CN115375244A

CN115375244A - Fourth-party automobile accessory logistics platform based on big data and management method

Info

Publication number: CN115375244A
Application number: CN202211069107.2A
Authority: CN
Inventors: 黄海伦; 刘松; 王家伟; 吴志坚; 杨上富
Original assignee: Shenzhen Xiaoshi Express Technology Co ltd
Current assignee: Shenzhen Xiaoshi Express Technology Co ltd
Priority date: 2022-09-02
Filing date: 2022-09-02
Publication date: 2022-11-22
Anticipated expiration: 2042-09-02
Also published as: CN117094626A; CN115375244B

Abstract

The embodiment of the application provides a fourth-party automobile accessory logistics platform based on big data and a management method. The management method comprises the following steps: building a steam distribution chain among a maintenance unit, an accessory supplier, a transaction platform and a distribution unit, and analyzing maintenance requirements after the maintenance unit triggers the maintenance requirements to determine standby accessories and corresponding priorities thereof; and then matching the warehousing information of the warehousing unit through a self-adaptive distribution model constructed in advance based on the decision tree based on the information, determining a target warehouse corresponding to the spare part, issuing an order to the target warehouse, calling logistics resources of the distribution unit based on the priority to distribute, synchronizing the part demand information to the part supplier, and simultaneously coordinating with the processing of other process procedures.

Description

Fourth-party automobile accessory logistics platform based on big data and management method

Technical Field

The application relates to the technical field of computers, in particular to a big data-based fourth automobile accessory logistics platform, a management method, a computer readable medium and electronic equipment.

Background

The whole steam distribution can not be developed without stream, even can be regarded as complementary, and the steam distribution supply chain is essentially the combination of the commercial stream and the stream. The key point that the market is difficult to make after the automobile is the automobile part logistics distribution plate, a logistics distribution solution system which can meet the distribution requirements of most customers cannot be established in a short time, and the conveying efficiency is low.

Disclosure of Invention

Embodiments of the present application provide a fourth steam distribution logistics platform based on big data, a management method, a computer readable medium, and an electronic device, and further an online fourth steam distribution logistics platform can be established, so that the transportation efficiency of steam distribution is improved.

Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.

According to an aspect of an embodiment of the present application, there is provided a fourth method for parts logistics management based on big data, including: building a steam distribution chain based on a cloud platform among a maintenance unit, an accessory supplier, a transaction platform and a distribution unit; after a maintenance unit triggers a maintenance requirement, analyzing the maintenance requirement, and determining the identifier of the spare part and the priority corresponding to the spare part; determining a target storage corresponding to the spare parts in a storage unit through a self-adaptive distribution model constructed in advance based on a decision tree based on the identification and the corresponding priority of the spare parts, establishing order flow from the part supplier through the transaction platform by a maintenance unit, and establishing logistics for the maintenance unit by the part supplier; generating an order based on the standby accessory identification, and sending the order to the target storage; and extracting the spare parts from the target warehouse, calling logistics resources of a distribution unit for distribution based on the priority, and tracking a distribution progress to generate logistics information.

In some embodiments of the present application, based on the foregoing solution, the repair requirements include a first category of repair requirements and a second category of repair requirements, where the first category of repair requirements refers to a determination of the type and number of the parts in the repair information, and the second category of repair requirements refers to a determination of the type and number of the parts in the repair information; before determining the identification of the standby accessories and the corresponding priority of the standby accessories, the method further comprises the following steps: determining whether the first type of maintenance requirement or the second type of maintenance requirement belongs to; if the first type of maintenance requirements exist, acquiring the identification of each accessory; and if the repair is required by the second type, matching out necessary repair parts and optional repair parts according to a fuzzy recognition algorithm.

In some embodiments of the present application, based on the foregoing solution, the second type of maintenance requirement is a maintenance requirement after a vehicle collision, and if the second type of maintenance requirement is the second type of maintenance requirement, matching a necessary repair part and an optional repair part according to a fuzzy recognition algorithm includes: acquiring a plurality of images of a vehicle, identifying the images, and determining the vehicle type and a fault area according to the fuzzy identification algorithm; and determining necessary repair parts in the fault area, and matching the repair parts according to the vehicle type.

In some embodiments of the present application, based on the foregoing solution, the analyzing the maintenance requirement and determining the identifier of the standby component and the corresponding priority of the standby component includes: recognizing the text content corresponding to each text identifier in the maintenance requirement, and determining an identifier field containing an accessory identifier; using the identification field as the standby accessory identification; determining accessory information of accessories to be used from an accessory library, wherein the accessory information comprises quantity information, cost information, production data and storage information; quantizing the accessory information to obtain quantized information; determining a priority parameter of the standby accessory based on the quantitative information; and determining the corresponding priority of the spare accessory based on the priority parameter.

In some embodiments of the present application, based on the foregoing scheme, the quantizing the accessory information to obtain quantized information includes: obtaining production extreme value information corresponding to production data in the accessory information; and quantifying the accessory information based on the difference value between the production extreme value information and the production data, and determining quantitative information corresponding to the production data.

In some embodiments of the present application, based on the foregoing solution, the determining, by an adaptive delivery model constructed in advance based on a decision tree, a target warehouse corresponding to the standby part in a warehouse unit based on the standby part identifier and the corresponding priority thereof includes: determining at least one standby warehouse matched with the standby accessory identification from the warehouse information of a warehouse unit; estimating logistics time based on the information of the standby storage and the information of the standby accessories; and determining the target storage corresponding to the spare parts from the storage units through a pre-constructed adaptive distribution model based on the logistics time, the priority of the spare parts and the conveying difficulty parameter.

In some embodiments of the present application, based on the foregoing scheme, determining at least one spare warehouse matching the inactive accessory identifier from warehouse information of a warehouse unit includes: respectively determining the target storage of the accessories to be repaired and the target storage of the accessories to be repaired according to a pre-constructed storage network based on the accessory information of the accessories to be repaired and the accessories to be selected and repaired; or determining accessory combination information based on the accessory information of the required accessories and the selected accessories, and determining the target storage of the accessory combination according to a pre-constructed storage network.

In some embodiments of the present application, estimating the logistics time based on the information of the standby warehouse and the information of the standby accessories, based on the aforementioned scheme, includes: determining logistics time corresponding to each spare warehouse according to at least one dimension of logistics transportation capacity parameters, distances between warehouse positions and maintenance units, accessory volumes and transportation difficulty levels;

determining target storage corresponding to the spare parts from the storage units through a pre-constructed adaptive distribution model based on the logistics time, the priority of the spare parts and the conveying difficulty parameters, wherein the target storage comprises the following steps: inputting the transportation difficulty parameter corresponding to the spare part, the logistics time corresponding to each spare warehouse and the priority of the spare part into the self-adaptive distribution model based on the self-adaptive distribution model obtained based on decision tree training in advance, and outputting the loss function value corresponding to each spare warehouse; and determining the corresponding storage identification when the loss function value is minimum, and taking the storage corresponding to the storage identification as the target storage.

In some embodiments of the present application, based on the foregoing solution, the method further comprises: if the warehousing information of the warehousing unit is not matched with the accessory information corresponding to the to-be-used accessory identification, the accessory requirement is sent to a corresponding accessory supplier; and acquiring a production scheduling receipt returned by the accessory supplier.

In some embodiments of the present application, based on the foregoing solution, after the extracting and delivering the parts from the target warehouse and tracking the delivery progress to generate the logistics information, the method further includes: synchronizing the logistics information to the maintenance unit and the target warehouse; and when the signing-in information triggered by a maintenance unit is acquired, synchronizing the distribution completion information of the accessories to the automobile distribution chain.

In some embodiments of the present application, based on the foregoing solution, the method further comprises: generating block chain link points corresponding to a maintenance unit, an accessory supplier, a transaction platform and a distribution unit respectively based on a block chain technology; processing a steam distribution maintenance order among the blockchain nodes according to a blockchain consensus mechanism; and accounting is carried out on the steam distribution maintenance orders in different states, and the orders are synchronized to other block chain nodes.

In some embodiments of the present application, based on the foregoing solution, the method further comprises: when the logistics information is signed, triggering transfer information corresponding to the maintenance accessories; sending the transfer information to the maintenance unit to pay the money of the maintenance accessory; and triggering accessory transaction completion information when the payment completion information sent by the maintenance unit and the collection information sent by the storage unit are obtained.

According to an aspect of an embodiment of the present application, there is provided a fourth big-data-based steam accessories logistics platform, comprising:

the cloud platform unit is used for constructing a steam distribution chain based on the cloud platform among a maintenance unit, an accessory supplier, a transaction platform and a distribution unit;

the analysis unit is used for analyzing the maintenance requirement after the maintenance unit triggers the maintenance requirement, and determining the identifier of the spare part and the priority corresponding to the spare part;

the matching unit is used for determining target storage corresponding to the spare accessories in a storage unit through a self-adaptive distribution model constructed in advance based on the spare accessory identification and the corresponding priority of the spare accessory identification;

the order unit is used for generating an order based on the standby accessory identification and issuing the order to the target storage;

and the logistics unit is used for extracting the spare parts from the target storage, calling logistics resources of a distribution unit based on the priority to distribute and tracking a distribution progress to generate logistics information.

In some embodiments of the application, based on the foregoing scheme, the quantizing the accessory information to obtain quantized information includes: obtaining production extreme value information corresponding to production data in the accessory information; and quantizing the accessory information based on the difference between the production extreme value information and the production data, and determining the quantized information corresponding to the production data.

In some embodiments of the present application, based on the foregoing solution, the determining, by an adaptive delivery model constructed in advance based on a decision tree, a target warehouse corresponding to the standby part in a warehouse unit based on the standby part identifier and the corresponding priority thereof includes: determining at least one standby warehouse matched with the standby accessory identification from the warehouse information of a warehouse unit; estimating logistics time based on the information of the standby storage and the information of the standby accessories; and determining target storage corresponding to the spare accessories from the storage units through a pre-constructed self-adaptive distribution model based on the logistics time, the priority of the spare accessories and the conveying difficulty parameter.

In some embodiments of the present application, based on the foregoing scheme, determining at least one spare warehouse matching the inactive accessory identifier from warehouse information of a warehouse unit includes: respectively determining the target storage of the accessories to be repaired and the target storage of the accessories to be repaired according to a pre-constructed storage network based on the accessory information of the accessories to be repaired and the accessories to be selected and repaired; or determining accessory combination information based on accessory information of the necessary accessories and the selected accessories, and determining the target storage of the accessory combination according to a pre-constructed storage network.

In some embodiments of the present application, estimating the logistics time based on the information of the standby warehouse and the information of the standby accessories, based on the aforementioned scheme, includes: determining logistics time corresponding to each spare warehouse according to at least one dimension of logistics capacity parameters, distances between warehouse positions and maintenance units, accessory volumes and transportation difficulty levels;

In some embodiments of the present application, based on the foregoing solution, the repair requirements include a first category of repair requirements and a second category of repair requirements, where the first category of repair requirements refers to a determination of the type and number of the parts in the repair information, and the second category of repair requirements refers to a determination of the type and number of the parts in the repair information; before determining the identification of the standby accessories and the corresponding priority of the standby accessories, the method further comprises the following steps: determining whether the first type or the second type of maintenance requirements belongs; if the first type of maintenance requirements exist, acquiring the identification of each accessory; and if the repair is required by the second type, matching out necessary repair parts and optional repair parts according to a fuzzy recognition algorithm.

In some embodiments of the present application, based on the foregoing solution, after the extracting and delivering the parts from the target warehouse and tracking the delivery progress to generate the logistics information, the method further includes: synchronizing the logistics information to the maintenance unit and the target warehouse; and when the signing-in information triggered by the maintenance unit is acquired, synchronizing the accessory distribution completion information to the automobile distribution chain.

In some embodiments of the present application, based on the foregoing solution, the method further includes: generating block link points corresponding to a maintenance unit, an accessory supplier, a transaction platform and a distribution unit respectively based on a block chain technology; processing a steam distribution maintenance order among the blockchain nodes according to a blockchain consensus mechanism; and accounting is carried out on the steam distribution maintenance orders in different states, and the orders are synchronized to other block chain nodes.

In some embodiments of the present application, based on the foregoing solution, the method further includes: when the logistics information is signed, triggering transfer information corresponding to the maintenance accessories; sending the transfer information to the maintenance unit to pay the money of the maintenance accessory; and triggering accessory transaction completion information when the payment completion information sent by the maintenance unit and the collection information sent by the storage unit are obtained.

In some embodiments of the present application, based on the foregoing solution, the system further includes: the sending unit is used for sending the accessory requirement to a corresponding accessory supplier if the warehousing information of the warehousing unit is not matched with the accessory information corresponding to the standby accessory identification; and the acquisition unit is used for acquiring the production scheduling receipt returned by the accessory supplier.

According to an aspect of embodiments of the present application, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements the big-data based fourth method for steam distribution management as described in the above embodiments.

According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a fourth big-data based method of parts distribution management as described in the above embodiments.

According to an aspect of an embodiment of the present application, there is provided a computer program product or a computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the big data-based fourth method for steam distribution management provided in the above-mentioned various optional implementations.

In the technical scheme provided by some embodiments of the application, a steam distribution chain is constructed among a maintenance unit, an accessory supplier, a transaction platform and a distribution unit, and after the maintenance unit triggers a maintenance requirement, the maintenance requirement is analyzed to determine standby accessories and corresponding priorities thereof; then, based on the standby part identification and the corresponding priority of the standby part identification, a target warehouse corresponding to the standby part is determined in a warehouse unit through a self-adaptive distribution model constructed in advance based on a decision tree, an order is issued to the target warehouse, logistics resources of the distribution unit are called based on the priority to distribute, part demand information is synchronized to a part supplier, and meanwhile, processing of other process procedures is coordinated.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

FIG. 1 illustrates a flow diagram of a fourth big-data based method for steam distribution management according to an embodiment of the present application;

FIG. 2 shows a schematic diagram of a auto distribution chain according to an embodiment of the present application;

FIG. 3 illustrates a flow diagram for determining a type of repair request according to an embodiment of the present application;

FIG. 4 illustrates a flow diagram for determining standby accessory information according to one embodiment of the present application;

FIG. 5 shows a schematic of a fourth big-data based sparkler logistics platform according to an embodiment of the present application;

FIG. 6 illustrates a schematic structural diagram of a computer system suitable for use to implement the electronic device of the embodiments of the present application.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods and systems, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.

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 flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. 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 implementation details of the technical solution of the embodiment of the present application are set forth in detail below:

fig. 1 illustrates a flow diagram of a fourth big-data based method of parts logistics management, according to an embodiment of the present application. Referring to fig. 1, the fourth method for managing the steam distribution based on big data at least includes steps S110 to S150, which are described in detail as follows:

in step S110, a cloud platform based steam distribution chain is constructed among the maintenance unit, the accessory supplier, the transaction platform, and the distribution unit.

In one embodiment of the present application, a plurality of institutions or units, such as maintenance units, parts suppliers, trading platforms, distribution units, etc., are involved in the overall automobile repair process. Therefore, in the embodiment, a cloud platform-based steam distribution chain is constructed between the above-mentioned organizations or units to attribute a plurality of businesses to one system for coordination processing.

As shown in fig. 2, in this embodiment, a warehouse is used as a center, a network (1) is constructed based on a long-distance low-frequency transaction network, a network (2) -1 is constructed based on a local high-frequency transaction network, and the two networks are linked through trunk logistics; and then, a trading network (2) -2 taking the city automobile distribution city as a center is constructed, a merchant supply service network (3) and a network (4) from a maintenance factory to a C end (a vehicle owner end) are constructed, and an automobile distribution chain based on maintenance units, accessory suppliers, a trading platform and distribution units is realized through the cooperative connection among the networks.

Optionally, in this embodiment, the different types of mechanisms (or units) may be two or more, so as to ensure the operation of normal services.

The cloud platform in the embodiment is a data module constructed on the basis of information of a maintenance unit, an accessory supplier and a transaction platform; the transaction module is constructed based on a machine learning technology in artificial intelligence and is used for performing transaction matching, intelligent quotation and on-demand service distribution. The cloud platform in the embodiment realizes the functions of automatic transaction matching, intelligent quotation and on-demand service distribution by constructing a transaction matching model and an algorithm of the automobile industry based on industry data and transaction data accumulated by the platform history and by using a machine learning technology in artificial intelligence, and greatly improves transaction efficiency through continuous self-optimization and improvement.

In one embodiment of the present application, the method further comprises:

generating block link points corresponding to a maintenance unit, an accessory supplier, a transaction platform and a distribution unit respectively based on a block chain technology;

circulating and processing a steam distribution maintenance order among the block chain nodes according to a block chain consensus mechanism;

and accounting is carried out on the steam distribution maintenance orders in different states, and the orders are synchronized to other block chain nodes.

Specifically, in the embodiment, based on the blockchain technology, the repair units, the accessory suppliers, the transaction platform, and the distribution units are used as blockchain nodes to construct the block chain network-based steam distribution system. The method comprises the steps of carrying out processing such as steam distribution maintenance order circulation and allocation in a block chain network, carrying out bookkeeping on steam distribution maintenance orders in different states, and synchronizing the orders to other block chain nodes. The purposes that data are difficult to tamper and decentralized are achieved through the method, and based on the two characteristics, the information recorded by the block chain is more real and reliable.

In an embodiment of the present application, the maintenance requirements include first category maintenance requirements and second category maintenance requirements, where the first category maintenance requirements refer to that the types and the number of the components in the maintenance information are determined, and the second category maintenance requirements refer to that the types and the number of the components in the maintenance information are not determined, and in this case, component quantification needs to be performed.

Referring to FIG. 3, a flow diagram of determining a type of repair request according to one embodiment of the present application is shown. In an embodiment of the present application, before determining the identifier of the standby accessory and the corresponding priority of the standby accessory, the method further includes:

step S210, determining whether the maintenance requirements belong to a first type or a second type; if the first type of maintenance requirement is met, executing step S220; if the service requirement is the second type, executing step S230;

step S220, acquiring the identification of each accessory;

and step S230, matching a necessary part and a selected part according to a fuzzy recognition algorithm.

In this embodiment, after the maintenance requirement is obtained, a judgment is first made to determine whether the maintenance requirement belongs to a first type of maintenance requirement or a second type of maintenance requirement. If the first type of maintenance requirements indicate that the first type of maintenance requirements have clear accessory information, acquiring the identification of each accessory; and if the repair is required by the second type, matching out necessary repair parts and optional repair parts according to a fuzzy recognition algorithm.

Illustratively, if the second type of maintenance requirement is a vehicle post-collision maintenance requirement, acquiring a plurality of images of the vehicle, identifying the images, and determining the vehicle type and the fault area according to the fuzzy identification algorithm; and determining necessary repair accessories of the incomplete fault area, and matching the repair accessories according to the vehicle type. Specifically, the fuzzy recognition algorithm in this embodiment may be a fuzzy algorithm based on a laplacian operator, and the method includes obtaining multiple images corresponding to the same position of the vehicle, performing gray processing on the multiple images to obtain a gray image, performing laplacian change on the gray image, obtaining a variance of the changed images, and determining whether the images are clear based on a set threshold to segment the clear images to determine the vehicle type and the fault region. And then, based on the vehicle type, the fault area and the original fault map corresponding to the vehicle type, restoring the explosion map of the fault area, and deleting residual accessories or intact accessories so as to obtain the missing accessories. Meanwhile, the spare parts which are incomplete in the fault area are used as the parts which need to be repaired, and the spare parts which are obtained according to vehicle type matching are used as the selected repair parts.

In step S120, after the maintenance unit triggers the maintenance requirement, the maintenance requirement is analyzed to determine the identifier of the standby component and the corresponding priority of the standby component.

In one embodiment of the application, after a maintenance unit triggers a maintenance requirement, character analysis is performed on characters in the maintenance requirement, an identifier of a spare part in the character analysis is extracted, and a priority corresponding to the spare part is determined, so that the spare part is allocated based on the information.

FIG. 4 illustrates a flow diagram for determining standby accessory information according to one embodiment of the present application. In an embodiment of the present application, as shown in fig. 4, analyzing the maintenance requirement and determining the identifier of the spare part and the corresponding priority of the spare part includes steps S310 to S360:

s310, recognizing the text content corresponding to each text identifier in the maintenance requirement, and determining an identifier field containing an accessory identifier;

s320, using the identification field as the identification of the spare part;

s330, determining accessory information of the accessories to be used from an accessory library, wherein the accessory information comprises quantity information, cost information, production data and storage information;

s340, quantizing the accessory information to obtain quantized information;

s350, determining a priority parameter of the standby accessory based on the quantitative information;

and S360, determining the corresponding priority of the spare accessory based on the priority parameter.

Specifically, in this embodiment, the text content corresponding to each text identifier in the maintenance requirement may be identified by using an OCR technology, and the identifier field including the accessory identifier is determined as the identifier of the spare accessory. The accessory information of the spare accessories is determined from the accessory library, and the accessory information in the embodiment can comprise the current quantity information of the accessories, cost information representing unit price, production data representing production efficiency in unit time and storage information of each warehousing unit for the accessories. After the accessory information is acquired, the accessory information is quantized to obtain quantized information, and then priority parameters and priorities are determined based on the quantized information so as to comprehensively consider the condition of the accessory.

In an embodiment of the present application, the quantizing the accessory information in step S340 to obtain quantized information includes:

obtaining production extreme value information corresponding to production data in the accessory information;

and quantizing the accessory information based on the difference between the production extreme value information and the production data, and determining the quantized information corresponding to the production data.

In an embodiment of the present application, the production data may be a production quantity per unit time. In this embodiment, the production extreme value information corresponding to the production data in the accessory information is obtained, where the production extreme value information may include a production minimum value in unit time

And producing maxima

. Based on production min and production data of the day

First difference between

A second difference between the production maximum and the production data of the day

Determining quantitative production data corresponding to the production data

Comprises the following steps:

wherein ,

indicates the production factor. In the process, the production data is subjected to quantization processing to obtain accurate quantized production data, and the production information of one accessory is used as one of factors for measuring the shortage degree of the accessory.

In addition, in the embodiment, the quantitative information can be obtained by quantizing the quantity information, the cost information and the storage information, and then the shortage degree of a certain accessory is measured by the quantitative information, so that the priority degree of a certain accessory is measured by the shortage degree. For example, the more a fitting is in short, the higher the priority of the fitting.

In an embodiment of the present application, quantization quantity information may be included based on the quantization information

Quantifying cost information

And quantifying production data

And quantizing the stored information

Determining the priority parameters of the spare part

Comprises the following steps:

wherein ,

representing a quantity factor, a storage factor, and a cost factor, respectively. In the mode, the priority parameters corresponding to the accessories are obtained by comprehensively processing the information of various accessories, so that the importance degree of the accessories is measured through the priority parameters.

In this embodiment, after the priority parameter is determined, the priority level in which the priority parameter is located is determined based on the priority threshold range corresponding to each set priority level

. Optionally, in this embodiment, the priority levels may be three or more, and the higher the level is, the more important or the shortage of the accessories is indicated, and the high matching between the warehousing and logistics is required to complete the dispatching of the accessories in a short time.

In step S130, based on the identifier of the spare part and its corresponding priority, a target warehouse corresponding to the spare part is determined in a warehouse unit through an adaptive distribution model constructed in advance based on a decision tree.

In one embodiment of the application, after the identification of the spare parts and the priority thereof are determined, the latest warehousing information of each warehousing unit is obtained and matched in the warehousing information, and the target warehousing matched with the spare parts and the priority thereof is determined.

In an embodiment of the present application, the determining, in step S130, a target warehouse corresponding to the spare parts in the warehousing unit through an adaptive delivery model constructed in advance based on the decision tree includes:

determining at least one standby warehouse matched with the standby accessory identification from the warehouse information of a warehouse unit;

estimating logistics time based on the information of the standby storage and the information of the standby accessories;

and determining the target storage corresponding to the spare parts from preset storage units through a pre-constructed self-adaptive distribution model based on the logistics time, the priority of the spare parts and the conveying difficulty parameter.

Specifically, in this embodiment, the storage information and the identifier of the spare part are simply matched to determine the spare storage in which the spare part is stored. It should be noted that the number of the spare stockers in this embodiment may be one, two, or more, and the spare stockers storing the spare parts may be determined by text matching, so as to select the target stocker and delivery site with the lowest logistics cost and the highest logistics efficiency from the spare stockers for delivery.

Optionally, in this embodiment, when determining the standby storage, the accessory information is searched according to a pre-constructed storage network based on the accessory information of the necessary accessory and the optional accessory, and a target storage of the necessary accessory and a target storage of the optional accessory are respectively determined; or, determining accessory combination information based on the accessory information of the necessary accessories and the selected accessories, for example, generating a character string formed by an accessory identifier, and then searching the character string information according to a pre-constructed warehousing network to determine the target warehousing of the accessory combination.

But the use of spare parts can be affected due to different storage unit conditions, such as distance, logistics or storage quality, etc. Therefore, the logistics time is estimated based on the information of the standby warehouse and the information of the standby accessories in the embodiment, so that the proper target warehouse can be obtained through matching based on the logistics time and the priority of the standby accessories.

In this embodiment, the warehousing information of each warehousing unit can be acquired, and the standby warehousing in which the accessory is stored is determined by matching the warehousing information with the identifier of the accessory to be used. Then, the logistics time is estimated based on the information of the standby storage and the information of the spare accessories, and the information of the standby storage in the implementation can comprise the distance between the storage position and the maintenance unit

Current logistics transportation capacity parameter of storage unit

Etc., the information of the spare part may include the volume of the part

Transport difficulty parameter

The information is evaluated to obtain the logistics time corresponding to each standby warehouse

Comprises the following steps:

wherein ,

indicating a shipmentA factor. In the process, information such as logistics transportation capacity parameters, distances between storage positions and maintenance units, accessory volumes and transportation difficulty is considered in the estimation of logistics time, and when the logistics transportation capacity parameters are higher, the distances are shorter, the volumes are smaller, and the transportation difficulty parameters are smaller, smaller logistics time is obtained.

Optionally, in this embodiment, after the logistics time is obtained through estimation in the above manner, based on the logistics time, the priority of the spare part, and the transportation difficulty parameter, the target warehouse corresponding to the spare part is determined from the warehousing unit through a pre-constructed adaptive distribution model. For example, if it is estimated that the logistics time corresponding to each spare warehouse is short and the priority of the spare parts is high, the spare warehouse with short logistics time is selected as the target warehouse.

Further, in this embodiment, warehousing information corresponding to each warehouse and distribution information corresponding to a distribution point are obtained in advance, where the warehousing information and the distribution information both include location information and distribution cost information. And then, based on the warehousing information and the delivery information, constructing a self-adaptive delivery model through a decision tree and training. And each standby warehousing and distribution site in the model is used as a node in the decision tree, wherein the warehousing information and the distribution site corresponding to the standby warehousing are used as node information of the node.

When the distribution strategy is selected, the input information comprises the corresponding priority of the spare accessories

Corresponding delivery time when all the sites deliver cooperatively

And a delivery difficulty parameter corresponding to the spare part

Taking the logistics time corresponding to each warehousing delivery calculated by the steps as variable information of the decision tree, and taking the loss function as all warehousing or distributionCost of site-to-site correspondence

：

wherein ,

and (3) representing loss factors obtained by training, i representing the storage identifiers or the identifiers of all distribution stations along the logistics line, wherein the total delivery accessories need k distribution stations, the information is input into the self-adaptive distribution model, and when the output loss function is low, the self-adaptive distribution model can decide to output the corresponding target storage or distribution station with short distribution time and low distribution cost. According to the method, the loss function is determined based on the distribution difficulty parameter and the distribution time corresponding to each station, the distribution cost is higher when the loss function is higher, then the method cannot be selected for distribution, the distribution cost is lower when the loss function is lower, the corresponding target warehouse in the decision can be used for extracting the accessories, the corresponding distribution station is determined to be used for distributing the accessories, and then the accessory conveying cost is reduced on the premise that the distribution efficiency is guaranteed.

Optionally, the target storage can also be a plurality of for send the accessory to the maintenance unit through different target storage, can carry out the contrast of accessory quality and select the optimum to handle, also can carry out the accessory storage, be convenient for later have under the condition of demand directly use can, improve accessory conveying efficiency and availability factor through this kind of mode.

In one embodiment of the present application, the method further comprises:

if the warehousing information of the warehousing unit is not matched with the accessory information corresponding to the to-be-used accessory identification, the accessory requirement is sent to a corresponding accessory supplier;

and acquiring a production scheduling receipt returned by the accessory supplier.

Specifically, if the warehousing information matched with the identification of the spare part is not acquired, that is, no spare part exists in all current warehousing units, the part requirement needs to be sent to the corresponding part supplier so as to call the part from the part supplier. And then the accessory supplier returns a production scheduling receipt and informs the information such as the accessory production progress, time and the like of the demand party. Reliability and efficiency of accessory acquisition are guaranteed through the mode.

In step S140, generating an order based on the standby accessory identifier, and placing the order to the target storage; the repair entity establishes an order flow from the accessory supplier through the trading platform, and the accessory supplier establishes a logistics to the repair entity.

In one embodiment of the application, after the target warehouse is determined, an order is generated based on the standby accessory identification and sent to the target warehouse, so that the maintenance unit establishes an order flow from the accessory supplier through the transaction platform and logistics are established from the accessory supplier to the maintenance unit.

In this embodiment, the order may be generated through an order template, which may include information such as an accessory identifier, a repair unit address, and an accessory price.

In step S150, the spare parts are extracted from the target warehouse, the logistics resources of the distribution unit are called for distribution based on the priority, and the distribution progress is tracked to generate logistics information.

In one embodiment of the present application, after the target bin is determined, the spare part is extracted from the target bin. And determines the corresponding logistics resources for distribution based on the priority. In this embodiment, the distribution units may be classified into different distribution units according to the logistics speed, and the higher the logistics speed is, the higher the distribution unit is, the distribution unit is distributed to the standby accessories with higher priority, so as to improve the utilization rate of the logistics resources. And in the process of transportation, the distribution progress is tracked, logistics information is generated and synchronized to a maintenance unit and a storage unit. To ensure the reliability and safety of the delivery process.

It should be noted that the logistics resources in this embodiment may include third-party logistics and private logistics, and preferably, the third-party logistics occupies a majority. The method in the embodiment of the invention is realized based on a fourth part of the steam distribution logistics platform. The fourth-party automobile distribution logistics platform implementation refers to a platform for completing intelligent automobile distribution based on an automobile distribution chain among a maintenance unit, an accessory supplier, a transaction platform and a distribution unit.

In an embodiment of the present application, after extracting and delivering accessories from a target warehouse and tracking a delivery progress to generate logistics information, the method further includes:

synchronizing the logistics information to the maintenance unit and the target warehouse;

and when the signing-in information triggered by a maintenance unit is acquired, synchronizing the distribution completion information of the accessories to the automobile distribution chain.

Specifically, in the transportation process, the logistics information is generated and updated in real time, and is synchronized to the maintenance unit and the target warehouse. And when the signing-in information triggered by the maintenance unit is acquired, generating the accessory distribution completion information, and synchronizing the accessory distribution completion information to the automobile distribution chain.

In one embodiment of the present application, the method further comprises:

when the logistics information is signed, triggering transfer information corresponding to the maintenance accessories;

sending the transfer information to the maintenance unit to pay the money of the maintenance accessory;

and triggering accessory transaction completion information when the payment completion information sent by the maintenance unit and the collection information sent by the storage unit are obtained.

Specifically, when the logistics information is signed, transferring information corresponding to the maintenance accessories is generated and sent to the maintenance unit, so that the maintenance unit is informed to pay the money of the maintenance accessories. And then triggering accessory transaction completion information when the payment completion information sent by the maintenance unit and the collection information sent by the storage unit are obtained. By the method, the safe collection and payment of the order money are ensured, and the safety and reliability of the transaction are improved.

The following describes embodiments of the apparatus of the present application that may be used to perform the big data based fourth method of parts logistics management of the present application in the above-described embodiments. It will be appreciated that the apparatus may be a computer program (comprising program code) running on a computer device, for example an application software; the apparatus may be used to perform the corresponding steps in the methods provided by the embodiments of the present application. For details not disclosed in the apparatus embodiments of the present application, reference is made to the big data based fourth method for steam distribution management as described above in the present application.

In one-to-one correspondence with the above-described embodiments of the big-data based fourth method for parts logistics management, fig. 5 illustrates a block diagram of a big-data based fourth method for parts logistics platform, according to an embodiment of the present application.

Referring to fig. 5, a big data based fourth steam accessories logistics platform 300 according to one embodiment of the present application includes:

the cloud platform unit 410 is used for constructing a steam distribution chain based on a cloud platform among a maintenance unit, an accessory supplier, a transaction platform and a distribution unit;

the analysis unit 420 is configured to analyze the maintenance requirement after the maintenance unit triggers the maintenance requirement, and determine an identifier of the standby component and a priority corresponding to the standby component;

the matching unit 430 is used for determining a target warehouse corresponding to the spare parts in a warehousing unit through a self-adaptive distribution model constructed in advance based on a decision tree based on the identifiers of the spare parts and the corresponding priorities of the spare parts;

the order unit 440 is used for generating an order based on the standby accessory identification and placing the order to the target storage;

the logistics unit 450 is configured to extract the spare parts from the target warehouse, call logistics resources of a distribution unit based on the priority to distribute, and track a distribution progress to generate logistics information.

In some embodiments of the present application, based on the foregoing solution, the analyzing the maintenance requirement, and determining the identifier of the standby component and the corresponding priority of the standby component includes: recognizing the text content corresponding to each text identifier in the maintenance requirement, and determining an identifier field containing an accessory identifier; taking the identification field as the standby accessory identification; determining accessory information of accessories to be used from an accessory library, wherein the accessory information comprises quantity information, cost information, production data and storage information; quantizing the accessory information to obtain quantized information; determining a priority parameter of the standby accessory based on the quantitative information; and determining the corresponding priority of the spare accessory based on the priority parameter.

In some embodiments of the application, based on the foregoing scheme, the quantizing the accessory information to obtain quantized information includes: obtaining production extreme value information corresponding to production data in the accessory information; and quantifying the accessory information based on the difference value between the production extreme value information and the production data, and determining quantitative information corresponding to the production data.

determining target storage corresponding to the spare accessories from the storage units through a pre-constructed self-adaptive distribution model based on the logistics time, the priority of the spare accessories and the conveying difficulty parameters, and comprising the following steps of: inputting the transportation difficulty parameter corresponding to the spare part, the logistics time corresponding to each spare warehouse and the priority of the spare part into the self-adaptive distribution model based on the self-adaptive distribution model obtained based on decision tree training in advance, and outputting the loss function value corresponding to each spare warehouse; and determining the corresponding storage identification when the loss function value is minimum, and taking the storage corresponding to the storage identification as the target storage.

In some embodiments of the present application, based on the foregoing solution, the repair requirements include a first category of repair requirements and a second category of repair requirements, where the first category of repair requirements refers to a determination of the type and number of the parts in the repair information, and the second category of repair requirements refers to a determination of the type and number of the parts in the repair information; before determining the identification of the standby accessories and the corresponding priority of the standby accessories, the method further comprises the following steps: determining whether the first type or the second type of maintenance requirements belongs; if the maintenance requirements are of the first type, acquiring the identification of each accessory; and if the repair is required by the second type, matching out necessary repair parts and optional repair parts according to a fuzzy recognition algorithm.

In some embodiments of the present application, based on the foregoing solution, the method further includes: generating block chain link points corresponding to a maintenance unit, an accessory supplier, a transaction platform and a distribution unit respectively based on a block chain technology; circulating and processing a steam distribution maintenance order among the block chain nodes according to a block chain consensus mechanism; and accounting is carried out on the steam distribution maintenance orders in different states, and the orders are synchronized to other block chain nodes.

In some embodiments of the present application, based on the foregoing solution, the method further includes: when the logistics information is signed, money transferring information corresponding to the maintenance accessories is triggered; sending the transfer information to the maintenance unit to pay the money of the maintenance accessory; and triggering accessory transaction completion information when the payment completion information sent by the maintenance unit and the collection information sent by the storage unit are obtained.

FIG. 6 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.

It should be noted that the computer system 500 of the electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for system operation are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An Input/Output (I/O) interface 505 is also connected to bus 504.

The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output section 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.

In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 501.

It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a 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. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with a computer program 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 computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in the various alternative implementations described above.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A fourth method for managing the steam distribution logistics based on big data is characterized by comprising the following steps:

building a steam distribution chain based on a cloud platform among a maintenance unit, an accessory supplier, a transaction platform and a distribution unit;

after a maintenance unit triggers a maintenance requirement, analyzing the maintenance requirement, and determining the identifier of the spare part and the priority corresponding to the spare part;

determining target storage corresponding to the spare parts in a storage unit through a self-adaptive distribution model constructed in advance based on a decision tree based on the identifier of the spare parts and the corresponding priority of the spare parts;

generating an order based on the standby accessory identification, and sending the order to the target storage; the repair organization establishing an order flow from the accessory supplier through the transaction platform, the accessory supplier establishing a logistics to the repair organization;

and extracting the spare accessories from the target storage, calling logistics resources of a distribution unit based on the priority to distribute, and tracking a distribution progress to generate logistics information.

2. The method of claim 1, wherein the repair requirements include a first category of repair requirements and a second category of repair requirements, the first category of repair requirements being a determination of a type and quantity of parts in the repair information, the second category of repair requirements being a determination of a type and quantity of parts in the repair information; before determining the identification of the standby accessories and the corresponding priority of the standby accessories, the method further comprises the following steps:

determining whether the first type of maintenance requirement or the second type of maintenance requirement belongs to;

if the first type of maintenance requirements exist, acquiring the identification of each accessory;

and if the repair requirements are of the second type, matching out necessary repair parts and optional repair parts according to a fuzzy recognition algorithm.

3. The method of claim 2, wherein the second type of repair requirement is a post-collision vehicle repair requirement, and if the second type of repair requirement is the second type of repair requirement, matching a required repair part and an optional repair part according to a fuzzy recognition algorithm comprises:

acquiring a plurality of images of the vehicle, identifying the images, and determining the vehicle type and the fault area according to the fuzzy identification algorithm;

and determining necessary repair parts in the fault area, and matching the repair parts according to the vehicle type.

4. The method of claim 1, wherein analyzing the repair requirements to determine the identification of the inactive parts and the corresponding priorities of the inactive parts comprises:

recognizing the text content corresponding to each text identifier in the maintenance requirement, and determining an identifier field containing an accessory identifier;

using the identification field as the standby accessory identification;

determining accessory information of accessories to be used from an accessory library, wherein the accessory information comprises quantity information, cost information, production data and storage information;

quantizing the accessory information to obtain quantized information;

determining a priority parameter of the standby accessory based on the quantitative information;

and determining the corresponding priority of the spare accessory based on the priority parameter.

5. The method of claim 4, wherein quantizing the accessory information to obtain quantized information comprises:

acquiring production extreme value information corresponding to production data in the accessory information;

and quantifying the accessory information based on the difference value between the production extreme value information and the production data, and determining quantitative information corresponding to the production data.

6. The method according to claim 3, wherein the determining of the target warehouse corresponding to the spare parts in the warehouse unit through an adaptive delivery model constructed in advance based on the decision tree based on the identifier of the spare parts and the corresponding priority thereof comprises:

determining at least one standby storage matched with the standby accessory identification from the storage information of a storage unit;

and determining target storage corresponding to the spare parts from the storage units through a pre-constructed self-adaptive distribution model based on the logistics time, the priority of the spare parts and the conveying difficulty parameters.

7. The method of claim 6, wherein determining at least one spare bin from bin information of a bin entity that matches the inactive accessory identification comprises:

respectively determining the target storage of the accessories to be repaired and the target storage of the selected accessories to be repaired according to a pre-constructed storage network based on the accessory information of the accessories to be repaired and the selected accessories to be repaired; or determining accessory combination information based on accessory information of the necessary accessories and the selected accessories, and determining the target storage of the accessory combination according to a pre-constructed storage network.

8. The method of claim 6, wherein estimating logistics time based on the information of the back-up stocker and the information of the standby accessories comprises:

determining logistics time corresponding to each spare warehouse according to at least one dimension of logistics capacity parameters, distances between warehouse positions and maintenance units, accessory volumes and transportation difficulty levels;

determining target storage corresponding to the spare accessories from the storage units through a pre-constructed self-adaptive distribution model based on the logistics time, the priority of the spare accessories and the conveying difficulty parameters, and comprising the following steps of:

inputting the transportation difficulty parameter corresponding to the spare part, the logistics time corresponding to each spare warehouse and the priority of the spare part into the self-adaptive distribution model based on the self-adaptive distribution model obtained based on decision tree training in advance, and outputting the loss function value corresponding to each spare warehouse;

and determining a storage identifier corresponding to the minimum loss function value, and taking the storage corresponding to the storage identifier as the target storage.

9. The method of claim 8, further comprising:

acquiring warehousing information corresponding to each warehouse and distribution information corresponding to distribution points, wherein the warehousing information and the distribution information comprise position information and distribution cost information;

constructing a decision tree based on the warehousing information and the delivery information, wherein nodes in the decision tree are used for representing warehousing and delivery points;

and training the decision tree based on the accessory information and the delivery information in the historical order information to obtain the self-adaptive delivery model.

10. A fourth big-data-based steam distribution logistics platform, comprising:

the matching unit is used for determining target storage corresponding to the spare parts in a storage unit through a self-adaptive distribution model constructed in advance based on the identifier of the spare parts and the corresponding priority of the spare parts;