CN110728432A

CN110728432A - Transport capacity scheduling method and device, electronic equipment and storage medium

Info

Publication number: CN110728432A
Application number: CN201910886162.2A
Authority: CN
Inventors: 卢学远; 石宽
Original assignee: Hangzhou Feibao Technology Co Ltd
Current assignee: Hangzhou Feibao Technology Co Ltd
Priority date: 2019-09-19
Filing date: 2019-09-19
Publication date: 2020-01-24

Abstract

The disclosure provides a capacity scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: the method comprises the steps of receiving order data sent by an object, determining an order area corresponding to the order data, calculating preference probability of each transport capacity to the order area according to a preset estimation judgment model, selecting the transport capacity with the highest preference probability, scheduling the selected transport capacity to the order area so as to be convenient for the selected transport capacity to take and/or dispatch orders, determining preference and cost information of each transport capacity to the order area by calculating the preference probability of each transport capacity to the order area, scheduling the transport capacity with the highest preference probability to the order area so as to select the transport capacity with the highest preference and relatively low cost from the transport capacities, and processing the orders by the transport capacities, so that flexibility and diversity of transport capacity scheduling are achieved, scheduling cost is saved, and the technical effects of meeting market demand and individualized demand of the transport capacity are achieved.

Description

Transport capacity scheduling method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of logistics transportation technologies, and in particular, to a capacity scheduling method and apparatus, an electronic device, and a storage medium.

Background

With the popularization of internet application, online order business is favored. However, as the amount of orders increases, the difficulty of distributing the orders increases, and how to schedule the capacity becomes a technical focus.

In the prior art, the dispatching of the transport capacity is mainly realized by two modes, one mode is an instant mode, and if an order is generated, the corresponding transport capacity is immediately dispatched; the other type is a preset time system, namely after the orders with the preset time are accumulated, the corresponding transport capacity is scheduled.

However, in the process of implementing the present disclosure, the inventors found that at least the following problems exist: the corresponding cost of scheduling is high.

Disclosure of Invention

The disclosure provides a capacity scheduling method and device, an electronic device and a storage medium, which are used for solving the problem of relatively high cost corresponding to scheduling in the prior art.

In one aspect, an embodiment of the present disclosure provides a capacity scheduling method, where the method includes:

receiving order data sent by an object;

determining an order area corresponding to the order data;

calculating preference probability of each transport capacity to the order area according to a preset pre-estimation judgment model;

selecting the transport capacity with the maximum preference probability;

and dispatching the picked capacity to the order area so as to collect and/or dispatch the order by the picked capacity.

In some embodiments, the step of determining the predictive judgment model comprises:

acquiring historical data of each self-receiving and/or dispatching order of each transport capacity within a preset time period;

dividing the area corresponding to each order into at least one order gathering area according to the information of each order;

generating training samples according to the historical data and the at least one order gathering area;

and training according to the training sample and the initial network model to obtain the estimation judgment model.

In some embodiments, the dividing the area corresponding to each order into at least one order aggregation area according to the information of each order includes:

and performing area division processing on the information of each order according to a clustering algorithm or a thermodynamic diagram method to generate at least one order aggregation area.

In some embodiments, the historical data includes at least location information and trajectory information.

On the other hand, the embodiment of the present disclosure further provides a capacity scheduling apparatus, the apparatus includes:

the receiving module is used for receiving order data sent by the object;

the determining module is used for determining an order area corresponding to the order data;

the calculation module is used for calculating preference probability of each transport capacity to the order area according to a preset pre-estimation judgment model;

the selecting module is used for selecting the transport capacity with the highest preference probability;

and the scheduling module is used for scheduling the selected transport capacity to the order area so as to collect and/or dispatch the order by the selected transport capacity.

In some embodiments, the apparatus further comprises:

the training module is used for acquiring historical data of each self-receiving and/or dispatching order of each capacity within a preset time period, dividing an area corresponding to each order into at least one order aggregation area according to information of each order, generating a training sample according to the historical data and the at least one order aggregation area, and training according to the training sample and an initial network model to obtain the estimation judgment model.

In some embodiments, the training module is configured to perform area division processing on the information of each order according to a clustering algorithm or a thermodynamic diagram method, so as to generate the at least one order aggregation area.

In another aspect, an embodiment of the present disclosure further provides an electronic device, including: a memory, a processor;

a memory for storing the processor-executable instructions;

wherein the processor, when executing the instructions in the memory, is configured to implement a method as in any of the embodiments above.

In another aspect, the disclosed embodiments also provide a computer-readable storage medium, in which computer-executable instructions are stored, and when executed by a processor, the computer-executable instructions are used to implement the method according to any one of the above embodiments.

The order data sent by the receiving object provided by the present disclosure, the order area corresponding to the order data is determined, calculating preference probability of each transport capacity to the order area according to a preset pre-estimation judgment model, selecting the transport capacity with the highest preference probability, scheduling the selected transport capacity to the order area so as to receive and/or dispatch the order by the selected transport capacity, by calculating preference probability of each capacity to the order area to determine information of preference and cost of each capacity to the order area, and scheduling the capacity with the highest preference probability to the order area, to select the transportation capacity with the highest preference and relatively low cost from the transportation capacities so as to process the order by the transportation capacity, therefore, the flexibility and diversity of the transport capacity scheduling are realized, the scheduling cost is saved, and the technical effects of meeting the marketization requirement and the individualized requirement of the transport capacity are achieved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.

Fig. 1 is a scene schematic diagram of a capacity scheduling method according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart of a capacity scheduling method according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a method for determining a predictive judgment model according to an embodiment of the present disclosure;

fig. 4 is a block diagram of a capacity scheduling apparatus according to an embodiment of the present disclosure;

fig. 5 is a block diagram of a capacity scheduling apparatus according to another embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure;

reference numerals: 10. the system comprises a server, 20, a user, 30, a user terminal, 1, a receiving module, 2, a determining module, 3, a calculating module, 4, a selecting module, 5, a scheduling module, 6 and a training module.

With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

The capacity scheduling method provided by the embodiment of the disclosure can be applied to the scene shown in fig. 1.

The main body executing the capacity scheduling method of the embodiment of the present disclosure may be a capacity scheduling device, and the capacity scheduling device may be a server (such as a cloud server or a local server) and a terminal device (such as a computer, a palmtop computer, and an iPad), and the like. The object can be a user, a user terminal and the like.

In the application scenario shown in fig. 1, the capacity scheduling device is a server 10, the object is a user 20, and the user 20 interacts with the server 10 through a user terminal 30.

In some embodiments, the user 20 sends the order data to the server 10 through the user terminal 30, and the server 10 executes the transportation capacity scheduling method of the embodiment of the present disclosure, and selects the transportation capacity to receive and/or dispatch the order.

In other embodiments, the user 20 performs an order placing operation on the interface of the user terminal 30, the user terminal 30 generates order data according to the order placing operation, and the user terminal 30 transmits the order data to the server 10, so that the server 10 executes the transportation capacity scheduling method of the embodiment of the present disclosure to select transportation capacity to receive and/or dispatch the order.

The following describes the technical solutions of the present disclosure and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present disclosure will be described below with reference to the accompanying drawings.

In one aspect, the embodiment of the present disclosure provides a capacity scheduling method suitable for the application scenario.

Referring to fig. 2, fig. 2 is a flowchart illustrating a capacity scheduling method according to an embodiment of the disclosure.

As shown in fig. 2, the method includes:

s101: and receiving order data sent by the object.

The order data is data generated when an object transmits and receives an order such as an article.

S102: and determining an order area corresponding to the order data.

In this step, when order data is received, an order area corresponding to the order data is determined.

Wherein, this step can include: and determining the position information according to the order data, and determining an order area corresponding to the position information.

Wherein, the order area can be set based on the requirement. For example, an area with a relatively large number of orders may be divided into a larger number of order areas and an area with a relatively small number of orders may be divided into a smaller number of order areas. That is, the number of order areas may be proportional to the number of orders, and the more the number of orders, the more the order areas may be.

In some embodiments, if the order data carries the location information, an order area corresponding to the order data may be determined based on the location information.

In other embodiments, an object sending order data may be located so as to obtain location information of the object, and an order area corresponding to the order data may be determined based on the location information.

In other embodiments, after receiving the order data, a location obtaining request may be sent to the object, location information fed back by the object based on the location obtaining request may be received, and an order area corresponding to the order data may be determined according to the location information.

In other embodiments, if the object is a user terminal, after receiving order data sent by the user terminal, acquiring an ID of the user terminal, and querying the location information of the user terminal according to the ID, so as to determine an order area based on the queried location information.

S103: and calculating preference probability of each transport capacity to the order area according to a preset pre-estimation judgment model.

The transportation capacity refers to manpower or vehicles which can transport orders, and can be divided into different types, such as full-time transportation capacity, part-time transportation capacity, crowdsourcing transportation capacity and the like.

The preference probability represents the preference degree of each capacity for order processing of the order area and represents the cost for order processing of each capacity for order area, generally speaking, the higher the preference probability is, the more the capacity is prone to processing the order of the order area, and the cost is relatively lower.

The pre-estimation judgment model can calculate preference probability of each capacity to the order area, and the pre-estimation judgment model comprises but is not limited to a CNN convolution neural network model, a BP neural network model and an LSTM network model.

S104: and selecting the transport capacity with the highest preference probability.

In some embodiments, if there are more than one preference probabilities, the capacity with one maximum preference probability can be selected.

S105: the picked capacity is dispatched to the order area for taking and/or dispatching of orders by the picked capacity.

In some embodiments, if the transportation capacity is human power, after selecting the transportation capacity with the highest preference probability, the server determines an identifier of a terminal corresponding to the transportation capacity and sends the order data to the terminal, or generates scheduling task information based on the order data and sends the scheduling task information to the terminal, so that the transportation capacity processes the order.

In some embodiments, if the capacity is a vehicle (e.g., an autonomous vehicle), after selecting the capacity with the highest preference probability, the server determines an identifier (e.g., a license plate of the autonomous vehicle) corresponding to the capacity, and sends the order data to the vehicle, or generates scheduling task information based on the order data and sends the scheduling task information to the vehicle, so that the capacity processes the order.

The embodiment of the disclosure provides a new capacity scheduling method, which comprises the following steps: the method comprises the steps of receiving order data sent by an object, determining an order area corresponding to the order data, calculating preference probability of each transport capacity to the order area according to a preset estimation judgment model, selecting the transport capacity with the highest preference probability, scheduling the selected transport capacity to the order area so as to be convenient for the selected transport capacity to take and/or dispatch orders, determining preference and cost information of each transport capacity to the order area by calculating the preference probability of each transport capacity to the order area, scheduling the transport capacity with the highest preference probability to the order area so as to select the transport capacity with the highest preference and relatively low cost from the transport capacities, and processing the orders by the transport capacities, so that flexibility and diversity of transport capacity scheduling are achieved, scheduling cost is saved, and the technical effects of meeting market demand and individualized demand of the transport capacity are achieved.

Referring to fig. 3, fig. 3 is a schematic diagram illustrating a method for determining an estimation judgment model according to an embodiment of the disclosure.

As shown in fig. 3, the method for determining the predictive judgment model includes:

s31: and acquiring historical data of each self-taking and/or dispatching order of each transport capacity in a preset time period.

The time period may be set based on the demand, such as half a year or a year.

For example: historical data of active collection and/or dispatching of orders by each capacity within a half year is obtained.

Since the historical data is generated for each capacity active processing order, based on the historical region, preference information of each capacity processing order, such as a preference of a geographic location, a preference of a time, and the like, may be determined.

It will be appreciated that, in general, the capacity self-selection largely represents the cost of processing an order, and that if a capacity is located further from an order area, the cost of processing the order is relatively high, and the capacity will generally forego the opportunity to process the order, and will select an order for which the cost due to distance is relatively low (i.e., relatively close).

For example: if a capacity goes to a cell and receives an order, the historical data may include current location information of the capacity, location information of the cell, and trajectory information between locations of the cell from the current location.

S32: and dividing the area corresponding to each order into at least one order aggregation area according to the information of each order.

The order gathering areas can be divided based on the density of the orders, if the distance between the building A and the building B is one kilometer, the building A and the building B are divided into one order gathering area due to the fact that the orders of the building A and the building B are small; if the order amount is large in both the building a and the building B, the building a is divided into one order aggregation area, and the building B is divided into one order aggregation area. The order quantity is more or less by setting a threshold value, comparing the order quantity with the threshold value, if the order quantity is greater than or equal to the threshold value, the order quantity is more, and if the order quantity is less than the threshold value, the order quantity is less.

In some embodiments, S32 includes: and performing area division processing on the information of each order according to a clustering algorithm or a thermodynamic diagram method to generate at least one order aggregation area.

S33: training samples are generated from the historical data and the at least one order aggregation zone.

S34: and training according to the training sample and the initial network model to obtain a pre-estimated judgment model.

It will be appreciated that the process of training may be an iterative process, such as:

the real value of the training sample can be determined based on the historical data, the test value of the training sample can be obtained through training, the loss value between the real value and the test value is calculated, and the parameter of the initial network model is adjusted based on the loss value until the loss value is smaller than a preset loss threshold value or the iteration number is equal to a preset number threshold value.

According to another aspect of the disclosed embodiment, the disclosed embodiment further provides a capacity scheduling device.

Referring to fig. 4, fig. 4 is a block diagram of a capacity scheduling device according to an embodiment of the present disclosure.

As shown in fig. 4, the apparatus includes:

the receiving module 1 is used for receiving order data sent by an object;

a determining module 2, configured to determine an order area corresponding to the order data;

the calculation module 3 is used for calculating preference probability of each transport capacity to the order area according to a preset pre-estimation judgment model;

the selection module 4 is used for selecting the transport capacity with the maximum preference probability;

and the scheduling module 5 is used for scheduling the selected transport capacity to the order area so as to collect and/or dispatch the order by the selected transport capacity.

As can be seen in conjunction with fig. 5, in some embodiments, the apparatus further comprises:

the training module 6 is configured to acquire historical data of each self-receiving and/or dispatching order of each capacity within a preset time period, divide an area corresponding to each order into at least one order aggregation area according to information of each order, generate a training sample according to the historical data and the at least one order aggregation area, and train according to the training sample and an initial network model to obtain the estimation judgment model.

In some embodiments, the training module 6 is configured to perform area division processing on the information of each order according to a clustering algorithm or a thermal mapping method, so as to generate the at least one order aggregation area.

According to another aspect of the embodiments of the present disclosure, there is also provided an electronic device, including: a memory, a processor;

a memory for storing processor-executable instructions;

wherein, when executing the instructions in the memory, the processor is configured to implement the method of any of the embodiments above.

Referring to fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.

As shown in fig. 6, the electronic device includes a memory and a processor, and the electronic device may further include a communication interface and a bus, wherein the processor, the communication interface, and the memory are connected by the bus; the processor is used to execute executable modules, such as computer programs, stored in the memory.

The Memory may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Via at least one communication interface, which may be wired or wireless), the communication connection between the network element of the system and at least one other network element may be implemented using the internet, a wide area network, a local network, a metropolitan area network, etc.

The bus may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.

The memory is used for storing a program, and the processor executes the program after receiving an execution instruction.

The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The steps of the method disclosed in connection with the embodiments of the present disclosure may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

According to another aspect of the embodiments of the present disclosure, there is also provided a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor, are configured to implement the method according to any one of the embodiments.

The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present disclosure.

In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present disclosure may be substantially or partially contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

It should also be understood that, in the embodiments of the present disclosure, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

While the present disclosure has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A capacity scheduling method, the method comprising:

receiving order data sent by an object;

determining an order area corresponding to the order data;

selecting the transport capacity with the maximum preference probability;

2. The method of claim 1, wherein the step of determining the predictive judgment model comprises:

3. The method according to claim 2, wherein the dividing the area corresponding to each order into at least one order aggregation area according to the information of each order comprises:

4. A method according to claim 2 or 3, characterized in that the history data comprises at least position information and trajectory information.

5. A capacity scheduling apparatus, comprising:

the receiving module is used for receiving order data sent by the object;

6. The apparatus of claim 5, further comprising:

7. The apparatus of claim 6, wherein the training module is configured to perform a region division process on the information of each order according to a clustering algorithm or a thermal mapping method to generate the at least one order aggregation region.

8. The apparatus of claim 6 or 7, wherein the historical data comprises at least location information and trajectory information.

9. An electronic device, comprising: a memory, a processor;

a memory for storing the processor-executable instructions;

wherein the processor, when executing the instructions in the memory, is configured to implement the method of any of claims 1-4.

10. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, are configured to implement the method of any one of claims 1 to 4.