the online distribution personnel is combined with the service portrait to determine the distribution personnel of the order to be distributed, so that the problem of low customer satisfaction caused by incapability of reasonably utilizing human resources in the prior art can be solved.
The advantageous effects provided by the second aspect to the fourth aspect of the embodiments of the present application are the same as those provided by the first aspect of the embodiments of the present application, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating an implementation of an order dispatching method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart illustrating an implementation of an assigning method according to another embodiment of the present application;
FIG. 3 is a flowchart illustrating an implementation of a method for matching orders according to another embodiment of the present application;
FIG. 4 is a block diagram of a configuration device provided in an embodiment of the present application;
fig. 5 is a block diagram of a structure of an order dispatching device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The order dispatching method can be executed by the order dispatching equipment. The order dispatching device includes but is not limited to a terminal device or a server. The server may be a single server or a cloud server cluster, and the terminal device may be a personal digital device, a notebook, a desktop computer, a smart wearable device, a robot, or the like. And is not particularly limited herein.
The order dispatching method is applied to various order dispatching service scenes and can collect service information of online distribution personnel; analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine service portraits of all online distributors; and acquiring a destination position to be delivered, and determining a delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person. The service information of the online distribution personnel is combined with the service portrait to determine the distribution personnel of the order to be distributed, so that the problem of low customer satisfaction caused by incapability of reasonably utilizing human resources in the prior art can be solved.
The following describes an exemplary assignment method provided in the embodiments of the present application with reference to the drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of an issuer method according to an embodiment of the present disclosure. The order dispatching method provided by the embodiment of the application can be implemented by a terminal or a server. As shown in fig. 1, the method for dispatching orders provided in the present embodiment includes steps S101 to S103. The details are as follows:
s101, collecting service information of online distribution personnel.
The service information of the online distribution personnel comprises geographical position information, service state information and preset distribution target position information of the online distribution personnel; specifically, the geographical location information of the online distribution personnel includes the geographical location of the online distribution personnel, and if the online distribution personnel are in a distribution state, the geographical location information of the online distribution personnel changes along with the movement of time. The service status information comprises a delivery status, a to-be-accepted order status and a delivery service type, and the service status of the online delivery personnel also changes with time. The preset delivery target position information is the corresponding target position to be delivered when the online delivery personnel are in the delivery state.
In specific implementation, the collecting service information of the online distribution personnel includes: and periodically collecting the geographical position information, the service state information and the preset distribution target position information of the on-line distribution personnel according to a preset time interval. Therefore, the situation that the collected service information of the online distribution personnel is inaccurate due to the fact that the geographic position information, the service state information and the preset distribution target position information of the online distribution personnel are changed along with the change of time is effectively prevented.
In addition, the online delivery personnel may also change with the difference of the collection time, for example, when the delivery personnel completes the delivery of the current order or does not receive the delivery order for a long time, the online delivery personnel may be offline. In this embodiment, the accuracy of collecting information can be effectively improved by periodically collecting the service information of the online distribution personnel according to the preset time interval. The preset time interval may be preset, for example, 5 seconds, 10 seconds, and the like, and is not limited in any way.
And S102, analyzing the service information based on a pre-constructed distributor service portrait analysis model, and determining the service portrait of each online distributor.
The service portrait of each online distribution personnel is determined based on the service information of the online distribution personnel in the preset time period through a pre-constructed distributor service portrait analysis model, and the service portrait of each online distribution personnel and the order information can be combined to determine the distribution personnel for distributing the order to be distributed.
The order information includes, but is not limited to, service information and a destination location. Specifically, the service information includes a delivery type.
In the concrete implementation, the distributor service image analysis model may be constructed in advance, or may be constructed after acquiring the service information of the online distributor.
For example, in an embodiment of the present application, before analyzing the service information based on a pre-constructed distributor service representation analysis model to obtain a service representation of each online distributor, the method includes: acquiring distribution order information of preset quantity of distribution personnel, and acquiring comprehensive service data of each distribution personnel in the preset quantity of distribution personnel based on the distribution order information; and training a preset neural network model based on the comprehensive service data to obtain the distributor service portrait analysis model.
In one embodiment, the composite service data includes at least one of an online order grabbing rate, an order taking rate, a delivery route, a service score, a service level, and a delivery type.
The method for analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine the service portrait of each online distributor comprises the following steps: and inputting the service information into a pre-constructed distributor service portrait analysis model for analysis to obtain the online order grabbing probability, order receiving rate, dispatching route, service score, service level and dispatching type of each online distributor.
S103, acquiring a destination position to be delivered, and determining a target delivery person for delivering the order to be delivered according to the service image, the service information and the destination position of each online delivery person.
The method for acquiring the end point position to be delivered and determining the target delivery personnel for delivering the order to be delivered according to the service portrait, the service information and the end point position of each online delivery personnel comprises the following steps:
determining online distribution personnel to be selected, wherein the distance between the online distribution personnel and the end point position is within a preset distance range according to the geographical position information of each online distribution personnel and the end point position;
and analyzing the service image of the online delivery personnel to be selected according to a preset scheduling rule, and determining target delivery personnel for delivering the order to be delivered.
Specifically, according to the geographical position information of each online distribution person at the current moment and the end position, calculating the position between the geographical position of each online distribution person at the current moment and the end position, determining the online distribution person of which the distance between the geographical position of each online distribution person at the current moment and the end position is within a preset distance range, and taking the determined online distribution person as the online distribution person to be selected; and further, analyzing the service portrait of each to-be-selected online distribution personnel according to a preset scheduling rule in the current distribution range. Specifically, the preset scheduling rule is to substitute the service pictures of all the to-be-selected online dispatching personnel into a preset scheduling algorithm for calculation, and determine target dispatching personnel for dispatching the to-be-selected orders.
The preset scheduling algorithm is online order grabbing probability multiplied by a + order receiving rate multiplied by b + dispatching route multiplied by c + service score multiplied by d + service level multiplied by e + distribution type multiplied by f. Specifically, a, b, c, d, e, and f are all preset cardinalities, and may be a number greater than 0.
After the analyzing the service image of the to-be-selected online delivery person according to the preset scheduling rule and determining a target delivery person for delivering the to-be-delivered order, the method includes:
counting the distribution success probability of the target distribution personnel within a preset time length, and determining whether distribution personnel need to be allocated or not based on the distribution success probability;
and if the distribution personnel need to be distributed, determining the personnel to be distributed according to the geographical position information of the target distribution personnel and the service pictures of other distribution personnel to be selected.
It should be understood that if the target delivery personnel frequently and recently have poor order taking effect, the target delivery personnel may have a large number of orders and need to re-determine the personnel to be deployed. To improve the timeliness of the order.
Through the analysis, the order dispatching method provided by the embodiment of the application collects the service information of the online distribution personnel; analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine service portraits of all online distributors; and acquiring a destination position to be delivered, and determining a delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person. The service information of the online distribution personnel is combined with the service portrait to determine the distribution personnel of the order to be distributed, so that the problem of low customer satisfaction caused by incapability of reasonably utilizing human resources in the prior art can be solved.
Referring to fig. 2, fig. 2 is a schematic view illustrating an implementation flow of an issuer method according to another embodiment of the present application. As shown in fig. 2, compared with the provisioning method shown in fig. 1, the specific implementation processes of S201 and S101 and S204 to S205 and S102 to S103 in the provisioning method provided in this embodiment are the same, except that S202 and S203 are further included before S204. Wherein, S202 and S201 are parallel execution relationship, and can be selected to be executed. The details are as follows:
s201, collecting service information of online distribution personnel.
S202, obtaining distribution order information of preset quantity of distribution personnel, and obtaining comprehensive service data of each distribution personnel in the preset quantity of distribution personnel based on the distribution order information.
And S203, training a preset neural network model based on the comprehensive service data to obtain the service portrait analysis model of the distributor.
And S204, analyzing the service information based on a pre-constructed distributor service portrait analysis model, and determining the service portrait of each online distributor.
S205, acquiring a destination position to be distributed, and determining a target distribution person for distributing the order to be distributed according to the service image, the service information and the destination position of each online distribution person.
Through the analysis, the order dispatching method provided by the embodiment of the application collects the service information of the online distribution personnel; analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine service portraits of all online distributors; and acquiring a destination position to be delivered, and determining a delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person. The method and the system have the advantages that the service information of the online delivery personnel is combined with the service portrait, the delivery personnel of the order to be delivered are determined, and the problem that in the prior art, the customer satisfaction degree is low due to the fact that human resources cannot be reasonably utilized can be solved.
Referring to fig. 3, fig. 3 is a flowchart illustrating an implementation of a method for provisioning orders according to another embodiment of the present application. As shown in fig. 3, in this embodiment, compared with the embodiment shown in fig. 2, the specific implementation processes of S301 to S305 are the same as those of S201 to S205, except that S306 and S307 are further included after S305. The details are as follows:
s301, collecting service information of online distribution personnel.
S302, obtaining the distribution order information of preset quantity of distribution personnel, and obtaining the comprehensive service data of each distribution personnel in the preset quantity of distribution personnel based on the distribution order information.
And S303, training a preset neural network model based on the comprehensive service data to obtain the distributor service portrait analysis model.
S304, analyzing the service information based on a pre-constructed distributor service portrait analysis model, and determining the service portrait of each online distributor.
S305, acquiring a destination position to be distributed, and determining a target distribution person for distributing the order to be distributed according to the service image, the service information and the destination position of each online distribution person.
S306, counting the distribution success probability of the target distribution personnel within a preset time length, and determining whether distribution personnel need to be allocated or not based on the distribution success probability.
S307, if distribution personnel need to be allocated, determining the personnel to be allocated according to the geographical position information of the target distribution personnel and the service images of other personnel to be allocated.
Through the analysis, the order matching method provided by the embodiment of the application collects the service information of the online distribution personnel; analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine service portraits of all online distributors; and acquiring a destination position to be delivered, and determining a delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person. The service information of the online distribution personnel is combined with the service portrait to determine the distribution personnel of the order to be distributed, so that the problem of low customer satisfaction caused by incapability of reasonably utilizing human resources in the prior art can be solved.
As shown in fig. 4, fig. 4 is a block diagram of a configuration unit provided in the embodiment of the present application. The ordering apparatus in this embodiment includes modules for executing the steps in the above method embodiments. Please refer to fig. 1 to fig. 3 for the corresponding embodiments. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 4, the ordering apparatus 40 includes:
a collecting module 401, configured to collect service information of online distribution personnel;
an analysis module 402, configured to analyze the service information based on a pre-constructed distributor service portrait analysis model, and determine a service portrait of each online distributor;
the determining module 403 is configured to obtain a destination position to be delivered, and determine a delivery person for delivering an order to be delivered according to the service image, the service information, and the destination position of each online delivery person.
In one embodiment, the service information includes geographical location information, service status information, and preset delivery target location information;
the collection module 401 is specifically configured to:
and periodically collecting the geographical position information, the service state information and the preset distribution target position information of the on-line distribution personnel according to a preset time interval.
In an embodiment, the apparatus 40 further includes:
the system comprises a first obtaining module, a second obtaining module and a sending module, wherein the first obtaining module is used for obtaining distribution order information of preset quantity of distribution personnel and obtaining comprehensive service data of each distribution personnel in the preset quantity of distribution personnel based on the distribution order information;
and the second obtaining module is used for training a preset neural network model based on the comprehensive service data to obtain the distributor service portrait analysis model.
In an embodiment, the analysis module 402 is specifically configured to:
and inputting the service information into a pre-constructed distributor service portrait analysis model for analysis to obtain the online order grabbing probability, order receiving rate, dispatching route, service score, service level and dispatching type of each online distributor.
In an embodiment, the determining module 403 includes:
the first determining unit is used for determining online distribution personnel to be selected, wherein the distance between the online distribution personnel and the end point position is within a preset distance range according to the geographical position information of each online distribution personnel and the end point position;
and the second determining unit is used for analyzing the service image of the online delivery personnel to be selected according to a preset scheduling rule and determining target delivery personnel for delivering the order to be delivered.
In an embodiment, the apparatus 40 further includes:
the statistical module is used for counting the distribution success probability of the target distribution personnel within the preset time length and determining whether distribution personnel need to be allocated or not based on the distribution success probability;
and the third obtaining module is used for determining the staff to be allocated according to the geographical position information of the target distribution staff and the service pictures of other staff to be allocated if the distribution staff needs to be allocated.
In one embodiment, the composite service data includes at least one of an online order grabbing rate, an order taking rate, a delivery route, a service score, a service level, and a delivery type.
It should be understood that, in the structural block diagram of the order dispatching device 40 shown in fig. 4, each module is used to execute each step in the embodiment corresponding to fig. 1 to fig. 3, and each step in the embodiment corresponding to fig. 1 to fig. 3 has been explained in detail in the above embodiment, and specific reference is made to the relevant description in the embodiment corresponding to fig. 1 to fig. 3, which is not repeated herein.
Referring to fig. 5, fig. 5 is a block diagram of a dispatch device according to an embodiment of the present disclosure. As shown in fig. 5, the order dispatching apparatus 50 of this embodiment includes: a processor 510, a memory 520, and a computer program 530, such as a dispatcher, stored in the memory 520 and operable on the processor 510. The processor 510, when executing the computer program 530, implements the steps in the various embodiments of the policy method described above, such as the steps shown in fig. 1-3. Alternatively, when the processor 510 executes the computer program 530, the functions of the modules or units in the embodiment corresponding to fig. 4, for example, the functions of the modules 401 to 403 shown in fig. 4, are implemented, for which reference is specifically made to the relevant description in the embodiment corresponding to fig. 4, which is not repeated herein.
Illustratively, the computer program 530 may be divided into one or more units, which are stored in the memory 520 and executed by the processor 510 to accomplish the present application. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 530 in the ordering device 50. For example, the computer program 530 may be partitioned to include: the device comprises a collection module, an analysis module and a determination module; the specific functions of the modules are described in fig. 4.
The ordering device 50 may include, but is not limited to, a processor 510, a memory 520. Those skilled in the art will appreciate that fig. 5 is merely an example of the ordering device 50 and does not constitute a limitation of the ordering device 50 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the ordering device 50 may also include input-output devices, network access devices, buses, etc.
The Processor 510 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
collecting service information of online distribution personnel;
analyzing the service information based on a pre-constructed service portrait analysis model of the distributor to determine service portraits of all online distributors;
and acquiring a destination position to be delivered, and determining a target delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person.
In one embodiment, the service information includes geographical location information, service status information, and preset delivery target location information;
the collecting service information of the online distribution personnel comprises the following steps:
and periodically collecting the geographical position information, the service state information and the preset distribution target position information of the on-line distribution personnel according to a preset time interval.
In an embodiment, before the analyzing the service information based on a pre-constructed distributor service representation analysis model to obtain a service representation of each online distributor, the method includes:
acquiring distribution order information of preset quantity of distribution personnel, and acquiring comprehensive service data of each distribution personnel in the preset quantity of distribution personnel based on the distribution order information;
and training a preset neural network model based on the comprehensive service data to obtain the distributor service portrait analysis model.
In an embodiment, the analyzing the service information based on a pre-constructed distributor service image analysis model to determine a service image of each online distributor includes:
and inputting the service information into a pre-constructed distributor service portrait analysis model for analysis to obtain the online order grabbing probability, order receiving rate, dispatching route, service score, service level and dispatching type of each online distributor.
In an embodiment, the obtaining an end position to be delivered and determining a target delivery person for delivering an order to be delivered according to a service drawing, service information and the end position of each online delivery person includes:
determining online distribution personnel to be selected, wherein the distance between the online distribution personnel and the end point position is within a preset distance range according to the geographical position information of each online distribution personnel and the end point position;
and analyzing the service image of the online delivery personnel to be selected according to a preset scheduling rule, and determining target delivery personnel for delivering the order to be delivered.
In an embodiment, after the analyzing the service portraits of the to-be-selected online delivery persons according to the preset scheduling rule and determining the target delivery persons for delivering the to-be-delivered orders, the method includes:
counting the distribution success probability of the target distribution personnel within a preset time length, and determining whether distribution personnel need to be allocated or not based on the distribution success probability;
and if distribution personnel need to be allocated, determining the personnel to be allocated according to the geographical position information of the target distribution personnel and the service pictures of other distribution personnel to be selected.
In one embodiment, the composite service data includes at least one of an online order grabbing rate, an order taking rate, a delivery route, a service score, a service level, and a delivery type.
The memory 520 may be an internal storage unit of the ordering device 50, such as a hard disk or a memory of the ordering device 50. The memory 520 may also be an external storage device of the dispatching device 50, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the dispatching device 50. Further, the memory 520 may also include both an internal storage unit and an external storage device of the ordering device 50. The memory 520 is used for storing the computer programs and other programs and data required by the ordering device 50. The memory 520 may also be used to temporarily store data that has been output or is to be output.
In an embodiment of the present application, a computer-readable storage medium is further provided, where a computer program is stored in the computer-readable storage medium, where the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the order dispatching method provided in each of the above embodiments of the present application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A method of dispatching a party, the method comprising:
collecting service information of online distribution personnel;
analyzing the service information based on a pre-constructed distributor service portrait analysis model to determine service portraits of all online distributors;
and acquiring a destination position to be delivered, and determining a target delivery person for delivering the order to be delivered according to the service drawing, the service information and the destination position of each online delivery person.
2. The method of claim 1, wherein the service information comprises geographical location information, service status information, preset delivery target location information;
the collecting service information of the online distribution personnel comprises the following steps:
and periodically collecting the geographical position information, the service state information and the preset distribution target position information of the on-line distribution personnel according to a preset time interval.
3. The method of claim 1, wherein prior to said analyzing said service information based on a pre-constructed distributor service representation analysis model to obtain a service representation for each online distributor, comprising:
acquiring distribution order information of preset quantity of distribution personnel, and acquiring comprehensive service data of each distribution personnel in the preset quantity of distribution personnel based on the distribution order information;
and training a preset neural network model based on the comprehensive service data to obtain the distributor service portrait analysis model.
4. The method of claim 3, wherein said analyzing said service information based on a pre-built distributor service representation analysis model to determine a service representation for each online distributor comprises:
and inputting the service information into a pre-constructed distributor service portrait analysis model for analysis to obtain the online order grabbing probability, order receiving rate, dispatching route, service score, service level and dispatching type of each online distributor.
5. The method of claim 2, wherein obtaining the destination location for delivery and determining the target delivery personnel for delivering the order to be delivered based on the service representation, the service information, and the destination location for each online delivery personnel comprises:
determining online distribution personnel to be selected, wherein the distance between the online distribution personnel and the end point position is within a preset distance range according to the geographical position information of each online distribution personnel and the end point position;
and analyzing the service image of the online delivery personnel to be selected according to a preset scheduling rule, and determining target delivery personnel for delivering the order to be delivered.
6. The method as claimed in claim 5, wherein after analyzing the service portraits of the selected on-line delivery persons according to the preset scheduling rules and determining the target delivery persons for delivering the orders to be delivered, the method comprises:
counting the distribution success probability of the target distribution personnel within a preset time length, and determining whether distribution personnel need to be allocated or not based on the distribution success probability;
and if the distribution personnel need to be distributed, determining the personnel to be distributed according to the geographical position information of the target distribution personnel and the service pictures of other distribution personnel to be selected.
7. The method of claim 3, wherein the composite service data comprises at least one of an online order grabbing rate, an order taking rate, a delivery route, a service score, a service level, and a delivery type.
8. An order dispatching device, characterized in that the device comprises:
the collection module is used for collecting the service information of the online distribution personnel;
the analysis module is used for analyzing the service information based on a pre-constructed dispatcher service portrait analysis model and determining the service portrait of each online dispatcher;
and the determining module is used for acquiring the end position to be delivered and determining the delivery personnel for delivering the order to be delivered according to the service drawing, the service information and the end position of each online delivery personnel.
9. A UI component access device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the order dispatching method as claimed in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method of dispatching as claimed in any one of claims 1 to 7.
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| CN202210742475.2A CN115081983A (en) | 2022-06-28 | 2022-06-28 | Order dispatching method, device, equipment and storage medium |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523426A (en) * | 2023-07-05 | 2023-08-01 | 成都花娃网络科技有限公司 | Intelligent distribution system and method based on order addresses |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523426A (en) * | 2023-07-05 | 2023-08-01 | 成都花娃网络科技有限公司 | Intelligent distribution system and method based on order addresses |
| CN116523426B (en) * | 2023-07-05 | 2023-09-08 | 成都花娃网络科技有限公司 | Intelligent distribution system and method based on order addresses |
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