1. The cost algorithm model construction method based on power grid logistics is characterized by comprising the following steps of:

acquiring different distribution service scene data in power grid logistics;

selecting first distribution business process data and first distribution process dynamic characteristic data from the distribution business scene data;

constructing an initial cost algorithm model based on pricing modes under different distribution scenes and different transportation modes;

training the initial cost algorithm model according to the first distribution business process data and the first distribution process dynamic characteristic data to generate a final cost algorithm model, wherein the final cost algorithm model is used for calculating the logistics cost.

2. The grid logistics based cost algorithm model building method of claim 1, further comprising:

acquiring a customer distribution demand;

service distribution and transportation are carried out on the customer distribution demands, and second distribution service flow data and second distribution process dynamic characteristic data are obtained;

and calculating the logistics cost of the second distribution business process data and the second distribution process dynamic characteristic data through the final cost algorithm model to obtain the logistics cost corresponding to the customer distribution demand.

3. The method for constructing the cost algorithm model based on the power grid logistics according to claim 1, wherein the obtaining of the data of different distribution service scenarios in the power grid logistics comprises:

acquiring different distribution service scenes in power grid logistics;

selecting a typical distribution scene from the distribution service scene through factor analysis, permutation and combination, difference analysis and induction summary, wherein the typical distribution scene comprises contract-leading distribution, submission and warehouse-returning distribution, cross-region distribution, emergency distribution, terminal distribution and island distribution;

setting a business process corresponding to the typical distribution scene;

the method comprises the steps of obtaining first distribution process dynamic characteristic data through a radio frequency, a Beidou positioning system, vehicle-mounted navigation equipment and an electronic fence, wherein the first distribution process dynamic characteristic data comprise weather, road conditions, vehicles, a GPS and vehicle-mounted positioning.

4. The grid logistics based cost algorithm model building method of claim 1, wherein the pricing model comprises: mileage interval pricing, mileage step pricing, station shift pricing, intermodal pricing, emergency pricing and island pricing.

5. The power grid logistics-based cost algorithm model building method of claim 1, wherein the initial cost algorithm model comprises a mileage interval algorithm, a mileage ladder algorithm, a vehicle carrying type algorithm, a shift algorithm, an emergency rescue algorithm, an optimal path algorithm, and a linkage algorithm.

6. The method for constructing a cost algorithm model based on grid logistics according to claim 2, wherein the service distribution and transportation of the customer distribution demand comprises:

carrying out transport capacity negotiation on carriers, and dispatching the customer delivery requirements to the carriers meeting the transport capacity;

and picking and delivering the items corresponding to the customer delivery demands from the warehouse, and delivering the items through the carrier.

7. Cost algorithm model construction device based on power grid logistics is characterized by comprising the following steps:

the scene data acquisition module is used for acquiring scene data of different distribution services in the power grid logistics;

the scene data selection module is used for selecting first distribution business process data and first distribution process dynamic characteristic data from the distribution business scene data;

the model construction module is used for constructing an initial cost algorithm model based on pricing modes in different distribution scenes and different transportation modes;

and the model generation module is used for training the initial cost algorithm model according to the first distribution business process data and the first distribution process dynamic characteristic data to generate a final cost algorithm model, and the final cost algorithm model is used for calculating the logistics cost.

8. The grid logistics based cost algorithm model building device of claim 7, further comprising:

acquiring a customer distribution demand;

service distribution and transportation are carried out on the customer distribution demands, and second distribution service flow data and second distribution process dynamic characteristic data are obtained;

and calculating the logistics cost of the second distribution business process data and the second distribution process dynamic characteristic data through the final cost algorithm model to obtain the logistics cost corresponding to the customer distribution demand.

9. An electronic device, wherein the electronic device comprises:

a processor and a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-6.

10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-6.

Background

With the growth of theories such as "cost center theory", "third profit source", "shared logistics", and the like, the logistics cost field gradually draws attention of the country and each enterprise, and how to calculate the optimal cost, thereby optimizing the current logistics resource allocation becomes a core problem in the logistics cost field. The traditional cost calculation technology mainly depends on manual work or an EXCLE spreadsheet, the calculation of the cost of single logistics is completed by setting a single and solidified formula, the algorithm is too single, stiff and insensitive, the problem of the occurrence of the actual transportation state cannot be reflected, and the logistics cost is easily high.

As large-scale energy and national support enterprises, safe, effective and agile logistics greatly guarantee the safe production of the enterprises, and with the extensive and extensive application of high and new technologies such as intelligent sensing, everything interconnection, big data, cloud computing and the like, an optimal logistics cost algorithm which is rapid, convenient, comprehensive and accurate is urgently needed, the defects of a traditional algorithm are overcome, the reasonable resource allocation target is achieved, the scale economy is fully exerted, the global cost is saved, and the high-speed development of the power grid logistics industry is promoted.

Disclosure of Invention

The invention provides a cost algorithm model construction method and device based on power grid logistics and electronic equipment, which are used for improving cost algorithm calculation efficiency, reducing inventory cost of each warehouse in a system and optimizing distribution carrier environment.

The embodiment of the specification provides a cost algorithm model construction method based on power grid logistics, which comprises the following steps:

acquiring different distribution service scene data in power grid logistics;

selecting first distribution business process data and first distribution process dynamic characteristic data from the distribution business scene data;

constructing an initial cost algorithm model based on pricing modes under different distribution scenes and different transportation modes;

training the initial cost algorithm model according to the first distribution business process data and the first distribution process dynamic characteristic data to generate a final cost algorithm model, wherein the final cost algorithm model is used for calculating the logistics cost.

Preferably, the method further comprises the following steps:

acquiring a customer distribution demand;

service distribution and transportation are carried out on the customer distribution demands, and second distribution service flow data and second distribution process dynamic characteristic data are obtained;

and calculating the logistics cost of the second distribution business process data and the second distribution process dynamic characteristic data through the final cost algorithm model to obtain the logistics cost corresponding to the customer distribution demand.

Preferably, the acquiring data of different distribution service scenes in the power grid logistics includes:

acquiring different distribution service scenes in power grid logistics;

selecting a typical distribution scene from the distribution service scene through factor analysis, permutation and combination, difference analysis and induction summary, wherein the typical distribution scene comprises contract-leading distribution, submission and warehouse-returning distribution, cross-region distribution, emergency distribution, terminal distribution and island distribution;

setting a business process corresponding to the typical distribution scene;

the method comprises the steps of obtaining first distribution process dynamic characteristic data through a radio frequency, a Beidou positioning system, vehicle-mounted navigation equipment and an electronic fence, wherein the first distribution process dynamic characteristic data comprise weather, road conditions, vehicles, a GPS and vehicle-mounted positioning.

Preferably, the pricing model comprises: mileage interval pricing, mileage step pricing, station shift pricing, intermodal pricing, emergency pricing and island pricing.

Preferably, the initial cost algorithm model comprises a mileage interval algorithm, a mileage ladder algorithm, a vehicle carrying type algorithm, a shift algorithm, an emergency rescue algorithm, an optimal path algorithm and a linkage algorithm.

Preferably, the service distribution and transportation of the customer delivery demand includes:

carrying out transport capacity negotiation on carriers, and dispatching the customer delivery requirements to the carriers meeting the transport capacity;

and picking and delivering the items corresponding to the customer delivery demands from the warehouse, and delivering the items through the carrier.

The embodiment of the present specification further provides a cost algorithm model building apparatus based on power grid logistics, including:

the scene data acquisition module is used for acquiring scene data of different distribution services in the power grid logistics;

the scene data selection module is used for selecting first distribution business process data and first distribution process dynamic characteristic data from the distribution business scene data;

the model construction module is used for constructing an initial cost algorithm model based on pricing modes in different distribution scenes and different transportation modes;

and the model generation module is used for training the initial cost algorithm model according to the first distribution business process data and the first distribution process dynamic characteristic data to generate a final cost algorithm model, and the final cost algorithm model is used for calculating the logistics cost.

Preferably, the method further comprises the following steps:

acquiring a customer distribution demand;

service distribution and transportation are carried out on the customer distribution demands, and second distribution service flow data and second distribution process dynamic characteristic data are obtained;

and calculating the logistics cost of the second distribution business process data and the second distribution process dynamic characteristic data through the final cost algorithm model to obtain the logistics cost corresponding to the customer distribution demand.

An electronic device, wherein the electronic device comprises:

a processor and a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of the above.

A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of the above.

The beneficial effects are that:

the invention improves the calculation efficiency of a cost algorithm, reduces the inventory cost of each warehouse in the system, scientifically plans the network, optimizes the delivery line, reasonably simplifies the vehicles, improves the single-vehicle efficiency and the economic efficiency, and optimizes the delivery carrier environment.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a schematic diagram illustrating a method for constructing a cost algorithm model based on grid logistics according to an embodiment of the present disclosure;

fig. 2 is a schematic structural diagram of a cost algorithm model construction device based on power grid logistics according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;

fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.

Detailed Description

Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.

Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.

In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.

The diagrams depicted in the figures are exemplary only, and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.

Referring to fig. 1, a schematic diagram of a cost algorithm model construction method based on power grid logistics according to an embodiment of the present disclosure includes:

s101: acquiring different distribution service scene data in power grid logistics;

in a preferred embodiment of the invention, different distribution service scenes in power grid logistics are obtained in a power grid logistics database, and typical distribution scenes are selected from the distribution service scenes through factor analysis, permutation and combination, difference analysis and induction summary, wherein the typical distribution scenes comprise contract procurement distribution, inspection and warehouse return distribution, cross-regional distribution, emergency distribution, terminal distribution, island distribution and the like; and then setting a service flow corresponding to the typical distribution scene, acquiring dynamic data such as weather, road conditions, vehicles, GPS, vehicle positioning and the like in transit by using technologies such as radio frequency, a Beidou positioning system, vehicle navigation equipment, electronic fences and the like, and finishing continuous accumulation and updating of big data in the whole logistics process so as to obtain final data of different distribution service scenes in power grid logistics.

S102: selecting first distribution business process data and first distribution process dynamic characteristic data from the distribution business scene data;

in a preferred embodiment of the present invention, the first distribution business process data and the first distribution process dynamic feature data are selected from the distribution business scene data and used as the feature data for model training.

S103: constructing an initial cost algorithm model based on pricing modes under different distribution scenes and different transportation modes;

in the preferred embodiment of the invention, different pricing modes are formulated according to different distribution scenes and different transportation modes, wherein the pricing modes comprise mileage interval pricing, mileage step pricing, station shift pricing, intermodal pricing, emergency pricing, sea island pricing and the like, and the initial cost algorithm model is constructed according to the different pricing modes.

S104: training the initial cost algorithm model according to the first distribution business process data and the first distribution process dynamic characteristic data to generate a final cost algorithm model, wherein the final cost algorithm model is used for calculating the logistics cost.

In a preferred embodiment of the present invention, the first distribution business process data and the first distribution process dynamic characteristic data are input to the initial cost algorithm model for model training, and the final cost algorithm model is obtained through multiple training. The final cost algorithm model is used for calculating logistics cost of corresponding distribution scenes under different requirements, and logistics cost under different distribution modes can be quickly calculated, so that a distribution mode which saves more cost is selected, and logistics benefits are improved. Based on a big data cloud platform, a typical distribution scene related business process and a function module are developed, a logistics cost algorithm model is realized, pricing mode intelligent matching under the condition of different typical scene multivariable is supported, and distribution cost is calculated and output efficiently.

Further, still include:

acquiring a customer distribution demand;

service distribution and transportation are carried out on the customer distribution demands, and second distribution service flow data and second distribution process dynamic characteristic data are obtained;

and calculating the logistics cost of the second distribution business process data and the second distribution process dynamic characteristic data through the final cost algorithm model to obtain the logistics cost corresponding to the customer distribution demand.

In a preferred embodiment of the invention, a demand unit provides multidimensional delivery demands for a logistics platform, and can relate to any one or more delivery demands of various typical delivery scenes such as reservation and acceptance, inspection and delivery, warehouse-returning delivery, acceptance and delivery, delivery in the last kilometer, island delivery, cross-region delivery, emergency delivery and the like to form an order pool, a delivery center manages the order pool, transport force consultation is carried out, the warehouse is picked out, transport and delivery are carried out after the shipper receives orders, the demand unit signs and receives the orders, and the whole business process completes the acquisition and input of full-process business data (types such as reservation, inspection, island, emergency and the like) and full-process dynamic characteristic variables (information such as weather, road conditions, GPS position information and the like) by means of interconnection sensing such as RFID, GPS positioning, vehicle-mounted positioning and the like; then, the logistics platform intelligently matches different cost algorithms of a logistics final cost algorithm model, including a multi-dimensional algorithm model of factors such as mileage intervals, mileage steps, vehicle carrying types, machine shifts, emergency rescue, optimal paths, linkage and the like, with the input full-flow service data and full-process dynamic characteristic variables, and the system model has the advantages of accurate processing process, rapidness, high efficiency and automatic output of logistics costs required by different delivery scenes; finally, the user can check the specific output calculation result in the logistics platform function, the user can also select the most suitable distribution mode to carry out distribution according to the calculation result, or the logistics platform automatically selects the distribution mode with lower cost to carry out logistics distribution, and meanwhile, the logistics distribution mode can also select other distribution modes based on the specific requirements of the user.

Further, the acquiring data of different distribution service scenes in power grid logistics includes:

acquiring different distribution service scenes in power grid logistics;

selecting a typical distribution scene from the distribution service scene through factor analysis, permutation and combination, difference analysis and induction summary, wherein the typical distribution scene comprises contract-leading distribution, submission and warehouse-returning distribution, cross-region distribution, emergency distribution, terminal distribution and island distribution;

setting a business process corresponding to the typical distribution scene;

the method comprises the steps of obtaining first distribution process dynamic characteristic data through a radio frequency, a Beidou positioning system, vehicle-mounted navigation equipment and an electronic fence, wherein the first distribution process dynamic characteristic data comprise weather, road conditions, vehicles, a GPS and vehicle-mounted positioning.

In a preferred embodiment of the invention, different distribution service scenes in power grid logistics are obtained in a power grid logistics database, and typical distribution scenes are selected from the distribution service scenes through factor analysis, permutation and combination, difference analysis and induction summary, wherein the typical distribution scenes comprise contract procurement distribution, inspection and warehouse return distribution, cross-regional distribution, emergency distribution, terminal distribution, island distribution and the like; and then setting a service flow corresponding to the typical distribution scene, acquiring dynamic data such as weather, road conditions, vehicles, GPS, vehicle positioning and the like in transit by using technologies such as radio frequency, a Beidou positioning system, vehicle navigation equipment, electronic fences and the like, and finishing continuous accumulation and updating of big data in the whole logistics process so as to obtain final data of different distribution service scenes in power grid logistics.

Further, the pricing model includes: mileage interval pricing, mileage step pricing, station shift pricing, intermodal pricing, emergency pricing and island pricing.

In the preferred embodiment of the present invention, the pricing model includes, but is not limited to, mileage interval pricing, mileage ladder pricing, shift pricing, intermodal pricing, emergency pricing, sea island pricing, etc.

Further, the initial cost algorithm model comprises a mileage interval algorithm, a mileage ladder algorithm, a vehicle carrying type algorithm, a shift algorithm, an emergency rescue algorithm, an optimal path algorithm and a linkage algorithm.

In the preferred embodiment of the present invention, the initial cost algorithm model includes but is not limited to a mileage interval algorithm, a mileage ladder algorithm, a vehicle carrying type algorithm, a shift algorithm, an emergency rescue algorithm, an optimal path algorithm, a joint algorithm, and other multi-cost algorithms.

Further, the service distribution and transportation of the customer delivery demand includes:

carrying out transport capacity negotiation on carriers, and dispatching the customer delivery requirements to the carriers meeting the transport capacity;

and picking and delivering the items corresponding to the customer delivery demands from the warehouse, and delivering the items through the carrier.

In a preferred embodiment of the invention, before distribution of the delivery demand, the carriers are consulted for the transport capacity, and only when the transport capacity corresponding to the delivery demand is met, the delivery demand is distributed to the carriers meeting the transport capacity, and the carriers distribute the goods corresponding to the delivery demand, thereby optimizing the environment of the delivery carriers.

The method comprises the steps of selecting various typical distribution scenes in modern logistics of a modern power grid, setting characteristic dynamic variables such as weather, paths, islands, cross-sea bridges, ferries, GPS satellite positioning and the like, fusing various algorithms such as mileage intervals, mileage steps, vehicle carrying types, station shift calculation, emergency calculation and the like, and realizing reasonable freight calculation under the condition of multivariable in different scenes through functional modeling of an information platform for power grid distribution. The method can balance the influence degree of each factor and each algorithm on the modern logistics cost of the power grid, obtains the optimal cost calculation result through intelligent model matching, and is beneficial to improving the calculation efficiency of the algorithm, realizing overall cost reduction and efficiency improvement and optimizing the distribution carrier environment.

In technical aspect: the application range of the internet of things sensing technology is expanded through field data acquisition systems such as vehicle-mounted internet of things equipment, monitoring equipment and traffic road condition service, and comprehensive awareness and whole-course controllability of logistics are realized; integration, analysis and application of big data are further grasped, technical support is provided for logistics cost modeling, and cost algorithm calculation efficiency is improved.

In the aspect of economy: through research and analysis of the optimal logistics cost, the goal of reasonably allocating logistics resources is achieved, the turnover of electric power materials can be accelerated, and the inventory cost of each warehouse in the system is reduced; the method has the advantages of scientifically planning the network, optimizing the distribution line, reasonably simplifying vehicles, improving the efficiency of a single vehicle, obtaining the benefit growth point of logistics sharing and intermodal transportation and improving the economic efficiency of a company.

In the social aspect: through popularization and application of the electric power logistics platform, the development of electric network logistics from traditional logistics to agile logistics is facilitated to be promoted, the internet of things technology is introduced, the control strength of safety in the way is enhanced, whole-course control is realized, various potential safety hazards are ensured to be discovered in time, scientifically prevented and controlled and effectively eliminated, safety guarantee is strengthened, and the environment of a delivery carrier is optimized.

Fig. 2 is a schematic structural diagram of a cost algorithm model building device based on power grid logistics according to an embodiment of the present disclosure, where the structure diagram includes:

the scene data acquisition module 201 is used for acquiring scene data of different distribution services in the power grid logistics;

a scene data selecting module 202, configured to select first distribution business process data and first distribution process dynamic characteristic data from the distribution business scene data;

the model construction module 203 is used for constructing an initial cost algorithm model based on pricing modes in different distribution scenes and different transportation modes;

the model generating module 204 is configured to train the initial cost algorithm model according to the first distribution business process data and the first distribution process dynamic characteristic data to generate a final cost algorithm model, where the final cost algorithm model is used to calculate the logistics cost.

Further, still include:

acquiring a customer distribution demand;

service distribution and transportation are carried out on the customer distribution demands, and second distribution service flow data and second distribution process dynamic characteristic data are obtained;

and calculating the logistics cost of the second distribution business process data and the second distribution process dynamic characteristic data through the final cost algorithm model to obtain the logistics cost corresponding to the customer distribution demand.

The invention improves the calculation efficiency of the cost algorithm and reduces the inventory cost of each warehouse in the system; scientifically planning the network, optimizing the distribution line, reasonably simplifying vehicles, improving the efficiency of a single vehicle, improving the economic efficiency and optimizing the distribution carrier environment.

Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.

In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.

Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 connecting different device components (including the memory unit 320 and the processing unit 310), a display unit 340, and the like.

Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps as shown in fig. 1.

The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM) 3201 and/or a cache storage unit 3202, and may further include a read only memory unit (ROM) 3203.

The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating device, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 330 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may 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 the network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives, and data backup storage devices, to name a few.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.

Fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present disclosure.

A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.