Intelligent recommendation methods, devices, equipment and storage media for component logistics attributes

Abstract

This invention discloses a method, apparatus, device, and storage medium for intelligent recommendation of component logistics attributes. The method receives BOM data and, when a missing logistics attribute is detected for a current component, determines whether the current component's current attribute matches a component logistics attribute database. If the current component's attribute does not match the database, it determines whether it matches a component family logistics attribute database. When the current component's attribute does not match the database, a pre-set intelligent recommendation auxiliary program intelligently recommends the current component's attribute. This enables seamless workflow and automatic data retrieval between multiple subsystems in the manufacturing field, avoiding manual data import / export and external processing between systems, thus reducing manpower significantly. It also lowers the data error rate, improves system response speed, and enhances the speed and efficiency of intelligent recommendation of component logistics attributes.

Description

Technical Field

[0001] This invention relates to the field of vehicle manufacturing technology, and in particular to a method, apparatus, equipment, and storage medium for intelligent recommendation of component logistics attributes. Background Technology

[0002] The current industry practice for applying component logistics attributes involves manually generating component logistics attribute data, which is stored in multiple systems without interoperability. Typically, material requirement orders are generated based on production plans, leading to manual requests. For vehicle model material master data, a manual verification of the logistics attribute integrity is initiated. Missing data is manually corrected externally via instant messaging in group chats (usually through Excel spreadsheets, with employees manually matching attributes one by one based on experience) and then uploaded to the database. However, existing technology has the following problems:

[0003] 1. System data is not integrated; data is imported, exported, or processed outside the system across multiple systems.

[0004] 2. The process mainly relies on manual handling, which consumes a lot of manpower and is inefficient. Each production line has 3-4 dedicated personnel to handle this type of data, including assembly process personnel, logistics process personnel, and on-site team leaders.

[0005] 3. High data error rate, with frequent data errors and omissions caused by human factors;

[0006] 4. Errors or omissions in data can only be discovered before it is used. Data processing requires high timeliness. Coupled with the need for manual processing, the actual effect is poor and the response speed is slow, which often leads to delays in the release of inclusion instructions. Summary of the Invention

[0007] The main objective of this invention is to provide a method, apparatus, device, and storage medium for intelligent recommendation of component logistics attributes. This invention aims to solve the technical problems in the application of component logistics attributes in the prior art, such as the lack of system data integration, high manpower consumption, low efficiency, high data error rate, errors or omissions only being discovered before each use, poor actual effect, and slow response speed.

[0008] In a first aspect, the present invention provides a method for intelligent recommendation of logistics attributes of components, the method comprising the following steps:

[0009] Upon receiving BOM data, when it is detected that the logistics attributes of the current component are missing, it is determined whether the current component's current part attributes match the part logistics attribute database.

[0010] When it is detected that the current part attribute does not match the part logistics attribute database, it is determined whether the current part attribute matches the part family logistics attribute library;

[0011] When the current part attributes do not match the part family logistics attribute library, a preset intelligent recommendation assistance program is used to make intelligent recommendations for the current part attributes.

[0012] Optionally, before determining whether the current part attribute matches the part logistics attribute database when the logistics attribute of the current part is missing upon receiving BOM data, the intelligent recommendation method for part logistics attributes further includes:

[0013] Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics, and obtain the material classification results.

[0014] A parts logistics attribute database is constructed based on the logistics attribute data in the material classification results;

[0015] Perform logistics attribute affinity analysis on the material classification results to obtain part family details, and construct a part family logistics attribute database based on the part family details;

[0016] By connecting to the manufacturing subsystem corresponding to the current vehicle production plan through the multi-system interface, a preset verification process for the automated logistics parameters of the manufacturing subsystem is obtained. The automated logistics parameters are verified according to the preset verification process, and logistics data that has been successfully verified is generated.

[0017] Optionally, the step of acquiring the internal flow characteristics of the component and classifying the component material data according to the internal flow characteristics to obtain the material classification result includes:

[0018] Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics of the components, obtain the general volume classification result, and incorporate it into the material feeding classification result and the online time lead time classification result.

[0019] The general volume classification result, the feeding classification result, and the online time lead time classification result are used as the material classification result.

[0020] Optionally, the step of performing logistics attribute affinity analysis on the material classification results to obtain part family details, and constructing a part family logistics attribute database based on the part family details, includes:

[0021] The material classification results are initially classified based on the common code field of similar parts in the BOM structure to obtain the preliminary classification results;

[0022] For part numbers with the same receiving inclusion attribute in the preliminary classification results, extract the same fields and classify them to obtain secondary classification results;

[0023] Revise the parts of the preset part family wildcard rules that conflict with the existing part family rules to obtain the revised target part family rules;

[0024] The secondary classification results are further refined according to the target part family rules to obtain each part family;

[0025] Taking each part family as the object, construct the corresponding logistics attributes for each part family, perform logistics attribute affinity analysis on each part family based on the logistics attributes of each part family, and generate part family details including corresponding part family master data, part family inclusion attributes, part family material feeding attributes, and part family assembly and distribution vehicle attributes.

[0026] A parts family logistics attribute database is constructed based on the parts family logistics attribute list corresponding to the parts family details.

[0027] Optionally, the step of connecting to the manufacturing subsystem corresponding to the current vehicle production plan via a multi-system interface, obtaining the automated logistics parameters of the manufacturing subsystem, verifying the automated logistics parameters according to the preset verification process, and generating successfully verified logistics data includes:

[0028] The system connects to the Logistics Execution System (LES), Manufacturing Execution System (MES), Material Requirements Planning (MRP) system, and Bill of Materials (BOM) system corresponding to the current vehicle production plan through multiple system interfaces.

[0029] The system receives production plan master data sent by the MRP system through the LES, receives plan execution information from the MES through the LES, receives BOM structure master data from the BOM system through the LES, and feeds back virtual workstation data and error workstation data to the BOM system through the LES.

[0030] The production plan master data, the plan execution information, the BOM structure master data, the virtual workstation data, and the error workstation data are used as automated logistics parameters;

[0031] Obtain the preset verification process corresponding to the automated logistics parameters. When the current production plan is locked, verify the target logistics parameters corresponding to the current production plan according to the preset verification process and generate logistics data that has been successfully verified.

[0032] Optionally, when receiving BOM data and detecting that the logistics attributes of the current component are missing, determining whether the current component's current part attributes match the part logistics attribute database includes:

[0033] Receive BOM data, and when the logistics attributes of the current component are detected to be missing, obtain the current part number, current vehicle model and current assembly station of the current component corresponding to the BOM data;

[0034] Obtain the part number field of the current component, and determine whether the current part attribute of the current component matches the part logistics attribute database based on the part number field, the current part number, the current vehicle model, and the current assembly station.

[0035] Optionally, when the current part attribute does not match the part family logistics attribute library, the step of intelligently recommending the current part attribute through a preset intelligent recommendation assistance program includes:

[0036] When the current part attributes do not match the part family logistics attribute library, the current part attributes are judged by a preset intelligent recommendation assistance program;

[0037] When the judgment result indicates that manual judgment is required, a manual judgment request is sent to the terminal.

[0038] Receive the manual judgment result, and when the manual judgment result indicates that the current part attribute is correct, write the current part attribute into the part logistics attribute database and the part family logistics attribute database according to the manual judgment result;

[0039] When the manual judgment result indicates that the current part attribute is incorrect, the error information is recorded and feedback is provided.

[0040] Secondly, to achieve the above objectives, the present invention also proposes an intelligent recommendation device for component logistics attributes, the intelligent recommendation device for component logistics attributes comprising:

[0041] The missing component detection module is used to receive BOM data and, when it detects that the logistics attributes of the current component are missing, determine whether the current component's current part attribute matches the part logistics attribute database.

[0042] The matching module is used to determine whether the current part attribute matches the part family logistics attribute library when it is detected that the current part attribute does not match the part logistics attribute database.

[0043] The intelligent recommendation module is used to make intelligent recommendations for the current part attributes when the current part attributes do not match the part family logistics attribute library, through a preset intelligent recommendation auxiliary program.

[0044] Thirdly, to achieve the above objectives, the present invention also proposes a component logistics attribute intelligent recommendation device, which includes: a memory, a processor, and a component logistics attribute intelligent recommendation program stored in the memory and executable on the processor. The component logistics attribute intelligent recommendation program is configured to implement the steps of the component logistics attribute intelligent recommendation method as described above.

[0045] Fourthly, to achieve the above objectives, the present invention also proposes a storage medium storing a component logistics attribute intelligent recommendation program, wherein the component logistics attribute intelligent recommendation program, when executed by a processor, implements the steps of the component logistics attribute intelligent recommendation method as described above.

[0046] The intelligent recommendation method for component logistics attributes proposed in this invention receives BOM data and, when a missing logistics attribute is detected for a current component, determines whether the current component's current attribute matches the component logistics attribute database. If the current component's attribute does not match the database, it determines whether it matches the component family logistics attribute library. When the current component's attribute does not match the library, a pre-set intelligent recommendation auxiliary program intelligently recommends the attribute. This method enables seamless workflow and automatic data retrieval between multiple subsystems in the manufacturing field, avoiding manual data import / export and external processing, thus reducing manpower significantly. It also enables online business processes, solidifies business processes and standards, and ensures the integrity and accuracy of process data. Based on the component family logistics attribute database, it achieves intelligent matching / recommendation of logistics attributes, reducing manual compilation workload and significantly lowering the data error rate. Furthermore, it ensures the timeliness of data processing, improves data integrity, reduces the data error rate, increases system response speed, and enhances the speed and efficiency of intelligent recommendation for component logistics attributes. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention;

[0048] Figure 2 This is a flowchart illustrating the first embodiment of the intelligent recommendation method for component logistics attributes of the present invention;

[0049] Figure 3 This is a flowchart illustrating the second embodiment of the intelligent recommendation method for component logistics attributes of the present invention;

[0050] Figure 4 This is a schematic diagram of the material attribute subclass feature matrix in the intelligent recommendation method for component logistics attributes of the present invention;

[0051] Figure 5 This is a schematic representation of the material attribute subclass attribute definition in the intelligent recommendation method for component logistics attributes of the present invention;

[0052] Figure 6 This is a schematic representation of part families and attribute details in the intelligent recommendation method for component logistics attributes of the present invention;

[0053] Figure 7 This is a schematic diagram of the system data interface association in the intelligent recommendation method for component logistics attributes of the present invention;

[0054] Figure 8 This is a schematic diagram of the three-level verification process in the intelligent recommendation method for component logistics attributes of the present invention;

[0055] Figure 9 This is a flowchart illustrating the third embodiment of the intelligent recommendation method for component logistics attributes of the present invention;

[0056] Figure 10 This is a flowchart illustrating the fourth embodiment of the intelligent recommendation method for component logistics attributes of the present invention;

[0057] Figure 11 This is a schematic diagram of the intelligent matching and recommendation process in the intelligent recommendation method for component logistics attributes of the present invention;

[0058] Figure 12 This is a functional block diagram of the first embodiment of the intelligent recommendation device for component logistics attributes of the present invention.

[0059] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0061] The solution of this invention mainly involves: receiving BOM data, and when a missing logistics attribute is detected for a current component, determining whether the current component's current part attribute matches the part logistics attribute database; when a mismatch is detected between the current part attribute and the part logistics attribute database, determining whether the current part attribute matches the part family logistics attribute library; and when a mismatch is detected between the current part attribute and the part family logistics attribute library, intelligently recommending the current part attribute through a preset intelligent recommendation auxiliary program. This enables seamless process integration and automatic data retrieval between multiple subsystems in the manufacturing field, avoiding manual data import, export, and external processing between systems, thus reducing the workload of a large number of personnel. It has achieved online business processes, solidified business processes and standards, and ensured the integrity and accuracy of process data. Based on the parts family logistics attribute database, it has realized intelligent matching / recommendation of logistics attributes, reducing a significant amount of manual compilation work and greatly lowering the data error rate. It has ensured the timeliness of data processing, improved data integrity, reduced the data error rate, increased system response speed, and improved the speed and efficiency of intelligent recommendation of parts logistics attributes. It has solved the technical problems in existing technologies where parts logistics attribute applications suffer from incomplete system data integration, high manpower consumption, low efficiency, high data error rate, errors or omissions only being discovered before use, poor actual results, and slow response speed.

[0062] Reference Figure 1 , Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention.

[0063] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0064] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0065] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating device, a network communication module, a user interface module, and a component logistics attribute intelligent recommendation program.

[0066] The device of this invention calls the intelligent recommendation program for the logistics attributes of parts stored in the memory 1005 through the processor 1001, and performs the following operations:

[0067] Upon receiving BOM data, when it is detected that the logistics attributes of the current component are missing, it is determined whether the current component's current part attributes match the part logistics attribute database.

[0068] When it is detected that the current part attribute does not match the part logistics attribute database, it is determined whether the current part attribute matches the part family logistics attribute library;

[0069] When the current part attributes do not match the part family logistics attribute library, a preset intelligent recommendation assistance program is used to make intelligent recommendations for the current part attributes.

[0070] The device of this invention, through processor 1001 calling the intelligent recommendation program for component logistics attributes stored in memory 1005, also performs the following operations:

[0071] Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics, and obtain the material classification results.

[0072] A parts logistics attribute database is constructed based on the logistics attribute data in the material classification results;

[0073] Perform logistics attribute affinity analysis on the material classification results to obtain part family details, and construct a part family logistics attribute database based on the part family details;

[0074] By connecting to the manufacturing subsystem corresponding to the current vehicle production plan through the multi-system interface, a preset verification process for the automated logistics parameters of the manufacturing subsystem is obtained. The automated logistics parameters are verified according to the preset verification process, and logistics data that has been successfully verified is generated.

[0075] The device of this invention, through processor 1001 calling the intelligent recommendation program for component logistics attributes stored in memory 1005, also performs the following operations:

[0076] Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics of the components, obtain the general volume classification result, and incorporate it into the material feeding classification result and the online time lead time classification result.

[0077] The general volume classification result, the feeding classification result, and the online time lead time classification result are used as the material classification result.

[0078] The device of this invention, through processor 1001 calling the intelligent recommendation program for component logistics attributes stored in memory 1005, also performs the following operations:

[0079] The material classification results are initially classified based on the common code field of similar parts in the BOM structure to obtain the preliminary classification results;

[0080] For part numbers with the same receiving inclusion attribute in the preliminary classification results, extract the same fields and classify them to obtain secondary classification results;

[0081] Revise the parts of the preset part family wildcard rules that conflict with the existing part family rules to obtain the revised target part family rules;

[0082] The secondary classification results are further refined according to the target part family rules to obtain each part family;

[0083] Taking each part family as the object, construct the corresponding logistics attributes for each part family, perform logistics attribute affinity analysis on each part family based on the logistics attributes of each part family, and generate part family details including corresponding part family master data, part family inclusion attributes, part family material feeding attributes, and part family assembly and distribution vehicle attributes.

[0084] A parts family logistics attribute database is constructed based on the parts family logistics attribute list corresponding to the parts family details.

[0085] The device of this invention, through processor 1001 calling the intelligent recommendation program for component logistics attributes stored in memory 1005, also performs the following operations:

[0086] The system connects to the Logistics Execution System (LES), Manufacturing Execution System (MES), Material Requirements Planning (MRP) system, and Bill of Materials (BOM) system corresponding to the current vehicle production plan through multiple system interfaces.

[0087] The system receives production plan master data sent by the MRP system through the LES, receives plan execution information from the MES through the LES, receives BOM structure master data from the BOM system through the LES, and feeds back virtual workstation data and error workstation data to the BOM system through the LES.

[0088] The production plan master data, the plan execution information, the BOM structure master data, the virtual workstation data, and the error workstation data are used as automated logistics parameters;

[0089] Obtain the preset verification process corresponding to the automated logistics parameters. When the current production plan is locked, verify the target logistics parameters corresponding to the current production plan according to the preset verification process and generate logistics data that has been successfully verified.

[0090] The device of this invention, through processor 1001 calling the intelligent recommendation program for component logistics attributes stored in memory 1005, also performs the following operations:

[0091] Receive BOM data, and when the logistics attributes of the current component are detected to be missing, obtain the current part number, current vehicle model and current assembly station of the current component corresponding to the BOM data;

[0092] Obtain the part number field of the current component, and determine whether the current part attribute of the current component matches the part logistics attribute database based on the part number field, the current part number, the current vehicle model, and the current assembly station.

[0093] The device of this invention, through processor 1001 calling the intelligent recommendation program for component logistics attributes stored in memory 1005, also performs the following operations:

[0094] When the current part attributes do not match the part family logistics attribute library, the current part attributes are judged by a preset intelligent recommendation assistance program;

[0095] When the judgment result indicates that manual judgment is required, a manual judgment request is sent to the terminal.

[0096] Receive the manual judgment result, and when the manual judgment result indicates that the current part attribute is correct, write the current part attribute into the part logistics attribute database and the part family logistics attribute database according to the manual judgment result;

[0097] When the manual judgment result indicates that the current part attribute is incorrect, the error information is recorded and feedback is provided.

[0098] This embodiment, through the above-described scheme, receives BOM data and, upon detecting a missing logistics attribute for a current component, determines whether the current component's current attribute matches the component's logistics attribute database. If the current component's attribute does not match the database, it determines whether it matches the component family's logistics attribute database. When the current component's attribute does not match the database, a pre-set intelligent recommendation program intelligently recommends the current component's attribute. This enables seamless workflow and automatic data retrieval between multiple subsystems in the manufacturing field, avoiding manual data import / export and external processing, thus significantly reducing manpower. It achieves online business processes, solidifies business processes and standards, and ensures the integrity and accuracy of process data. Based on the component family's logistics attribute database, it achieves intelligent matching / recommendation of logistics attributes, reducing manual compilation workload and significantly lowering the data error rate. It ensures the timeliness of data processing, improves data integrity, reduces the data error rate, increases system response speed, and enhances the speed and efficiency of intelligent recommendation of component logistics attributes.

[0099] Based on the above hardware structure, an embodiment of the intelligent recommendation method for component logistics attributes of the present invention is proposed.

[0100] Reference Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the intelligent recommendation method for component logistics attributes of the present invention.

[0101] In the first embodiment, the intelligent recommendation method for component logistics attributes includes the following steps:

[0102] Step S10: Receive BOM data. When the logistics attributes of the current component are detected to be missing, determine whether the current part attributes of the current component match the part logistics attribute database.

[0103] It should be noted that after receiving the Bill of Materials (BOM) data, when the logistics attributes of the current component are detected to be missing, it can be determined whether the current part attributes of the current component match the part logistics attribute database.

[0104] Step S20: When it is detected that the current part attribute does not match the part logistics attribute database, determine whether the current part attribute matches the part family logistics attribute database.

[0105] It should be understood that when it is detected that the current part attribute does not match the part logistics attribute database, it can be further determined whether the current part attribute matches the part family logistics attribute library.

[0106] Step S30: When the current part attribute does not match the part family logistics attribute library, intelligent recommendation is performed on the current part attribute through a preset intelligent recommendation assistance program.

[0107] It is understandable that when the current part attributes do not match the part family logistics attribute library, a pre-set intelligent recommendation assistance program will be used to intelligently recommend the current part attributes.

[0108] This embodiment, through the above-described scheme, receives BOM data and, upon detecting a missing logistics attribute for a current component, determines whether the current component's current attribute matches the component's logistics attribute database. If the current component's attribute does not match the database, it determines whether it matches the component family's logistics attribute database. When the current component's attribute does not match the database, a pre-set intelligent recommendation program intelligently recommends the current component's attribute. This enables seamless workflow and automatic data retrieval between multiple subsystems in the manufacturing field, avoiding manual data import / export and external processing, thus significantly reducing manpower. It achieves online business processes, solidifies business processes and standards, and ensures the integrity and accuracy of process data. Based on the component family's logistics attribute database, it achieves intelligent matching / recommendation of logistics attributes, reducing manual compilation workload and significantly lowering the data error rate. It ensures the timeliness of data processing, improves data integrity, reduces the data error rate, increases system response speed, and enhances the speed and efficiency of intelligent recommendation of component logistics attributes.

[0109] Furthermore, Figure 3 This is a flowchart illustrating the second embodiment of the intelligent recommendation method for component logistics attributes of the present invention, as shown below. Figure 3 As shown, based on the first embodiment, a second embodiment of the intelligent recommendation method for component logistics attributes of the present invention is proposed. In this embodiment, before step S10, the intelligent recommendation method for component logistics attributes further includes the following steps:

[0110] Step S01: Obtain the internal flow characteristics of the components, classify the material data of the components according to the internal flow characteristics, and obtain the material classification results.

[0111] It should be noted that after constructing the internal flow classification rules for materials, the internal flow of materials can be classified according to the internal flow characteristics of components, thereby obtaining the material classification results.

[0112] Furthermore, step S01 specifically includes the following steps:

[0113] Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics of the components, obtain the general volume classification result, and incorporate it into the material feeding classification result and the online time lead time classification result.

[0114] The general volume classification result, the feeding classification result, and the online time lead time classification result are used as the material classification result.

[0115] Understandably, based on the internal flow characteristics of the components, the internal flow of materials is classified. Different classification rules can generate different classification results, namely, general volume classification results, material feeding classification results, and online lead time classification results.

[0116] In practical implementation, material data can be categorized into ABC classifications, further dividing materials into A: large items, B: medium and small items, and C: general small items based on volume and versatility; materials can also be categorized into three types based on inclusion attributes: Kanban inclusion, sequence inclusion, and whole package inclusion; see [link to relevant documentation]. Figure 4 , Figure 4 This is a schematic diagram of the material attribute subclass feature matrix in the intelligent recommendation method for component logistics attributes of the present invention, as shown below. Figure 4 As shown, based on the internal flow path of the material, the feeding attributes are divided into five categories. Combined with the inclusion and feeding characteristics, the material is classified into multiple attributes, and each attribute can be coded and defined; see also Figure 5 , Figure 5 This is a representation of the subclass attribute definition of material attributes in the intelligent recommendation method for component logistics attributes of the present invention, such as... Figure 5 As shown, attribute definitions are made for material attribute subclasses, specifying parameters such as logistics lead time, inclusion method, inclusion frequency, online method, and online frequency for each subclass.

[0117] Step S02: Construct a parts logistics attribute database based on the logistics attribute data in the material classification results.

[0118] It is understandable that the logistics attribute data in the material classification results can be used to generate a parts logistics attribute database after data aggregation.

[0119] Step S03: Perform logistics attribute affinity analysis on the material classification results to obtain part family details, and construct a part family logistics attribute database based on the part family details.

[0120] It should be understood that a logistics attribute affinity analysis is performed on the material classification results to obtain part family details, and a part family logistics attribute database is constructed based on the part family details.

[0121] Furthermore, step S03 specifically includes the following steps:

[0122] The material classification results are initially classified based on the common code field of similar parts in the BOM structure to obtain the preliminary classification results;

[0123] For part numbers with the same receiving inclusion attribute in the preliminary classification results, extract the same fields and classify them to obtain secondary classification results;

[0124] Revise the parts of the preset part family wildcard rules that conflict with the existing part family rules to obtain the revised target part family rules;

[0125] The secondary classification results are further refined according to the target part family rules to obtain each part family;

[0126] Taking each part family as the object, construct the corresponding logistics attributes for each part family, perform logistics attribute affinity analysis on each part family based on the logistics attributes of each part family, and generate part family details including corresponding part family master data, part family inclusion attributes, part family material feeding attributes, and part family assembly and distribution vehicle attributes.

[0127] A parts family logistics attribute database is constructed based on the parts family logistics attribute list corresponding to the parts family details.

[0128] Understandably, big data analytics can be used to perform an initial preliminary classification based on the common code field of similar parts within the BOM structure, obtaining preliminary classification results. Big data analytics can then extract common fields from part numbers with the same receiving inclusion attributes for secondary classification, yielding secondary classification results. For differences in attributes among similar parts, a combination of common code field and identifier code field is used for refinement, and the created part family wildcard rules are compared with existing part family rules. If there are conflicts with existing rules, they are revised to obtain revised target part family rules. After refinement, the newly created part family rules are maintained and entered into the database.

[0129] In the specific implementation, see Figure 6 , Figure 6 This is a schematic representation of part families and attribute details in the intelligent recommendation method for component logistics attributes of the present invention, such as... Figure 6As shown, affinity analysis can be performed on massive amounts of logistics attribute data categorized by "vehicle model + part number" to generate a detailed table of part families. A logistics attribute database for part families can then be constructed. Logistics attributes for each part family are then built, forming a logistics attribute list. Part family attributes mainly include: inclusion attributes (inclusion method, inventory area, unloading location, warehouse keeper); material input attributes (online method, material input worker, distribution worker); and vehicle positioning attributes (protection method, front and rear attributes, left and right attributes). In the BOM structure data, each module and level of the same type of part has its own common fields, while different vehicle model character segments contain unique vehicle model information. The part code is 14 digits long, with digits 1-7 being the part feature code, digit 8 being the separator, and digits 9-14 being the vehicle model feature code. Generally, the part family logistics attribute list can be embedded into the information system to complete the initialization of the part family attribute database and the specific system interface.

[0130] Step S04: Connect to the manufacturing subsystem corresponding to the current vehicle production plan through the multi-system interface, obtain the preset verification process of the automated logistics parameters of the manufacturing subsystem, verify the automated logistics parameters according to the preset verification process, and generate logistics data that has been successfully verified.

[0131] It is understandable that by developing interfaces between multiple systems, multiple subsystems in the manufacturing field of a factory can be connected. That is, by connecting the manufacturing subsystem corresponding to the current vehicle production plan through the multi-system interface, the preset verification process of the automated logistics parameters of the manufacturing subsystem can be obtained, the automated logistics parameters can be verified according to the preset verification process, and logistics data with successful verification can be generated.

[0132] Furthermore, step S04 specifically includes the following steps:

[0133] The system connects to the Logistics Execution System (LES), Manufacturing Execution System (MES), Material Requirements Planning (MRP) system, and Bill of Materials (BOM) system corresponding to the current vehicle production plan through multiple system interfaces.

[0134] The system receives production plan master data sent by the MRP system through the LES, receives plan execution information from the MES through the LES, receives BOM structure master data from the BOM system through the LES, and feeds back virtual workstation data and error workstation data to the BOM system through the LES.

[0135] The production plan master data, the plan execution information, the BOM structure master data, the virtual workstation data, and the error workstation data are used as automated logistics parameters;

[0136] Obtain the preset verification process corresponding to the automated logistics parameters. When the current production plan is locked, verify the target logistics parameters corresponding to the current production plan according to the preset verification process and generate logistics data that has been successfully verified.

[0137] Understandably, see Figure 7 , Figure 7 This is a schematic diagram of the system data interface association in the intelligent recommendation method for component logistics attributes of the present invention, as shown below. Figure 7 As shown, based on the multi-system interfaces, it is possible to connect to the Logistics Execution System (LES), Manufacturing Execution System (MES), Material Requirements Planning (MRP) system, and Bill of Materials (BOM) system corresponding to the current vehicle production plan; interfaces between multiple systems were developed, connecting multiple subsystems of factory manufacturing, and the data interface requirements between related systems were sorted out, forming a system data interface relationship diagram. The relationships between data systems are as follows: Figure 7 As shown, the data interface development for each system is as follows:

[0138] ① The MPS system (Master Planning System) is used to transfer planning information to the LES system (Logistics Execution System).

[0139] The information transmitted includes: vehicle model information, BOM status, BOM date, production line, plan sequence, and quantity.

[0140] ②P-BOM (Manufacturing BOM) → LES, used to transfer the master BOM data of the planned vehicle model;

[0141] The information includes: vehicle model, part number, part name, part quantity, production line, assembly route, and assembly station.

[0142] In the specific implementation, see Figure 8 , Figure 8 This is a schematic diagram of the three-level verification process in the intelligent recommendation method for component logistics attributes of the present invention, as shown below. Figure 8 As shown, a three-level verification process based on system-wide automated logistics parameters is constructed, with tasks automatically pushed. 2) Automatic push of three-level verification tasks.

[0143] ① When the N+6 / N+3 / N+1 plan is locked, the MRP system pushes the plan to the LES system master plan. N is the production date, N+6 means the current date + 6 days, +3, and +1 mean the current date + 3 days and +1 days respectively.

[0144] ②The BOM system pushes the BOM structure data of the planned vehicle model.

[0145] ③ The LES system intelligently matches / recommends parts logistics attributes based on big data models and logistics attribute databases.

[0146] ④ Generate N+6 (5:00 AM daily) / N+3 (9:00 AM - 10:00 AM daily) / N+1 (12:00 PM daily) checklists and push them to the corresponding logistics engineer's task interface.

[0147] ⑤ The logistics engineer receives the task and completes the maintenance of logistics attributes on the same day.

[0148] ⑥ Virtual workstations and error data LES are pushed to BOM, and the technical department performs maintenance (node ​​requirements: virtual workstations verified by N+6 need to be maintained before the planned verification of N+3, and workstations with errors verified by N+3 need to be maintained before the planned verification of N+1).

[0149] This embodiment, through the above-described scheme, acquires the internal flow characteristics of components, classifies component material data according to these characteristics, and obtains material classification results. It then constructs a component logistics attribute database based on the logistics attribute data in the material classification results. Furthermore, it performs logistics attribute affinity analysis on the material classification results to obtain component family details, and constructs a component family logistics attribute database based on these details. By connecting to the manufacturing subsystem corresponding to the current vehicle production plan via a multi-system interface, it obtains a preset verification process for the automated logistics parameters of the manufacturing subsystem, verifies the automated logistics parameters according to the preset verification process, and generates successfully verified logistics data. This enables seamless process integration and automatic data retrieval between multiple subsystems in the manufacturing field, avoiding manual data import, export, and external processing between systems, thus reducing significant manpower. It also achieves online business processes, solidifies business processes and standards, ensures the integrity and accuracy of process data, and improves the speed and efficiency of intelligent recommendation of component logistics attributes.

[0150] Furthermore, Figure 9 This is a flowchart illustrating the third embodiment of the intelligent recommendation method for component logistics attributes of the present invention, as shown below. Figure 9 As shown, based on the first embodiment, a third embodiment of the intelligent recommendation method for component logistics attributes of the present invention is proposed. In this embodiment, step S10 specifically includes the following steps:

[0151] Step S11: Receive BOM data. When the logistics attributes of the current component are missing, obtain the current part number, current vehicle model, and current assembly station of the current component corresponding to the BOM data.

[0152] It should be noted that after receiving the BOM data, when the logistics attributes of the current component are detected to be missing, the current part number, current vehicle model, and current assembly station of the current component corresponding to the BOM data can be obtained.

[0153] Step S12: Obtain the part number field of the current component, and determine whether the current part attribute of the current component matches the part logistics attribute database based on the part number field, the current part number, the current vehicle model and the current assembly station.

[0154] It is understandable that after obtaining the part number field of the current component, it is possible to determine whether the current part attribute of the current component matches the part logistics attribute database based on the part number field, the current part number, the current vehicle model, and the current assembly station.

[0155] In practice, newly added BOM data (part number & vehicle model & assembly station) is matched with the part logistics attribute library using the part number field.

[0156] This embodiment, through the above-described scheme, receives BOM data and, upon detecting a missing logistics attribute for a current component, obtains the current part number, current vehicle model, and current assembly station corresponding to the current component in the BOM data. It then obtains the part number field of the current component and, based on the part number field, the current part number, the current vehicle model, and the current assembly station, determines whether the current component's current part attribute matches the part logistics attribute database. This enables seamless workflow and automatic data retrieval between multiple subsystems in the manufacturing field, avoiding manual data import / export and external processing between systems, thus reducing significant manpower. It also lowers the data error rate, improves system response speed, and enhances the speed and efficiency of intelligent recommendation of component logistics attributes.

[0157] Furthermore, Figure 10 This is a flowchart illustrating the fourth embodiment of the intelligent recommendation method for component logistics attributes of the present invention, as shown below. Figure 10 As shown, based on the first embodiment, a fourth embodiment of the intelligent recommendation method for component logistics attributes of the present invention is proposed. In this embodiment, step S30 specifically includes the following steps:

[0158] Step S31: When the current part attribute does not match the part family logistics attribute library, the current part attribute is judged by a preset intelligent recommendation auxiliary program.

[0159] It should be noted that when the current part attributes do not match the part family logistics attribute library, the current part attributes can be judged based on the part family logistics attribute database through a pre-set intelligent recommendation auxiliary program.

[0160] Step S32: When the judgment result indicates that manual judgment is required, send a manual judgment request to the terminal.

[0161] Understandably, when the judgment result indicates that manual judgment is required, a manual judgment request can be sent to the terminal.

[0162] Step S33: Receive the manual judgment result. When the manual judgment result indicates that the current part attribute is correct, write the current part attribute into the part logistics attribute database and the part family logistics attribute database according to the manual judgment result.

[0163] It should be understood that, upon receiving the manual judgment result, if the manual judgment result indicates that the current part attribute is correct, the current part attribute can be written into the part logistics attribute database and the part family logistics attribute database based on the manual judgment result.

[0164] In the specific implementation, see Figure 11 , Figure 11 This is a schematic diagram of the intelligent matching and recommendation process in the intelligent recommendation method for component logistics attributes of the present invention, as shown below. Figure 11 As shown, the system's automatic intelligent matching / recommendation process and rules are as follows:

[0165] ① New BOM data (part number & vehicle model & assembly station) is matched with the part logistics attribute database using the part number field;

[0166] ② Part number matching: If successful, a second matching will be performed by “part number & assembly station”; otherwise, proceed to ④ Part family matching.

[0167] ③ Part Number & Assembly Station Matching: If successful, the system automatically copies the logistics attributes; if unsuccessful, it intelligently recommends based on the existing logistics attributes of the part number and matches it against the part family database.

[0168] ④ Part family matching: If successful, a secondary matching will be performed based on "part family & assembly station"; if unsuccessful, logistics attributes will be recommended based on similar part family attributes.

[0169] ⑤ Part family & assembly station matching: If successful, copy logistics attributes according to the part family attributes; if unsuccessful, make intelligent recommendations based on the existing logistics attributes of the part family.

[0170] ⑥ After manual determination of logistics attributes, the system improves the logistics attribute database for parts and part families.

[0171] Step S34: When the manual judgment result indicates that the current part attribute is incorrect, record the error information and provide feedback.

[0172] It is understandable that when the manual judgment result indicates that the current part attribute is incorrect, the error information can be recorded and feedback provided.

[0173] In practical implementation, a field problem feedback process is established to improve data accuracy and enable the part family model to have a certain degree of self-learning ability; a data anomaly feedback process is also established to continuously improve data accuracy and enable the part family model to have a certain degree of self-learning ability.

[0174] ① Errors are discovered during warehousing and logistics distribution operations or final assembly operations;

[0175] ② Record any errors in the unit's records (production line, vehicle model, part name, part number, error phenomenon) into the shared unified management table;

[0176] ③ Determine the cause and follow up on the handling: Human error causing the jump ④ Correct assembly station but incorrect logistics attributes causing the jump ⑤ Incorrect assembly station causing incorrect logistics attributes leading to the jump ⑥;

[0177] ④ The warehouse should strengthen employee training and ensure employees operate according to instructions;

[0178] ⑤ Correcting erroneous logistics attributes within the LES system;

[0179] ⑥ The BOM system corrects erroneous workstations and transmits the corrected data to the LES system, which then automatically runs the intelligent matching / recommendation process.

[0180] ⑦ The corrected logistics attributes are automatically added to the logistics attribute database, optimizing the big data model.

[0181] This embodiment, through the above-described scheme, determines the current part attributes by using a preset intelligent recommendation auxiliary program when the current part attributes do not match the part family logistics attribute database. If the determination result indicates that manual judgment is required, a manual judgment request is sent to the terminal. Upon receiving the manual judgment result, if the manual judgment result indicates that the current part attributes are correct, the current part attributes are written into the part logistics attribute database and the part family logistics attribute database based on the manual judgment result. If the manual judgment result indicates that the current part attributes are incorrect, error information is recorded and feedback is provided. This enables seamless workflow and automatic data retrieval between multiple subsystems in the manufacturing field, avoiding manual data import, export, and external processing between systems, thus reducing significant manpower. It achieves online business processes, solidifies business processes and standards, and ensures the integrity and accuracy of process data. Based on the part family logistics attribute database, it realizes intelligent matching / recommendation of logistics attributes, reducing a large amount of manual compilation work and significantly lowering the data error rate. It ensures the timeliness of data processing, improves data integrity, reduces the data error rate, improves system response speed, and enhances the speed and efficiency of intelligent recommendation of component logistics attributes.

[0182] Accordingly, the present invention further provides an intelligent recommendation device for the logistics attributes of parts.

[0183] Reference Figure 12 , Figure 12 This is a functional block diagram of the first embodiment of the intelligent recommendation device for component logistics attributes of the present invention.

[0184] In the first embodiment of the intelligent recommendation device for component logistics attributes of the present invention, the intelligent recommendation device for component logistics attributes includes:

[0185] The missing data detection module 10 is used to receive BOM data and, when it detects that the logistics attributes of the current component are missing, determine whether the current component's current part attributes match the part logistics attribute database.

[0186] The matching module 20 is used to determine whether the current part attribute matches the part family logistics attribute library when it is detected that the current part attribute does not match the part logistics attribute database.

[0187] The intelligent recommendation module 30 is used to make intelligent recommendations for the current part attributes by means of a preset intelligent recommendation auxiliary program when the current part attributes do not match the part family logistics attribute library.

[0188] The missing component judgment module 10 is further configured to acquire the internal flow characteristics of the components, classify the component material data according to the internal flow characteristics, and obtain the material classification results; construct a component logistics attribute database based on the logistics attribute data in the material classification results; perform logistics attribute affinity analysis on the material classification results to obtain component family details, construct a component family logistics attribute database based on the component family details; connect to the manufacturing subsystem corresponding to the current vehicle production plan through the multi-system interface, acquire the preset verification process of the automated logistics parameters of the manufacturing subsystem, verify the automated logistics parameters according to the preset verification process, and generate successfully verified logistics data.

[0189] The missing component judgment module 10 is also used to obtain the internal flow characteristics of the component, classify the component material data according to the internal flow characteristics, obtain a general volume classification result, and include it in the material feeding classification result and the online time lead time classification result; and use the general volume classification result, the material feeding classification result and the online time lead time classification result as the material classification result.

[0190] The missing data judgment module 10 is further configured to: perform preliminary classification of the material classification results based on the common code field of similar parts in the BOM structure to obtain preliminary classification results; extract the same part fields from the part numbers with the same receiving inclusion attribute in the preliminary classification results for classification to obtain secondary classification results; revise the parts in the preset part family wildcard rules that conflict with the existing part family rules to obtain revised target part family rules; refine the classification of the secondary classification results according to the target part family rules to obtain each part family; construct corresponding logistics attributes for each part family as objects; perform logistics attribute affinity analysis on each part family according to the logistics attributes of each part family to generate part family details including corresponding part family master data, part family inclusion attributes, part family material feeding attributes, and part family assembly and distribution vehicle attributes; and construct a part family logistics attribute database according to the part family logistics attribute list corresponding to the part family details.

[0191] The missing item determination module 10 is further configured to connect to the Logistics Execution System (LES), Manufacturing Execution System (MES), Material Requirements Planning (MRP) system, and Bill of Materials (BOM) system corresponding to the current vehicle production plan via a multi-system interface; receive production plan master data sent by the MRP system through the LES; receive plan execution information from the MES through the LES; receive BOM structure master data from the BOM system through the LES; and feed back virtual workstation data and error workstation data to the BOM system through the LES; use the production plan master data, the plan execution information, the BOM structure master data, the virtual workstation data, and the error workstation data as automated logistics parameters; obtain a preset verification process corresponding to the automated logistics parameters; and when the current production plan is locked, verify the target logistics parameters corresponding to the current production plan according to the preset verification process to generate successfully verified logistics data.

[0192] The missing data determination module 10 is also used to receive BOM data, and when the missing logistics attributes of the current component are detected, obtain the current part number, current vehicle model and current assembly station of the current component corresponding to the BOM data; obtain the part number field of the current component, and determine whether the current part attribute of the current component matches the part logistics attribute database based on the part number field, the current part number, the current vehicle model and the current assembly station.

[0193] The intelligent recommendation module 30 is further configured to: when the current part attribute does not match the part family logistics attribute library, determine the current part attribute through a preset intelligent recommendation auxiliary program; when the determination result indicates that manual determination is required, send a manual determination request to the terminal; receive the manual determination result; when the manual determination result indicates that the current part attribute is correct, write the current part attribute into the part logistics attribute database and the part family logistics attribute library according to the manual determination result; and when the manual determination result indicates that the current part attribute is incorrect, record the error information and provide feedback.

[0194] The steps for implementing each functional module of the intelligent recommendation device for component logistics attributes can be referred to in the various embodiments of the intelligent recommendation method for component logistics attributes of the present invention, and will not be repeated here.

[0195] Furthermore, this embodiment of the invention also proposes a storage medium storing a component logistics attribute intelligent recommendation program, which, when executed by a processor, performs the following operations:

[0196] Upon receiving BOM data, if the logistics attributes of the current component are detected to be missing, determine whether the current component's current part attributes match the part logistics attribute database.

[0197] When it is detected that the current part attribute does not match the part logistics attribute database, it is determined whether the current part attribute matches the part family logistics attribute library;

[0198] When the current part attributes do not match the part family logistics attribute library, a preset intelligent recommendation assistance program is used to make intelligent recommendations for the current part attributes.

[0199] Furthermore, when the intelligent recommendation program for the logistics attributes of the components is executed by the processor, it also performs the following operations:

[0200] Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics, and obtain the material classification results.

[0201] A parts logistics attribute database is constructed based on the logistics attribute data in the material classification results;

[0202] Perform logistics attribute affinity analysis on the material classification results to obtain part family details, and construct a part family logistics attribute database based on the part family details;

[0203] By connecting to the manufacturing subsystem corresponding to the current vehicle production plan through the multi-system interface, a preset verification process for the automated logistics parameters of the manufacturing subsystem is obtained. The automated logistics parameters are verified according to the preset verification process, and logistics data that has been successfully verified is generated.

[0204] Furthermore, when the intelligent recommendation program for the logistics attributes of the components is executed by the processor, it also performs the following operations:

[0205] Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics of the components, obtain the general volume classification result, and incorporate it into the material feeding classification result and the online time lead time classification result.

[0206] The general volume classification result, the feeding classification result, and the online time lead time classification result are used as the material classification result.

[0207] Furthermore, when the intelligent recommendation program for the logistics attributes of the components is executed by the processor, it also performs the following operations:

[0208] The material classification results are initially classified based on the common code field of similar parts in the BOM structure to obtain the preliminary classification results;

[0209] For part numbers with the same receiving inclusion attribute in the preliminary classification results, extract the same fields and classify them to obtain secondary classification results;

[0210] Revise the parts of the preset part family wildcard rules that conflict with the existing part family rules to obtain the revised target part family rules;

[0211] The secondary classification results are further refined according to the target part family rules to obtain each part family;

[0212] Taking each part family as the object, construct the corresponding logistics attributes for each part family, perform logistics attribute affinity analysis on each part family based on the logistics attributes of each part family, and generate part family details including corresponding part family master data, part family inclusion attributes, part family material feeding attributes, and part family assembly and distribution vehicle attributes.

[0213] A parts family logistics attribute database is constructed based on the parts family logistics attribute list corresponding to the parts family details.

[0214] Furthermore, when the intelligent recommendation program for the logistics attributes of the components is executed by the processor, it also performs the following operations:

[0215] The system connects to the Logistics Execution System (LES), Manufacturing Execution System (MES), Material Requirements Planning (MRP) system, and Bill of Materials (BOM) system corresponding to the current vehicle production plan through multiple system interfaces.

[0216] The system receives production plan master data sent by the MRP system through the LES, receives plan execution information from the MES through the LES, receives BOM structure master data from the BOM system through the LES, and feeds back virtual workstation data and error workstation data to the BOM system through the LES.

[0217] The production plan master data, the plan execution information, the BOM structure master data, the virtual workstation data, and the error workstation data are used as automated logistics parameters;

[0218] Obtain the preset verification process corresponding to the automated logistics parameters. When the current production plan is locked, verify the target logistics parameters corresponding to the current production plan according to the preset verification process and generate logistics data that has been successfully verified.

[0219] Furthermore, when the intelligent recommendation program for the logistics attributes of the components is executed by the processor, it also performs the following operations:

[0220] Receive BOM data, and when the logistics attributes of the current component are detected to be missing, obtain the current part number, current vehicle model and current assembly station of the current component corresponding to the BOM data;

[0221] Obtain the part number field of the current component, and determine whether the current part attribute of the current component matches the part logistics attribute database based on the part number field, the current part number, the current vehicle model, and the current assembly station.

[0222] Furthermore, when the intelligent recommendation program for the logistics attributes of the components is executed by the processor, it also performs the following operations:

[0223] When the current part attributes do not match the part family logistics attribute library, the current part attributes are judged by a preset intelligent recommendation assistance program;

[0224] When the judgment result indicates that manual judgment is required, a manual judgment request is sent to the terminal.

[0225] Receive the manual judgment result, and when the manual judgment result indicates that the current part attribute is correct, write the current part attribute into the part logistics attribute database and the part family logistics attribute database according to the manual judgment result;

[0226] When the manual judgment result indicates that the current part attribute is incorrect, the error information is recorded and feedback is provided.

[0227] Those skilled in the art will understand that all or part of the steps in the methods described above can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium is a computer-readable storage medium, including: USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media capable of storing program code.

[0228] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0229] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0230] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for intelligent recommendation of logistics attributes for parts, characterized in that, The intelligent recommendation method for component logistics attributes includes: Upon receiving BOM data, when it is detected that the logistics attributes of the current component are missing, it is determined whether the current component's current part attributes match the part logistics attribute database. When it is detected that the current part attribute does not match the part logistics attribute database, it is determined whether the current part attribute matches the part family logistics attribute database; When the current part attribute does not match the part family logistics attribute database, a preset intelligent recommendation assistance program is used to make intelligent recommendations for the current part attribute; The intelligent recommendation method for component logistics attributes, before determining whether the current component's current attributes match the component logistics attribute database when the logistics attributes of the current component are detected to be missing during the receipt of BOM data, further includes: Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics, and obtain the material classification results. A parts logistics attribute database is constructed based on the logistics attribute data in the material classification results; Perform logistics attribute affinity analysis on the material classification results to obtain part family details, and construct a part family logistics attribute database based on the part family details; By connecting to the manufacturing subsystem corresponding to the current vehicle production plan through the multi-system interface, a preset verification process for the automated logistics parameters of the manufacturing subsystem is obtained. The automated logistics parameters are verified according to the preset verification process, and logistics data that has been successfully verified is generated.

2. The intelligent recommendation method for component logistics attributes as described in claim 1, characterized in that, The process of acquiring the internal flow characteristics of components and classifying the internal flow of component material data based on these characteristics to obtain material classification results includes: Obtain the internal flow characteristics of the components, classify the internal flow of the component material data according to the internal flow characteristics of the components, obtain the general volume classification result, and incorporate it into the material feeding classification result and the online time lead time classification result. The general volume classification result, the feeding classification result, and the online time lead time classification result are used as the material classification result.

3. The intelligent recommendation method for component logistics attributes as described in claim 1, characterized in that, The process involves performing a logistics attribute affinity analysis on the material classification results to obtain a detailed list of part families, and then constructing a part family logistics attribute database based on this detailed list. The material classification results are initially classified based on the common code field of similar parts in the BOM structure to obtain the preliminary classification results; For part numbers with the same receiving inclusion attribute in the preliminary classification results, extract the same fields and classify them to obtain secondary classification results; Revise the parts of the preset part family wildcard rules that conflict with the existing part family rules to obtain the revised target part family rules; The secondary classification results are further refined according to the target part family rules to obtain each part family; Taking each part family as the object, construct the corresponding logistics attributes for each part family, perform logistics attribute affinity analysis on each part family based on the logistics attributes of each part family, and generate part family details including corresponding part family master data, part family inclusion attributes, part family material feeding attributes, and part family assembly and distribution vehicle attributes. A parts family logistics attribute database is constructed based on the parts family logistics attribute list corresponding to the parts family details.

4. The intelligent recommendation method for component logistics attributes as described in claim 1, characterized in that, The process of connecting to the manufacturing subsystem corresponding to the current vehicle production plan via a multi-system interface, obtaining the automated logistics parameters of the manufacturing subsystem, verifying the automated logistics parameters according to the preset verification process, and generating successfully verified logistics data includes: The system connects to the Logistics Execution System (LES), Manufacturing Execution System (MES), Material Requirements Planning (MRP) system, and Bill of Materials (BOM) system corresponding to the current vehicle production plan through multiple system interfaces. The system receives production plan master data sent by the MRP system through the LES, receives plan execution information from the MES through the LES, receives BOM structure master data from the BOM system through the LES, and feeds back virtual workstation data and error workstation data to the BOM system through the LES. The production plan master data, the plan execution information, the BOM structure master data, the virtual workstation data, and the error workstation data are used as automated logistics parameters; Obtain the preset verification process corresponding to the automated logistics parameters. When the current production plan is locked, verify the target logistics parameters corresponding to the current production plan according to the preset verification process and generate logistics data that has been successfully verified.

5. The intelligent recommendation method for component logistics attributes as described in claim 1, characterized in that, When receiving BOM data, if the logistics attributes of the current component are detected to be missing, the method of determining whether the current component's current part attributes match the part logistics attribute database includes: Receive BOM data, and when the logistics attributes of the current component are detected to be missing, obtain the current part number, current vehicle model and current assembly station of the current component corresponding to the BOM data; Obtain the part number field of the current component, and determine whether the current part attribute of the current component matches the part logistics attribute database based on the part number field, the current part number, the current vehicle model, and the current assembly station.

6. The intelligent recommendation method for component logistics attributes as described in claim 1, characterized in that, When the current part attributes do not match the part family logistics attribute database, the method of intelligently recommending the current part attributes through a preset intelligent recommendation assistance program includes: When the current part attributes do not match the part family logistics attribute database, the current part attributes are judged by a preset intelligent recommendation assistance program; When the judgment result indicates that manual judgment is required, a manual judgment request is sent to the terminal. Receive the manual judgment result, and when the manual judgment result indicates that the current part attribute is correct, write the current part attribute into the part logistics attribute database and the part family logistics attribute database according to the manual judgment result; When the manual judgment result indicates that the current part attribute is incorrect, the error information is recorded and feedback is provided.

7. A component logistics attribute intelligent recommendation device, characterized in that, The intelligent recommendation device for the logistics attributes of the components includes: The missing component detection module is used to receive BOM data and, when it detects that the logistics attributes of the current component are missing, determine whether the current component's current part attribute matches the part logistics attribute database. The matching module is used to determine whether the current part attribute matches the part family logistics attribute database when it is detected that the current part attribute does not match the part logistics attribute database. The intelligent recommendation module is used to make intelligent recommendations for the current part attributes by means of a preset intelligent recommendation auxiliary program when the current part attributes do not match the part family logistics attribute database. The missing component detection module is further configured to: acquire the internal flow characteristics of the components; classify the component material data according to the internal flow characteristics to obtain material classification results; construct a component logistics attribute database based on the logistics attribute data in the material classification results; perform logistics attribute affinity analysis on the material classification results to obtain component family details; construct a component family logistics attribute database based on the component family details; connect to the manufacturing subsystem corresponding to the current vehicle production plan through a multi-system interface; acquire the preset verification process of the automated logistics parameters of the manufacturing subsystem; verify the automated logistics parameters according to the preset verification process; and generate successfully verified logistics data.

8. A component logistics attribute intelligent recommendation device, characterized in that, The intelligent recommendation device for component logistics attributes includes: a memory, a processor, and an intelligent recommendation program for component logistics attributes stored in the memory and executable on the processor. The intelligent recommendation program for component logistics attributes is configured to implement the steps of the intelligent recommendation method for component logistics attributes as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a component logistics attribute intelligent recommendation program, which, when executed by a processor, implements the steps of the component logistics attribute intelligent recommendation method as described in any one of claims 1 to 6.

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