A fourth party auto parts logistics platform based on big data and a management method thereof

Abstract

Embodiments of the present application provide a fourth-party auto parts logistics platform and management method based on big data. The management method comprises: building an auto parts chain among repair units, parts suppliers, transaction platforms and distribution units; after a repair unit triggers a repair demand, analyzing the repair demand and determining the parts to be used and their corresponding priorities; then, based on the above information, matching in the storage information of the storage unit through an adaptive distribution model constructed in advance based on a decision tree to determine the target storage corresponding to the parts to be used, and placing an order in the target storage; based on the priority, calling the logistics resources of the distribution unit for distribution, and synchronizing the parts demand information to the parts supplier while coordinating the processing of other procedures. The technical solution of the embodiments of the present application improves the delivery efficiency of auto parts.

Description

[0001] This application is a divisional application of the invention application filed on September 2, 2022, with Chinese application number 202211069107.2 and entitled "A Fourth-Party Auto Parts Logistics Platform and Management Method Based on Big Data". Technical Field

[0002] This application relates to the field of computer technology, and more specifically, to a big data-based fourth-party auto parts logistics platform, management method, computer-readable medium, and electronic device. Background Technology

[0003] The development of the entire auto parts industry is inseparable from logistics; in fact, they are mutually reinforcing. The auto parts supply chain is essentially a combination of commerce and logistics. A key challenge in the automotive aftermarket is the auto parts logistics and distribution sector. It's difficult to establish a logistics and distribution solution that can meet the delivery needs of most customers in a short period, resulting in low delivery efficiency. Summary of the Invention

[0004] The embodiments of this application provide a big data-based fourth-party auto parts logistics platform, management method, computer-readable medium, and electronic device, which can establish an online fourth-party auto parts logistics platform and improve the delivery efficiency of auto parts.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of the embodiments of this application, a fourth-party auto parts logistics management method based on big data is provided, comprising: constructing an auto parts chain based on a cloud platform among repair shops, parts suppliers, trading platforms, and delivery units; after a repair shop triggers a repair request, analyzing the repair request to determine the identifier of the parts to be used and the priority corresponding to the parts; based on the identifier of the parts to be used and its corresponding priority, determining the target warehouse corresponding to the parts to be used in the warehousing units through an adaptive delivery model pre-built based on a decision tree, the repair shop establishing an order flow from the parts supplier through the trading platform, and the parts supplier establishing logistics to the repair shop; generating an order based on the identifier of the parts to be used and placing the order to the target warehouse; retrieving the parts to be used from the target warehouse, calling the logistics resources of the delivery unit for delivery based on the priority, and tracking the delivery progress to generate logistics information.

[0007] In some embodiments of this application, based on the aforementioned scheme, the repair needs include a first category of repair needs and a second category of repair needs. The first category of repair needs refers to repair information where the types and quantities of parts are determined, while the second category of repair needs refers to repair information where the types and quantities of parts are uncertain. Before determining the identifier of the parts to be used and the priority corresponding to the parts to be used, the method further includes: determining whether it belongs to the first category of repair needs or the second category of repair needs; if it is the first category of repair needs, then obtaining the identifier of each part; if it is the second category of repair needs, then matching the mandatory parts and optional parts according to the fuzzy recognition algorithm.

[0008] In some embodiments of this application, based on the aforementioned scheme, the second type of repair need is a vehicle collision repair need. If it is a second type of repair need, then according to the fuzzy recognition algorithm, the mandatory repair parts and optional repair parts are matched, including: acquiring multiple images of the vehicle, recognizing the images, determining the vehicle model and the fault area according to the fuzzy recognition algorithm; determining the mandatory repair parts missing in the fault area, and matching optional repair parts according to the vehicle model.

[0009] In some embodiments of this application, based on the foregoing scheme, the step of analyzing the maintenance requirements and determining the identifiers of spare parts and their corresponding priorities includes: identifying the text content corresponding to each text identifier in the maintenance requirements, and determining an identifier field containing the part identifier; using the identifier field as the identifier of the spare part; determining the part information of the spare part from the parts library, wherein the part information includes quantity information, cost information, production data, and storage information; quantifying the part information to obtain quantified information; determining the priority parameters of the spare part based on the quantified information; and determining the priority of the spare part based on the priority parameters.

[0010] In some embodiments of this application, based on the aforementioned scheme, the step of quantifying the component information to obtain quantified information includes: obtaining production extreme value information corresponding to the production data in the component information; quantifying the component information based on the difference between the production extreme value information and the production data to determine the quantified information corresponding to the production data.

[0011] In some embodiments of this application, based on the aforementioned scheme, determining the target warehouse corresponding to the spare parts in the warehousing unit using an adaptive delivery model pre-built based on a decision tree, based on the spare parts identifier and its corresponding priority, includes: determining at least one backup warehouse matching the spare parts identifier from the warehousing information of the warehousing unit; estimating the logistics time based on the information of the backup warehouse and the information of the spare parts; and determining the target warehouse corresponding to the spare parts from the warehousing unit using the pre-built adaptive delivery model based on the logistics time, the priority of the spare parts, and the transportation difficulty parameter.

[0012] In some embodiments of this application, based on the aforementioned scheme, determining at least one backup warehouse matching the identifier of the spare parts to be used from the warehouse information of the warehouse unit includes: determining the target warehouse for the mandatory spare parts and the target warehouse for the optional spare parts respectively based on the spare parts information of the mandatory spare parts and the optional spare parts, according to a pre-built warehouse network; or, determining spare parts combination information based on the spare parts information of the mandatory spare parts and the optional spare parts, and determining the target warehouse for the spare parts combination according to a pre-built warehouse network.

[0013] In some embodiments of this application, based on the aforementioned scheme, logistics time is estimated based on the information of the backup warehouse and the information of the spare parts, including: determining the logistics time corresponding to each backup warehouse according to at least one dimension of logistics capacity parameters, distance between the warehouse location and the repair unit, spare parts volume, and transportation difficulty level.

[0014] Based on the logistics time, priority of the spare parts, and transportation difficulty parameters, a pre-built adaptive delivery model is used to determine the target warehouse corresponding to the spare parts from the warehousing units. This includes: based on the adaptive delivery model pre-trained using a decision tree, inputting the transportation difficulty parameters corresponding to the spare parts, the logistics time corresponding to each backup warehouse, and the priority of the spare parts into the adaptive delivery model, and outputting the loss function value corresponding to each backup warehouse; determining the warehouse identifier corresponding to the minimum loss function value, and using the warehouse identifier corresponding to the warehouse as the target warehouse.

[0015] In some embodiments of this application, based on the foregoing scheme, the method further includes: if the warehousing information of the warehousing unit does not match the accessory information corresponding to the accessory identifier to be used, then the accessory requirement is sent to the corresponding accessory supplier; and the production scheduling receipt returned by the accessory supplier is obtained.

[0016] In some embodiments of this application, based on the aforementioned scheme, after retrieving and distributing parts from the target warehouse and tracking the delivery progress to generate logistics information, the method further includes: synchronizing the logistics information to the repair unit and the target warehouse; and synchronizing the parts delivery completion information to the auto parts chain when the repair unit triggers the receipt information.

[0017] In some embodiments of this application, based on the foregoing scheme, the method further includes: generating blockchain nodes corresponding to repair shops, parts suppliers, trading platforms, and delivery units based on blockchain technology; processing auto parts repair orders among the blockchain nodes according to the blockchain consensus mechanism; recording auto parts repair orders in different states and synchronizing them to other blockchain nodes.

[0018] In some embodiments of this application, based on the foregoing scheme, the method further includes: when the logistics information indicates receipt, triggering the transfer information corresponding to the repair parts; sending the transfer information to the repair unit to pay for the repair parts; and triggering the parts transaction completion information upon receiving payment completion information sent by the repair unit and payment receipt information sent by the warehousing unit.

[0019] According to one aspect of the embodiments of this application, a fourth-party auto parts logistics platform based on big data is provided, comprising:

[0020] The cloud platform unit is used to build a cloud-based auto parts supply chain among repair shops, parts suppliers, trading platforms, and delivery companies.

[0021] The analysis unit is used to analyze the maintenance request after the maintenance unit triggers the maintenance request, and to determine the identifier of the spare parts and the priority of the spare parts.

[0022] The matching unit is used to determine the target warehouse corresponding to the spare parts in the warehousing unit based on the spare parts identifier and its corresponding priority, through an adaptive delivery model pre-built based on a decision tree.

[0023] An order unit is used to generate an order based on the identifier of the spare parts to be used, and to place the order to the target warehouse.

[0024] The logistics unit is used to extract the spare parts to be used from the target warehouse, call the logistics resources of the delivery unit for delivery based on the priority, and track the delivery progress to generate logistics information.

[0025] In some embodiments of this application, based on the foregoing scheme, the step of analyzing the maintenance requirements and determining the identifiers of spare parts and their corresponding priorities includes: identifying the text content corresponding to each text identifier in the maintenance requirements, and determining an identifier field containing the part identifier; using the identifier field as the identifier of the spare part; determining the part information of the spare part from the parts library, wherein the part information includes quantity information, cost information, production data, and storage information; quantifying the part information to obtain quantified information; determining the priority parameters of the spare part based on the quantified information; and determining the priority of the spare part based on the priority parameters.

[0026] In some embodiments of this application, based on the aforementioned scheme, the step of quantifying the component information to obtain quantified information includes: obtaining production extreme value information corresponding to the production data in the component information; quantifying the component information based on the difference between the production extreme value information and the production data to determine the quantified information corresponding to the production data.

[0027] In some embodiments of this application, based on the aforementioned scheme, determining the target warehouse corresponding to the spare parts in the warehousing unit using an adaptive delivery model pre-built based on a decision tree, based on the spare parts identifier and its corresponding priority, includes: determining at least one backup warehouse matching the spare parts identifier from the warehousing information of the warehousing unit; estimating the logistics time based on the information of the backup warehouse and the information of the spare parts; and determining the target warehouse corresponding to the spare parts from the warehousing unit using the pre-built adaptive delivery model based on the logistics time, the priority of the spare parts, and the transportation difficulty parameter.

[0028] In some embodiments of this application, based on the aforementioned scheme, determining at least one backup warehouse matching the identifier of the spare parts to be used from the warehouse information of the warehouse unit includes: determining the target warehouse for the mandatory spare parts and the target warehouse for the optional spare parts respectively based on the spare parts information of the mandatory spare parts and the optional spare parts, according to a pre-built warehouse network; or, determining spare parts combination information based on the spare parts information of the mandatory spare parts and the optional spare parts, and determining the target warehouse for the spare parts combination according to a pre-built warehouse network.

[0029] In some embodiments of this application, based on the aforementioned scheme, logistics time is estimated based on the information of the backup warehouse and the information of the spare parts, including: determining the logistics time corresponding to each backup warehouse according to at least one dimension of logistics capacity parameters, distance between the warehouse location and the repair unit, spare parts volume, and transportation difficulty level.

[0030] Based on the logistics time, priority of the spare parts, and transportation difficulty parameters, a pre-built adaptive delivery model is used to determine the target warehouse corresponding to the spare parts from the warehousing units. This includes: based on the adaptive delivery model pre-trained using a decision tree, inputting the transportation difficulty parameters corresponding to the spare parts, the logistics time corresponding to each backup warehouse, and the priority of the spare parts into the adaptive delivery model, and outputting the loss function value corresponding to each backup warehouse; determining the warehouse identifier corresponding to the minimum loss function value, and using the warehouse identifier corresponding to the warehouse as the target warehouse.

[0031] In some embodiments of this application, based on the aforementioned scheme, the repair needs include a first category of repair needs and a second category of repair needs. The first category of repair needs refers to repair information where the types and quantities of parts are determined, while the second category of repair needs refers to repair information where the types and quantities of parts are uncertain. Before determining the identifier of the parts to be used and the priority corresponding to the parts to be used, the method further includes: determining whether it belongs to the first category of repair needs or the second category of repair needs; if it is the first category of repair needs, then obtaining the identifier of each part; if it is the second category of repair needs, then matching the mandatory parts and optional parts according to the fuzzy recognition algorithm.

[0032] In some embodiments of this application, based on the aforementioned scheme, the second type of repair need is a vehicle collision repair need. If it is a second type of repair need, then according to the fuzzy recognition algorithm, the mandatory repair parts and optional repair parts are matched, including: acquiring multiple images of the vehicle, recognizing the images, determining the vehicle model and the fault area according to the fuzzy recognition algorithm; determining the mandatory repair parts missing in the fault area, and matching optional repair parts according to the vehicle model.

[0033] In some embodiments of this application, based on the aforementioned scheme, after retrieving and distributing parts from the target warehouse and tracking the delivery progress to generate logistics information, the method further includes: synchronizing the logistics information to the repair unit and the target warehouse; and synchronizing the parts delivery completion information to the auto parts chain when the repair unit triggers the receipt information.

[0034] In some embodiments of this application, based on the aforementioned scheme, the method further includes: generating blockchain nodes corresponding to repair shops, parts suppliers, trading platforms, and delivery units based on blockchain technology; processing auto parts repair orders among the blockchain nodes according to the blockchain consensus mechanism; recording auto parts repair orders in different states and synchronizing them to other blockchain nodes.

[0035] In some embodiments of this application, based on the foregoing scheme, the method further includes: when the logistics information indicates receipt, triggering the transfer information corresponding to the repair parts; sending the transfer information to the repair unit to pay for the repair parts; and triggering the parts transaction completion information upon receiving payment completion information from the repair unit and receipt information from the warehousing unit.

[0036] In some embodiments of this application, based on the foregoing scheme, the system further includes: a sending unit, configured to send the part requirement to the corresponding part supplier if the warehousing information of the warehousing unit does not match the part information corresponding to the part identifier to be used; and an acquisition unit, configured to acquire the production scheduling receipt returned by the part supplier.

[0037] According to one aspect of the embodiments of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the big data-based fourth-party auto parts logistics management method as described in the above embodiments.

[0038] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and 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 the big data-based fourth-party auto parts logistics management method as described in the above embodiments.

[0039] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the big data-based fourth-party auto parts logistics management method provided in the various optional implementations described above.

[0040] In some embodiments of this application, an auto parts chain is constructed among repair shops, parts suppliers, trading platforms, and delivery units. After a repair shop triggers a repair request, the request is analyzed to determine the parts to be used and their corresponding priorities. Then, based on the identifiers of the parts to be used and their corresponding priorities, an adaptive delivery model based on a decision tree is used to determine the target warehouse corresponding to the parts to be used in the warehousing units, and the order is placed in the target warehouse. The logistics resources of the delivery unit are called for delivery based on the priority, and the parts demand information is synchronized to the parts supplier. At the same time, other process procedures are coordinated. The technical solutions of this application improve the delivery efficiency of auto parts.

[0041] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0043] Figure 1 A flowchart illustrating a big data-based fourth-party auto parts logistics management method according to an embodiment of this application is shown;

[0044] Figure 2 A schematic diagram of an auto parts supply chain according to an embodiment of this application is shown;

[0045] Figure 3 A flowchart illustrating the determination of repair needs according to one embodiment of this application is shown;

[0046] Figure 4 A flowchart illustrating the determination of available accessory information according to an embodiment of this application is shown;

[0047] Figure 5A schematic diagram of a big data-based fourth-party auto parts logistics platform according to an embodiment of this application is shown;

[0048] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0049] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0050] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods and systems, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0051] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0052] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0053] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0054] Figure 1 A flowchart illustrating a big data-based fourth-party auto parts logistics management method according to an embodiment of this application is shown. (Refer to...) Figure 1 As shown, this big data-based fourth-party auto parts logistics management method includes at least steps S110 to S150, which are detailed below:

[0055] In step S110, a cloud-based auto parts supply chain is established among repair shops, parts suppliers, trading platforms, and delivery units.

[0056] In one embodiment of this application, the entire automotive repair process involves multiple institutions or entities, such as repair shops, parts suppliers, trading platforms, and delivery companies. Therefore, this embodiment constructs a cloud-based auto parts supply chain among these institutions or entities to coordinate and process multiple business processes within a single system.

[0057] like Figure 2 As shown, this embodiment centers on warehousing and constructs network ① based on a remote low-frequency trading network and network ②-1 based on a local high-frequency trading network. The two networks are linked by trunk logistics. Then, a trading network ②-2 centered on the city's auto parts market is constructed, along with a merchant supply service network ③ and a network ④ from repair shops to consumers (car owners). Through the collaborative connection between these networks, an auto parts supply chain is realized, connecting repair shops, parts suppliers, trading platforms, and delivery units.

[0058] Optionally, in this embodiment, there can be two or more institutions (or units) of different types mentioned above to ensure the normal operation of business.

[0059] In this embodiment, the cloud platform constructs a data module based on information from repair shops, parts suppliers, and the trading platform; and a trading module built using machine learning technology in artificial intelligence. This trading module is used for transaction matching, intelligent pricing, and on-demand service allocation. Based on industry data and historical transaction data accumulated by the platform, the cloud platform in this embodiment utilizes machine learning technology in artificial intelligence to construct a transaction matching model and algorithm for the auto parts industry. This achieves automatic transaction matching, intelligent pricing, and on-demand service allocation functions, and significantly improves transaction efficiency through continuous self-optimization and improvement.

[0060] In one embodiment of this application, the method further includes:

[0061] Based on blockchain technology, blockchain nodes are generated for repair shops, parts suppliers, trading platforms, and delivery units respectively.

[0062] Auto parts repair orders are processed and transferred between the blockchain nodes according to the blockchain consensus mechanism.

[0063] Auto parts repair orders in different states are recorded and synchronized to other blockchain nodes.

[0064] Specifically, this embodiment uses blockchain technology, designating repair shops, parts suppliers, trading platforms, and delivery companies as blockchain nodes to construct an auto parts system based on a blockchain network. By processing auto parts repair orders through the blockchain network, and recording orders in different states, the system synchronizes these records to other blockchain nodes. This approach achieves the goals of data immutability and decentralization. Based on these two characteristics, the information recorded by the blockchain is more authentic and reliable.

[0065] In one embodiment of this application, the repair requirements include a first category of repair requirements and a second category of repair requirements. The first category of repair requirements refers to repair information where the types and quantities of parts are determined, while the second category of repair requirements refers to repair information where the types and quantities of parts are uncertain. In this case, parts quantification is required to complete the repair.

[0066] refer to Figure 3 This document illustrates a flowchart of determining the type of repair need according to an embodiment of this application. In one embodiment of this application, before determining the identifier of the spare parts and the priority corresponding to the spare parts, the process further includes:

[0067] Step S210: Determine whether the repair need belongs to the first category or the second category; if it is the first category, proceed to step S220; if it is the second category, proceed to step S230.

[0068] Step S220: Obtain the identification of each component;

[0069] Step S230: Match the required and optional parts according to the fuzzy recognition algorithm.

[0070] In this embodiment, after obtaining the repair request, a judgment is first made to determine whether the repair request belongs to the first type of repair request or the second type of repair request. If it is the first type of repair request, it means that there is clear parts information, so the identifier of each part is obtained; if it is the second type of repair request, the mandatory parts and optional parts are matched according to the fuzzy recognition algorithm.

[0071] For example, if the second type of repair need is a post-collision repair need, multiple images of the vehicle are acquired, the images are identified, and the vehicle model and fault area are determined according to the fuzzy recognition algorithm. The essential repair parts missing in the fault area are determined, and optional repair parts are matched according to the vehicle model. Specifically, the fuzzy recognition algorithm in this embodiment can be a fuzzy algorithm based on the Laplacian operator. Multiple images corresponding to the same location on the vehicle are acquired, grayscale processing is performed on the multiple images to obtain grayscale images, then a Laplacian transformation is performed on the grayscale images, and the variance of the transformed images is calculated. Then, based on a set threshold, the clarity of the image is judged, and clear images are segmented to determine the vehicle model and fault area. Then, based on the vehicle model, fault area, and the original fault image corresponding to the vehicle model, the exploded view of the fault area is restored, and residual or intact parts are deleted to obtain the missing parts. Simultaneously, the missing parts in the fault area are designated as essential repair parts, and the parts matched according to the vehicle model are designated as optional repair parts.

[0072] In step S120, after the repair unit triggers a repair request, the repair request is analyzed to determine the identifier of the spare parts to be used and the priority of the spare parts to be used.

[0073] In one embodiment of this application, after the repair unit triggers a repair request, the text in the repair request is analyzed to extract the identifier of the spare parts to be used, and the priority of the spare parts to be used is determined, so as to allocate the spare parts based on the above information.

[0074] Figure 4 A flowchart illustrating the determination of available accessory information according to one embodiment of this application is shown. In one embodiment of this application, such as... Figure 4 As shown, the maintenance needs are analyzed to determine the identifiers of spare parts and their corresponding priorities, including steps S310 to S360:

[0075] S310, Identify the text content corresponding to each text identifier in the repair request, and determine the identifier field containing the part identifier;

[0076] S320, the identification field is used as the identifier of the spare part;

[0077] S330, determine the part information of the parts to be used from the parts library, wherein the part information includes quantity information, cost information, production data and storage information;

[0078] S340, quantify the accessory information to obtain quantified information;

[0079] S350, determine the priority parameters of the spare parts to be used based on the quantification information;

[0080] S360, determine the priority of the spare part based on the priority parameter.

[0081] Specifically, in this embodiment, OCR technology can be used to identify the text content corresponding to each text identifier in the repair request, and determine the identifier field containing the part identifier as the identifier of the part to be used. Then, the part information of the part to be used is determined from the part library. In this embodiment, the part information may include the current quantity information of the part, cost information indicating the unit price, production data indicating the production efficiency per unit time, and storage information of the part by each warehousing unit. After obtaining the part information, the part information is quantified to obtain quantified information. Then, based on the quantified information, priority parameters and priorities are determined to comprehensively consider the condition of the part.

[0082] In one embodiment of this application, step S340 quantifies the accessory information to obtain quantified information, including:

[0083] Obtain the production extreme value information corresponding to the production data in the component information;

[0084] The accessory information is quantified based on the difference between the production extreme value information and the production data to determine the quantified information corresponding to the production data.

[0085] In one embodiment of this application, the production data can be the production quantity per unit time. This embodiment obtains the production extreme value information corresponding to the production data in the component information, wherein the production extreme value information can include the minimum production value Pro_min and the maximum production value Pro_max per unit time. Based on the first difference Dir_fir between the minimum production value and the daily production data Pro_cur, and the second difference Dir_sec between the maximum production value and the daily production data, the quantified production data Qua_pro corresponding to the production data is determined as follows:

[0086]

[0087] Here, α represents the production factor. The above process quantifies production data to obtain accurate quantitative production data, which is used as one of the factors to measure the scarcity of a component by analyzing its production information.

[0088] In addition, this embodiment can also quantify information by using quantity information, cost information, and storage information to obtain quantitative information. Then, the scarcity of a certain component can be measured using the quantitative information, and the priority of a component can be determined by the scarcity level. For example, the scarcer a component is, the higher its priority.

[0089] In one embodiment of this application, based on the quantification information, which may include quantification quantity information Qua_mon, quantification cost information Qua_cos, quantification production data Qua_pro, and quantification storage information Qua_sto, the priority parameter Par_pro of the spare part to be used is determined as follows:

[0090] Par_pro=log2(β·Qua_mon+γ·Qua_sto)+log2(Qua_pro+θ·Qua_cos)

[0091] Where β, γ, and θ represent the quantity factor, storage factor, and cost factor, respectively. The above method comprehensively processes various component information to obtain priority parameters for each component, which are then used to measure the importance of the component.

[0092] In this embodiment, after determining the priority parameters, the priority level Par_poi of the priority parameters is determined based on the priority threshold range corresponding to each priority level. Optionally, in this embodiment, there can be three or more priority levels. The higher the level, the more important or scarce the parts are, requiring close cooperation between warehousing and logistics to complete the retrieval of parts in a short time.

[0093] In step S130, based on the identifier of the spare parts to be used and its corresponding priority, the target warehouse corresponding to the spare parts to be used is determined in the warehouse unit through an adaptive delivery model pre-built based on a decision tree.

[0094] In one embodiment of this application, after determining the identifier of the spare parts to be used and its priority, the latest storage information of each storage unit is obtained, and the storage information is matched to determine the target storage that matches the spare parts to be used and its priority.

[0095] In one embodiment of this application, step S130, which determines the target warehouse corresponding to the parts to be used in the warehouse unit by using an adaptive delivery model pre-built based on a decision tree, includes:

[0096] Identify at least one backup warehouse that matches the identifier of the spare parts to be used from the warehouse unit's warehouse information;

[0097] Estimating logistics time based on the information on the backup storage and the information on spare parts;

[0098] Based on the logistics time, the priority of the spare parts to be used, and the transportation difficulty parameters, the target warehouse corresponding to the spare parts to be used is determined from the preset storage units through a pre-built adaptive delivery model.

[0099] Specifically, in this embodiment, a simple match is first performed between warehouse information and the identifier of the spare parts to determine the backup warehouses storing the spare parts. It should be noted that the number of backup warehouses in this embodiment can be one, two, or more. The backup warehouses storing the spare parts can be determined by text matching, so that the target warehouse and distribution station with the lowest logistics cost and the highest logistics efficiency can be selected from these backup warehouses for delivery.

[0100] Optionally, in this embodiment, when determining the backup warehouse, the accessory information is first searched according to the pre-built warehouse network based on the accessory information of the mandatory and optional parts, and the target warehouses of the mandatory and optional parts are determined respectively; or, based on the accessory information of the mandatory and optional parts, accessory combination information is determined, for example, a string composed of accessory identifiers is generated, and then the string information is searched according to the pre-built warehouse network to determine the target warehouse of the accessory combination.

[0101] However, due to varying conditions at different warehousing units, such as distance, logistics, or warehousing quality, the availability of spare parts can be affected. Therefore, in this embodiment, logistics time is estimated based on information from backup warehousing and spare parts, and a suitable target warehouse is matched based on the priority of logistics time and spare parts.

[0102] In this embodiment, the warehousing information of each warehousing unit can be obtained. By matching the warehousing information with the identifier of the spare parts, the backup warehousing that stores the spare parts is determined. Then, the logistics time is estimated based on the information of the backup warehousing and the information of the spare parts. In this embodiment, the information of the backup warehousing may include the distance between the warehousing location and the repair unit, Par_dit, and the current logistics capacity parameters of the warehousing unit, Par_cap, etc. The information of the spare parts may include the part volume, Par_vol, and the transportation difficulty parameter, Par_tan, etc. Based on the above information, the logistics time Tim_tan corresponding to each backup warehousing is estimated as follows:

[0103]

[0104] in, This represents the transportation factor. In the above process, by taking into account information such as logistics capacity parameters, the distance between the warehouse location and the repair unit, the volume of the parts, and the transportation difficulty into the estimation of logistics time, a smaller logistics time will be obtained when the logistics capacity parameter is higher, the distance is shorter, the volume is smaller, and the transportation difficulty parameter is lower.

[0105] Optionally, in this embodiment, after estimating the logistics time using the above method, the target warehouse corresponding to the spare parts is determined from the warehousing units based on the logistics time, the priority of the spare parts, and the transportation difficulty parameters, using a pre-built adaptive delivery model. For example, if the estimated logistics time for each backup warehouse is relatively short and the priority of the spare parts is relatively high, the backup warehouse with the shorter logistics time is selected as the target warehouse.

[0106] Furthermore, in this embodiment, the warehousing information corresponding to each warehouse and the delivery information corresponding to each delivery point are pre-acquired, wherein both the warehousing information and the delivery information include location information and delivery cost information. Then, based on the warehousing information and the delivery information, an adaptive delivery model is constructed using a decision tree and trained. In this model, each backup warehouse and delivery station serves as a node in the decision tree, with the warehousing information corresponding to the backup warehouse and the delivery station serving as the node information.

[0107] When selecting a delivery strategy, the input information includes the priority Par_poi of the spare parts, the delivery time Tim_tan_i of each site for collaborative delivery, and the transportation difficulty parameter Par_tan_i of the spare parts. The logistics time of each warehouse and delivery site calculated through the above steps is used as the variable information of the decision tree. The loss function is the cost Cot(i) of all warehouses or delivery sites.

[0108]

[0109] in, Let represent the loss factor obtained during training, and 'i' represent the identifier of the warehouse or each delivery station along the logistics route. A total of k delivery stations are needed to transport the parts. This information is input into the adaptive delivery model. When the output loss function is low, the adaptive delivery model can decide which target warehouse or delivery station corresponds to the shortest delivery time and lowest delivery cost. This method determines the loss function based on the delivery difficulty parameters and delivery time corresponding to each station. A higher loss function indicates higher delivery costs, thus avoiding that method of delivery. A lower loss function indicates lower delivery costs, allowing the target warehouse in this decision to be used to retrieve the part, and the corresponding delivery station to be used for delivery, thereby reducing the cost of part transportation while ensuring delivery efficiency.

[0110] Optionally, there can be multiple target warehouses, which are used to send parts to the repair unit through different target warehouses. This allows for comparison of parts quality to select the best one for processing, or for storing parts for direct use when needed later. This improves the efficiency of parts transportation and use.

[0111] In one embodiment of this application, the method further includes:

[0112] If the warehousing information of the warehousing unit does not match the accessory information corresponding to the accessory identifier to be used, the accessory request will be sent to the corresponding accessory supplier.

[0113] Obtain the production scheduling receipt returned by the component supplier.

[0114] Specifically, if no matching warehouse information is available for the requested spare part, meaning none of the warehouse units currently hold the spare part, the spare part request needs to be sent to the corresponding spare part supplier to retrieve the spare part. The spare part supplier then returns a production scheduling receipt, notifying the requester of the spare part's production progress and timeline. This process ensures the reliability and efficiency of spare part retrieval.

[0115] In step S140, an order is generated based on the identifier of the spare parts to be used, and the order is placed in the target warehouse; the repair unit establishes an order flow from the spare parts supplier through the transaction platform, and the spare parts supplier establishes logistics to the repair unit.

[0116] In one embodiment of this application, after the target warehouse is determined, an order is generated based on the identifier of the spare parts to be used, and the order is sent to the target warehouse, so that the repair unit can establish an order flow from the spare parts supplier through the transaction platform and establish logistics from the spare parts supplier to the repair unit.

[0117] In this embodiment, orders can be generated using an order template, which may include information such as part identification, repair shop address, and part price.

[0118] In step S150, the spare parts to be used are extracted from the target warehouse, the logistics resources of the delivery unit are called for delivery based on the priority, and the delivery progress is tracked to generate logistics information.

[0119] In one embodiment of this application, after the target warehouse is determined, the spare part is retrieved from the target warehouse. The corresponding logistics resources are then determined based on priority for delivery. In this embodiment, delivery units can be tiered according to logistics speed, with higher speeds resulting in higher tiers. Higher-priority spare parts are then assigned to higher-tier delivery units to improve the utilization rate of logistics resources. During transportation, the delivery progress is tracked, and logistics information is generated and synchronized to the repair unit and the warehouse unit to ensure the reliability and safety of the transportation process.

[0120] It should be noted that the logistics resources in this embodiment may include third-party logistics and self-operated logistics, preferably with third-party logistics accounting for the majority. The method in this embodiment is implemented based on a fourth-party auto parts logistics platform. The fourth-party auto parts logistics platform refers to a platform that completes intelligent auto parts delivery based on the auto parts chain between repair shops, parts suppliers, trading platforms, and delivery units.

[0121] In some embodiments of this application, an auto parts chain is constructed among repair shops, parts suppliers, trading platforms, and delivery units. After a repair shop triggers a repair request, the request is analyzed to determine the parts to be used and their corresponding priorities. Then, based on the identifiers of the parts to be used and their corresponding priorities, an adaptive delivery model based on a decision tree is used to determine the target warehouse corresponding to the parts to be used in the warehousing units, and the order is placed in the target warehouse. The logistics resources of the delivery unit are called for delivery based on the priority, and the parts demand information is synchronized to the parts supplier. At the same time, other process procedures are coordinated. The technical solutions of this application improve the delivery efficiency of auto parts.

[0122] In one embodiment of this application, after retrieving and distributing parts from the target warehouse and generating logistics information by tracking the delivery progress, the method further includes:

[0123] The logistics information is synchronized to the repair unit and the target warehouse;

[0124] Upon receiving the receipt information triggered by the repair shop, the parts delivery completion information is simultaneously transmitted to the auto parts supply chain.

[0125] Specifically, in this embodiment, logistics information is generated and updated in real time during transportation, and this information is synchronized to the repair unit and the target warehouse. Upon receiving the receipt information triggered by the repair unit, parts delivery completion information is generated and synchronized to the auto parts supply chain.

[0126] In one embodiment of this application, the method further includes:

[0127] When the logistics information indicates that the item has been signed for, the corresponding payment information for the repair parts is triggered.

[0128] The transfer information is sent to the repair shop to pay for the repair parts;

[0129] Upon receiving payment completion information from the repair shop and payment receipt information from the warehousing shop, the parts transaction completion information is triggered.

[0130] Specifically, when the logistics information indicates receipt, payment information for the repair parts is generated and sent to the repair shop to notify them to pay for the parts. Subsequently, upon receiving payment completion information from the repair shop and receipt information from the warehousing unit, the parts transaction is completed. This process ensures the safe receipt and disbursement of order funds, improving transaction security and reliability.

[0131] The following describes an embodiment of the apparatus described in this application, which can be used to execute the big data-based fourth-party auto parts logistics management method in the above embodiments of this application. It is understood that the apparatus can be a computer program (including program code) running on a computer device, for example, the apparatus is an application software; the apparatus can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the big data-based fourth-party auto parts logistics management method described above in this application.

[0132] This corresponds one-to-one with the above-mentioned implementation examples of the fourth-party auto parts logistics management method based on big data. Figure 5 A block diagram of a big data-based fourth-party auto parts logistics platform according to an embodiment of this application is shown.

[0133] Reference Figure 5 As shown, a big data-based fourth-party auto parts logistics platform 300 according to an embodiment of this application includes:

[0134] Cloud platform unit 410 is used to build a cloud-based auto parts supply chain among repair shops, parts suppliers, trading platforms and delivery units;

[0135] Analysis unit 420 is used to analyze the maintenance request after the maintenance unit triggers the maintenance request, and determine the identifier of the spare parts and the priority of the spare parts.

[0136] The matching unit 430 is used to determine the target warehouse corresponding to the spare parts in the warehousing unit based on the spare parts identifier and its corresponding priority, through an adaptive delivery model pre-built based on a decision tree.

[0137] Order unit 440 is used to generate an order based on the identifier of the spare parts to be used, and to place the order to the target warehouse;

[0138] The logistics unit 450 is used to extract the spare parts from the target warehouse, call the logistics resources of the delivery unit for delivery based on the priority, and track the delivery progress to generate logistics information.

[0139] In some embodiments of this application, based on the foregoing scheme, the step of analyzing the maintenance requirements and determining the identifiers of spare parts and their corresponding priorities includes: identifying the text content corresponding to each text identifier in the maintenance requirements, and determining an identifier field containing the part identifier; using the identifier field as the identifier of the spare part; determining the part information of the spare part from the parts library, wherein the part information includes quantity information, cost information, production data, and storage information; quantifying the part information to obtain quantified information; determining the priority parameters of the spare part based on the quantified information; and determining the priority of the spare part based on the priority parameters.

[0140] In some embodiments of this application, based on the foregoing scheme, the step of quantifying the component information to obtain quantified information includes: obtaining production extreme value information corresponding to the production data in the component information; quantifying the component information based on the difference between the production extreme value information and the production data to determine the quantified information corresponding to the production data.

[0141] In some embodiments of this application, based on the aforementioned scheme, determining the target warehouse corresponding to the spare parts in the warehousing unit using an adaptive delivery model pre-built based on a decision tree, based on the spare parts identifier and its corresponding priority, includes: determining at least one backup warehouse matching the spare parts identifier from the warehousing information of the warehousing unit; estimating the logistics time based on the information of the backup warehouse and the information of the spare parts; and determining the target warehouse corresponding to the spare parts from the warehousing unit using the pre-built adaptive delivery model based on the logistics time, the priority of the spare parts, and the transportation difficulty parameter.

[0142] In some embodiments of this application, based on the aforementioned scheme, determining at least one backup warehouse matching the identifier of the spare parts to be used from the warehouse information of the warehouse unit includes: determining the target warehouse for the mandatory spare parts and the target warehouse for the optional spare parts respectively based on the spare parts information of the mandatory spare parts and the optional spare parts, according to a pre-built warehouse network; or, determining spare parts combination information based on the spare parts information of the mandatory spare parts and the optional spare parts, and determining the target warehouse for the spare parts combination according to a pre-built warehouse network.

[0143] In some embodiments of this application, based on the aforementioned scheme, logistics time is estimated based on the information of the backup warehouse and the information of the spare parts, including: determining the logistics time corresponding to each backup warehouse according to at least one dimension of logistics capacity parameters, distance between the warehouse location and the repair unit, spare parts volume, and transportation difficulty level.

[0144] Based on the logistics time, priority of the spare parts, and transportation difficulty parameters, a pre-built adaptive delivery model is used to determine the target warehouse corresponding to the spare parts from the warehousing units. This includes: based on the adaptive delivery model pre-trained using a decision tree, inputting the transportation difficulty parameters corresponding to the spare parts, the logistics time corresponding to each backup warehouse, and the priority of the spare parts into the adaptive delivery model, and outputting the loss function value corresponding to each backup warehouse; determining the warehouse identifier corresponding to the minimum loss function value, and using the warehouse identifier corresponding to the warehouse as the target warehouse.

[0145] In some embodiments of this application, based on the aforementioned scheme, the repair needs include a first category of repair needs and a second category of repair needs. The first category of repair needs refers to repair information where the types and quantities of parts are determined, while the second category of repair needs refers to repair information where the types and quantities of parts are uncertain. Before determining the identifier of the parts to be used and the priority corresponding to the parts to be used, the method further includes: determining whether it belongs to the first category of repair needs or the second category of repair needs; if it is the first category of repair needs, then obtaining the identifier of each part; if it is the second category of repair needs, then matching the mandatory parts and optional parts according to the fuzzy recognition algorithm.

[0146] In some embodiments of this application, based on the aforementioned scheme, the second type of repair need is a vehicle collision repair need. If it is a second type of repair need, then according to the fuzzy recognition algorithm, the mandatory repair parts and optional repair parts are matched, including: acquiring multiple images of the vehicle, recognizing the images, determining the vehicle model and the fault area according to the fuzzy recognition algorithm; determining the mandatory repair parts missing in the fault area, and matching optional repair parts according to the vehicle model.

[0147] In some embodiments of this application, based on the aforementioned scheme, after retrieving and distributing parts from the target warehouse and tracking the delivery progress to generate logistics information, the method further includes: synchronizing the logistics information to the repair unit and the target warehouse; and synchronizing the parts delivery completion information to the auto parts chain when the repair unit triggers the receipt information.

[0148] In some embodiments of this application, based on the aforementioned scheme, the method further includes: generating blockchain nodes corresponding to repair shops, parts suppliers, trading platforms, and delivery units based on blockchain technology; processing auto parts repair orders among the blockchain nodes according to the blockchain consensus mechanism; recording auto parts repair orders in different states and synchronizing them to other blockchain nodes.

[0149] In some embodiments of this application, based on the foregoing scheme, the method further includes: when the logistics information indicates receipt, triggering the transfer information corresponding to the repair parts; sending the transfer information to the repair unit to pay for the repair parts; and triggering the parts transaction completion information upon receiving payment completion information from the repair unit and receipt information from the warehousing unit.

[0150] In some embodiments of this application, based on the foregoing scheme, the system further includes: a sending unit, configured to send the part requirement to the corresponding part supplier if the warehousing information of the warehousing unit does not match the part information corresponding to the part identifier to be used; and an acquisition unit, configured to acquire the production scheduling receipt returned by the part supplier.

[0151] In some embodiments of this application, an auto parts chain is constructed among repair shops, parts suppliers, trading platforms, and delivery units. After a repair shop triggers a repair request, the request is analyzed to determine the parts to be used and their corresponding priorities. Then, based on the identifiers of the parts to be used and their corresponding priorities, an adaptive delivery model based on a decision tree is used to determine the target warehouse corresponding to the parts to be used in the warehousing units, and the order is placed in the target warehouse. The logistics resources of the delivery unit are called for delivery based on the priority, and the parts demand information is synchronized to the parts supplier. At the same time, other process procedures are coordinated. The technical solutions of this application improve the delivery efficiency of auto parts.

[0152] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0153] It should be noted that, Figure 6 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0154] like Figure 6As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage portion 508 into Random Access Memory (RAM) 503, such as performing the methods described in the above embodiments. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0155] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0156] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this application.

[0157] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0158] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0159] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0160] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0161] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0162] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0163] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0164] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0165] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A fourth-party auto parts logistics management method based on big data, characterized in that, include: Build a cloud-based auto parts supply chain among repair shops, parts suppliers, trading platforms, and delivery companies; After a repair unit triggers a repair request, the repair request is analyzed to determine the identifier of the spare parts and the priority of the spare parts. Based on the identifiers of the spare parts to be used and their corresponding priorities, the target warehouses corresponding to the spare parts to be used are determined in the warehousing units through an adaptive delivery model pre-built based on decision trees. An order is generated based on the identifier of the spare parts to be used, and the order is placed in the target warehouse; The repair shop establishes an order flow from the parts supplier through the transaction platform, and the parts supplier establishes a logistics flow to the repair shop; Extract the spare parts to be used from the target warehouse, call the logistics resources of the delivery unit for delivery based on the priority, and track the delivery progress to generate logistics information; The repair needs include a second category of repair needs, which refers to repair needs where the type and quantity of parts in the repair information are uncertain, and the second category of repair needs is repair needs after a vehicle collision; before determining the identifier of the parts to be used and the priority of the parts to be used, if it is a second category of repair needs, the mandatory parts and optional parts are matched according to the fuzzy recognition algorithm. The step of matching mandatory and optional parts according to the fuzzy recognition algorithm includes: Multiple images corresponding to the same location of the vehicle are acquired, and grayscale processing is performed on the multiple images to obtain a grayscale image; The grayscale image is subjected to a Laplacian transformation, and the variance of the transformed image is calculated. Based on a set threshold, the image is judged to be clear. Clear images are segmented to determine the vehicle model and fault area. Based on the vehicle model and the fault area, as well as the original fault diagram corresponding to the vehicle model, the exploded view of the fault area is restored, residual or intact parts are deleted, and the missing parts are obtained. The missing parts in the faulty area are designated as mandatory repair parts, while the parts matched according to the vehicle model are designated as optional repair parts.

2. The method according to claim 1, characterized in that, The repair requirements also include a first category of repair requirements, which refers to the determination of the type and quantity of parts in the repair information; before determining the identifier of the parts to be used and the priority of the parts to be used, it also includes: Determine whether the repair need belongs to the first category or the second category; If it is a Category 1 repair request, obtain the identification of each part.

3. The method according to claim 1, characterized in that, Based on the identifiers of the spare parts to be used and their corresponding priorities, the target warehouses corresponding to the spare parts to be used are determined in the warehousing units through an adaptive delivery model pre-built based on decision trees, including: Identify at least one backup warehouse that matches the identifier of the spare parts to be used from the warehouse unit's warehouse information; Estimating logistics time based on the information on the backup storage and the information on spare parts; Based on the logistics time, priority of the spare parts, and transportation difficulty parameters, the target warehouse corresponding to the spare parts is determined from the warehousing units through a pre-built adaptive delivery model.

4. The method according to claim 3, characterized in that, Identifying at least one backup warehouse from the warehouse information of the warehouse unit that matches the identifier of the spare parts to be used, including: Based on the accessory information of the required and optional parts, the target warehouses for the required and optional parts are determined according to the pre-built warehousing network; or, based on the accessory information of the required and optional parts, accessory combination information is determined, and the target warehouse for the accessory combination is determined according to the pre-built warehousing network.

5. The method according to claim 3, characterized in that, Based on the information on the backup storage and the information on the spare parts, the logistics time is estimated, including: The logistics time for each backup warehouse is determined based on at least one of the following dimensions: logistics capacity parameters, distance between warehouse location and repair unit, spare parts volume, and transportation difficulty level. Based on the logistics time, priority of the spare parts, and transportation difficulty parameters, a pre-built adaptive delivery model is used to determine the target warehouse corresponding to the spare parts from the warehousing units, including: Based on the adaptive delivery model trained in advance using decision trees, the transportation difficulty parameters corresponding to the spare parts, the logistics time corresponding to each backup warehouse, and the priority of the spare parts are input into the adaptive delivery model, and the loss function value corresponding to each backup warehouse is output. The warehouse identifier corresponding to the minimum value of the loss function is determined, and the warehouse corresponding to the warehouse identifier is taken as the target warehouse.

6. The method according to claim 5, characterized in that, The method further includes: Obtain warehousing information for each warehouse and delivery information for each delivery point, wherein the warehousing information and the delivery information both include location information and delivery cost information; Based on the warehousing information and the delivery information, a decision tree is constructed, and the nodes in the decision tree are used to represent warehousing and delivery points; The decision tree is trained based on the accessory information and shipping information in historical order information to obtain the adaptive delivery model.

7. The method according to claim 1, characterized in that, The method further includes: Based on blockchain technology, blockchain nodes are generated for repair shops, parts suppliers, trading platforms, and delivery units respectively. According to the blockchain consensus mechanism, auto parts repair orders are processed and transferred between the blockchain nodes; auto parts repair orders in different states are recorded and synchronized to other blockchain nodes.

8. The method according to claim 1, characterized in that, The method further includes: When the logistics information indicates that the item has been signed for, the corresponding payment information for the repair parts is triggered. The transfer information is sent to the repair shop to pay for the repair parts; Upon receiving payment completion information from the repair shop and payment receipt information from the warehousing shop, the parts transaction completion information is triggered.

9. A fourth-party auto parts logistics platform based on big data, characterized in that, include: The cloud platform unit is used to build a cloud-based auto parts supply chain among repair shops, parts suppliers, trading platforms, and delivery companies. The analysis unit is used to analyze the repair request after the repair unit triggers the repair request, determine the identifier of the spare parts and the priority of the spare parts, wherein the repair request includes a second category of repair request, the second category of repair request refers to the repair information where the type and quantity of spare parts are uncertain, and the second category of repair request is the repair request after the vehicle collision. The matching unit is used to determine the target warehouse corresponding to the spare parts in the warehousing unit based on the spare parts identifier and its corresponding priority, through an adaptive delivery model pre-built based on a decision tree. An order unit is used to generate an order based on the identifier of the spare parts to be used, and to place the order to the target warehouse. The logistics unit is used to extract the spare parts to be used from the target warehouse, call the logistics resources of the delivery unit for delivery based on the priority, and track the delivery progress to generate logistics information; The aforementioned big data-based fourth-party auto parts logistics platform is also used to, before determining the identifier of the parts in need and their corresponding priority, match mandatory and optional parts based on a fuzzy recognition algorithm if the repair request falls under the second category. These parts include: Multiple images corresponding to the same location of the vehicle are acquired, and grayscale processing is performed on the multiple images to obtain a grayscale image; The grayscale image is subjected to a Laplacian transformation, and the variance of the transformed image is calculated. Based on a set threshold, the image is judged to be clear. Clear images are segmented to determine the vehicle model and fault area. Based on the vehicle model and the fault area, as well as the original fault diagram corresponding to the vehicle model, the exploded view of the fault area is restored, residual or intact parts are deleted, and the missing parts are obtained. The missing parts in the faulty area are designated as mandatory repair parts, while the parts matched according to the vehicle model are designated as optional repair parts.

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