Part logistics attribute intelligent recommendation method, device and equipment and storage medium

Abstract

The invention discloses a part logistics attribute intelligent recommendation method, device and equipment and a storage medium, and the method comprises the steps: receiving BOM data, and judging whether the current part attribute of a current part is matched with a part logistics attribute database or not when detecting that the logistics attribute of the current part is missing; when it is detected that the current part attribute is not matched with the part logistics attribute database, whether the current part attribute is matched with the part family logistics attribute database or not is judged; when the current part attribute is not matched with the part family logistics attribute library, intelligent recommendation is performed on the current part attribute through the preset intelligent recommendation auxiliary program, so that process connection and automatic data calling among a plurality of subsystems in the manufacturing field can be realized, manual data import, export and out-of-system processing among the systems are avoided, and the production efficiency is improved. A large amount of manpower is reduced; the data error rate is reduced, the system response speed is improved, and the intelligent recommendation speed and efficiency of the part logistics attributes are improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle manufacturing technology, and in particular to a method, device, equipment and storage medium for intelligently recommending logistics attributes of parts. Background Art

[0002] The current status of the application of parts logistics attributes is that parts logistics attribute data is generated manually, and the data is stored in multiple systems. There is no connection between the systems. Generally, material demand orders are generated according to production plans, and manual requests are generated. For the vehicle material master data, the logistics attribute integrity check of the vehicle master data is manually initiated. Missing data is posted in the group through instant messaging tools. The data is manually improved outside the system (generally through Excel tables, one by one according to the experience of employees) and uploaded to the database. However, the existing technology has the following problems:

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

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

[0005] 3. The data error rate is high, and data errors and omissions often occur due to human factors;

[0006] 4. Errors or omissions in data can only be discovered before it is used. The timeliness of data processing is highly required. In addition, the need for manual transmission and processing results in poor actual results and slow response, which often leads to delayed release of inclusion instructions. Summary of the invention

[0007] The main purpose of the present invention is to provide a method, device, equipment and storage medium for intelligent recommendation of parts logistics attributes, aiming to solve the technical problems in the prior art of parts logistics attribute application, such as the lack of system data integration, large manpower consumption, low efficiency, high data error rate, errors or omissions can only be discovered before use, poor actual effect and slow response speed.

[0008] In a first aspect, the present invention provides a method for intelligently recommending parts logistics attributes, the method comprising the following steps:

[0009] Receiving BOM data, and when detecting that the logistics attribute of the current component is missing, determining whether the current part attribute of the current component matches a part logistics attribute database;

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

[0011] When the current part attribute does not match the part family logistics attribute library, an intelligent recommendation is performed on the current part attribute through a preset intelligent recommendation auxiliary program.

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

[0013] Acquire the internal flow characteristics of the parts, and classify the internal flow of the parts material data according to the internal flow characteristics of the parts to obtain the material classification result;

[0014] Constructing a parts logistics attribute database according to the logistics attribute data in the material classification results;

[0015] 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;

[0016] The manufacturing subsystem corresponding to the current vehicle production plan is connected according to the multi-system interface, a preset verification process of 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 with successful verification is generated.

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

[0018] Obtain the internal flow characteristics of the parts, classify the internal flow of the parts material data according to the internal flow characteristics of the parts, obtain the general volume classification results, and incorporate them into the material feeding classification results and the online time lead time classification results;

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

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

[0021] Preliminarily classify the material classification results according to the common code field of similar parts in the BOM structure to obtain preliminary classification results;

[0022] For the part numbers with the same receiving inclusion attribute in the preliminary classification results, the same partial fields are intercepted and classified to obtain a secondary classification result;

[0023] Revise the part of the preset part family wildcard rule that conflicts with the existing part family rule to obtain the revised target part family rule;

[0024] Refining and classifying the secondary classification results 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 of each part family, conduct logistics attribute affinity analysis on each part family according to the logistics attributes of each part family, and generate the part family details of the corresponding part family master data, part family inclusion attributes, part family feeding attributes and part family assembly vehicle attributes;

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

[0027] Optionally, the step of connecting to a manufacturing subsystem corresponding to a current vehicle production plan according to a multi-system interface, obtaining a preset verification process for 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] Connect the logistics execution system LES, manufacturing execution system MES, material requirement planning MRP system and bill of materials BOM system corresponding to the current vehicle production plan through the multi-system interface;

[0029] Receive the production plan master data sent by the MRP system through the LES, receive the plan execution information of the MES through the LES, receive the BOM structure master data of the BOM system through the LES, and feed back the virtual workstation data and the 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 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.

[0032] Optionally, the receiving of BOM data, when detecting that the logistics attribute of the current component is missing, determining whether the current part attribute of the current component matches a part logistics attribute database, includes:

[0033] Receiving BOM data, and when detecting that the logistics attribute of the current component is missing, obtaining the current part number, current vehicle model, and current assembly station of the current component corresponding to the BOM data;

[0034] The part number field of the current component is obtained, and whether the current part attribute of the current component matches the part logistics attribute database is determined according to 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, intelligently recommending the current part attribute through a preset intelligent recommendation auxiliary program includes:

[0036] 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;

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

[0038] receiving a manual determination result, and when it is detected that the manual determination result indicates that the current part attribute is correct, writing the current part attribute into the part logistics attribute database and the part family logistics attribute library according to the manual determination result;

[0039] When it is detected that the manual determination result is that the current part attribute is wrong, the error information is recorded and fed back.

[0040] In a second aspect, in order to achieve the above-mentioned purpose, the present invention further proposes a device for intelligently recommending parts logistics attributes, the device comprising:

[0041] A missing judgment module is used to receive BOM data and, when detecting that the logistics attribute of the current component is missing, judge whether the current part attribute of the current component matches the part logistics attribute database;

[0042] A matching judgment module, used for judging 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;

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

[0044] In the third aspect, in order to achieve the above-mentioned purpose, the present invention also proposes a device for intelligently recommending parts logistics attributes, wherein the device comprises: a memory, a processor, and a program for intelligently recommending parts logistics attributes stored in the memory and executable on the processor, wherein the program for intelligently recommending parts logistics attributes is configured to implement the steps of the method for intelligently recommending parts logistics attributes as described above.

[0045] In a fourth aspect, in order to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which is stored a program for intelligent recommendation of parts logistics attributes, and when the program for intelligent recommendation of parts logistics attributes is executed by a processor, the steps of the method for intelligent recommendation of parts logistics attributes as described above are implemented.

[0046] The method for intelligent recommendation of parts logistics attributes proposed by the present invention receives BOM data, and when it is detected that the logistics attributes of the current parts are missing, determines whether the current part attributes of the current parts match the parts logistics attribute database; when it is detected that the current part attributes do not match the parts logistics attribute database, determines whether the current part attributes match the parts family logistics attribute library; when the current part attributes do not match the parts family logistics attribute library, the current part attributes are intelligently recommended through a preset intelligent recommendation auxiliary program, which can realize the process connection and automatic data call between multiple subsystems in the manufacturing field, avoid manual data import, export, and out-of-system processing between systems, and reduce a lot of manpower; realize the online business process, solidify the business process and business standards, so that the integrity and accuracy of process data can be guaranteed; relying on the parts family logistics attribute database, realizes intelligent matching / recommendation of logistics attributes, reduces a lot of manual compilation workload, and greatly reduces the data error rate; ensures the timeliness of data processing, improves the integrity of data, reduces the data error rate, improves the system response speed, and improves the speed and efficiency of intelligent recommendation of parts logistics attributes. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention;

[0048] Figure 2 It is a flow chart of the first embodiment of the intelligent recommendation method for parts logistics attributes of the present invention;

[0049] Figure 3 This is a flow chart of a second embodiment of the method for intelligently recommending parts logistics attributes of the present invention;

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

[0051] Figure 5 It is a schematic diagram showing the definition of the material attribute subclass attribute in the intelligent recommendation method for parts logistics attributes of the present invention;

[0052] Figure 6 It is a schematic diagram of the detailed representation of part families and attributes in the intelligent recommendation method for parts logistics attributes of the present invention;

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

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

[0055] Fig. 9 It is a flow chart of the third embodiment of the intelligent recommendation method for parts logistics attributes of the present invention;

[0056] Fig.10 This is a flow chart of a fourth embodiment of the method for intelligently recommending parts logistics attributes of the present invention;

[0057] Fig.11 It is a schematic diagram of the system intelligent matching recommendation process in the intelligent recommendation method for parts logistics attributes of the present invention;

[0058] Fig.12 This is a functional module diagram of the first embodiment of the intelligent recommendation device for parts logistics attributes of the present invention.

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

[0060] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0061] The solution of the embodiment of the present invention is mainly: by receiving BOM data, when it is detected that the logistics attribute of the current component is missing, it is determined whether the current part attribute of the current component matches 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 library; when the current part attribute does not match the part family logistics attribute library, the current part attribute is intelligently recommended through a preset intelligent recommendation auxiliary program, which can realize the process connection and automatic data call between multiple subsystems in the manufacturing field, avoid manual data import, export and system processing between systems, and reduce a large amount of manpower. It realizes the online business process, solidifies the business process and business standards, and ensures the integrity and accuracy of process data; based on the parts family logistics attribute database, it realizes the intelligent matching / recommendation of logistics attributes, reduces a lot of manual compilation workload, and greatly reduces the data error rate; it ensures the timeliness of data processing, improves the integrity of data, reduces the data error rate, improves the system response speed, and improves the speed and efficiency of intelligent recommendation of parts logistics attributes, and solves the technical problems in the existing technology of parts logistics attribute application, such as the system data is not connected, a lot of manpower is consumed, low efficiency, high data error rate, errors or omissions can only be discovered before being used, poor actual effect, and slow response speed.

[0062] Reference Figure 1 , Figure 1 The figure is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment 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. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory, or it may be a stable memory (Non-Volatile Memory), such as a disk memory. The memory 1005 may optionally 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 in the figure does not constitute a limitation of the device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

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

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

[0067] Receiving BOM data, and when detecting that the logistics attribute of the current component is missing, determining whether the current part attribute of the current component matches a part logistics attribute database;

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

[0069] When the current part attribute does not match the part family logistics attribute library, an intelligent recommendation is performed on the current part attribute through a preset intelligent recommendation auxiliary program.

[0070] The device of the present invention calls the parts logistics attribute intelligent recommendation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0071] Acquire the internal flow characteristics of the parts, and classify the internal flow of the parts material data according to the internal flow characteristics of the parts to obtain the material classification result;

[0072] Constructing a parts logistics attribute database according to the logistics attribute data in the material classification results;

[0073] 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;

[0074] The manufacturing subsystem corresponding to the current vehicle production plan is connected according to the multi-system interface, a preset verification process of 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 with successful verification is generated.

[0075] The device of the present invention calls the parts logistics attribute intelligent recommendation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0076] Obtain the internal flow characteristics of the parts, classify the internal flow of the parts material data according to the internal flow characteristics of the parts, obtain the general volume classification results, and incorporate them into the material feeding classification results and the online time lead time classification results;

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

[0078] The device of the present invention calls the parts logistics attribute intelligent recommendation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0079] Preliminarily classify the material classification results according to the common code field of similar parts in the BOM structure to obtain preliminary classification results;

[0080] For the part numbers with the same receiving inclusion attribute in the preliminary classification results, the same partial fields are intercepted and classified to obtain a secondary classification result;

[0081] Revise the part of the preset part family wildcard rule that conflicts with the existing part family rule to obtain the revised target part family rule;

[0082] Refining and classifying the secondary classification results 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 of each part family, conduct logistics attribute affinity analysis on each part family according to the logistics attributes of each part family, and generate the part family details of the corresponding part family master data, part family inclusion attributes, part family feeding attributes and part family assembly vehicle attributes;

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

[0085] The device of the present invention calls the parts logistics attribute intelligent recommendation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0086] Connect the logistics execution system LES, manufacturing execution system MES, material requirement planning MRP system and bill of materials BOM system corresponding to the current vehicle production plan through the multi-system interface;

[0087] Receive the production plan master data sent by the MRP system through the LES, receive the plan execution information of the MES through the LES, receive the BOM structure master data of the BOM system through the LES, and feed back the virtual workstation data and the 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 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.

[0090] The device of the present invention calls the parts logistics attribute intelligent recommendation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0091] Receiving BOM data, and when detecting that the logistics attribute of the current component is missing, obtaining the current part number, current vehicle model, and current assembly station of the current component corresponding to the BOM data;

[0092] The part number field of the current component is obtained, and whether the current part attribute of the current component matches the part logistics attribute database is determined according to the part number field, the current part number, the current vehicle model and the current assembly station.

[0093] The device of the present invention calls the parts logistics attribute intelligent recommendation program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0094] 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;

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

[0096] receiving a manual determination result, and when it is detected that the manual determination result indicates that the current part attribute is correct, writing the current part attribute into the part logistics attribute database and the part family logistics attribute library according to the manual determination result;

[0097] When it is detected that the manual determination result is that the current part attribute is wrong, the error information is recorded and fed back.

[0098] This embodiment uses the above scheme to receive BOM data. When it is detected that the logistics attribute of the current component is missing, it is determined whether the current part attribute of the current component matches 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 library; when the current part attribute does not match the part family logistics attribute library, the current part attribute is intelligently recommended through a preset intelligent recommendation auxiliary program, which can realize the process connection and automatic data call between multiple subsystems in the manufacturing field, avoid manual data import, export, and out-of-system processing between systems, and reduce a lot of manpower; realize the online business process, solidify the business process and business standards, so that the integrity and accuracy of process data can be guaranteed; based on the part family logistics attribute database, it realizes intelligent matching / recommendation of logistics attributes, reduces a lot of manual compilation workload, and greatly reduces the data error rate; ensures the timeliness of data processing, improves the integrity of data, reduces the data error rate, improves the system response speed, and improves the speed and efficiency of intelligent recommendation of parts logistics attributes.

[0099] Based on the above hardware structure, an embodiment of the method for intelligently recommending parts logistics attributes of the present invention is proposed.

[0100] Reference Figure 2 , Figure 2 This is a flow chart of the first embodiment of the intelligent recommendation method for parts logistics attributes of the present invention.

[0101] In the first embodiment, the method for intelligently recommending parts logistics attributes includes the following steps:

[0102] Step S10, receiving BOM data, and when detecting that the logistics attribute of the current component is missing, determining whether the current part attribute of the current component matches the part logistics attribute database.

[0103] It should be noted that after receiving the bill of materials (BOM) data, when it is detected that the logistics attribute of the current component is missing, it can be determined whether the current part attribute of the current component matches 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, it is determined 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 database.

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

[0107] It is understandable that when the current part attribute does not match the part family logistics attribute library, the current part attribute is intelligently recommended through a pre-set preset intelligent recommendation auxiliary program.

[0108] This embodiment uses the above scheme to receive BOM data. When it is detected that the logistics attribute of the current component is missing, it is determined whether the current part attribute of the current component matches 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 library; when the current part attribute does not match the part family logistics attribute library, the current part attribute is intelligently recommended through a preset intelligent recommendation auxiliary program, which can realize the process connection and automatic data call between multiple subsystems in the manufacturing field, avoid manual data import, export, and out-of-system processing between systems, and reduce a lot of manpower; realize the online business process, solidify the business process and business standards, so that the integrity and accuracy of process data can be guaranteed; based on the part family logistics attribute database, it realizes intelligent matching / recommendation of logistics attributes, reduces a lot of manual compilation workload, and greatly reduces the data error rate; ensures the timeliness of data processing, improves the integrity of data, reduces the data error rate, improves the system response speed, and improves the speed and efficiency of intelligent recommendation of parts logistics attributes.

[0109] Further, Figure 3 FIG. 2 is a flow chart of a second embodiment of the method for intelligently recommending parts logistics attributes according to the present invention. Figure 3 As shown, based on the first embodiment, a second embodiment of the method for intelligently recommending parts logistics attributes of the present invention is proposed. In this embodiment, before step S10, the method for intelligently recommending parts logistics attributes further includes the following steps:

[0110] Step S01, obtaining the internal flow characteristics of the component, and classifying the component material data according to the internal flow characteristics of the component to obtain a material classification result.

[0111] It should be noted that after constructing the material internal flow classification rules, the internal flow of the material can be classified according to the internal flow characteristics of the parts, so as to obtain the material classification results.

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

[0113] Obtain the internal flow characteristics of the parts, classify the internal flow of the parts material data according to the internal flow characteristics of the parts, obtain the general volume classification results, and incorporate them into the material feeding classification results and the online time lead time classification results;

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

[0115] It can be understood that the internal flow of materials is classified according to the internal flow characteristics of parts and components, and different classification rules can generate different classification results, namely, the general classification results of volume, which include the classification results of feeding and the classification results of the lead time of going online.

[0116] In the specific implementation, the material data can be classified into ABC, and then the materials can be divided into A: large items, B: small and medium items, and C: general small items according to volume and commonality; the materials can be divided into three categories according to the inclusion attributes: kanban inclusion, sequence inclusion, and whole package inclusion; see Figure 4 , Figure 4 Schematic diagram of the material attribute subclass feature matrix in the intelligent recommendation method for parts logistics attributes of the present invention, such as Figure 4 As shown in the figure, the feeding properties are divided into five categories according to the internal flow path of the material. Combined with the feeding characteristics, the material is divided into multiple attributes, and each attribute can be coded and defined; see Figure 5 , Figure 5 This is a schematic diagram showing the definition of the material attribute subclass attribute in the intelligent recommendation method for parts logistics attributes of the present invention, such as Figure 5 As shown, the material attribute subcategories are defined to clarify the parameters of each subcategory, such as logistics lead time, inclusion method, inclusion frequency, online method, and online frequency.

[0117] Step S02: construct a parts logistics attribute database according to the logistics attribute data in the material classification results.

[0118] It is understandable that, based on the logistics attribute data in the material classification results, a parts logistics attribute database can be generated after the data is aggregated.

[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 the material classification results are subjected to a logistics attribute affinity analysis to obtain part family details, and a part family logistics attribute database is constructed based on the part family details.

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

[0122] Preliminarily classify the material classification results according to the common code field of similar parts in the BOM structure to obtain preliminary classification results;

[0123] For the part numbers with the same receiving inclusion attribute in the preliminary classification results, the same partial fields are intercepted and classified to obtain a secondary classification result;

[0124] Revise the part of the preset part family wildcard rule that conflicts with the existing part family rule to obtain the revised target part family rule;

[0125] Refining and classifying the secondary classification results 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 of each part family, conduct logistics attribute affinity analysis on each part family according to the logistics attributes of each part family, and generate the part family details of the corresponding part family master data, part family inclusion attributes, part family feeding attributes and part family assembly vehicle attributes;

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

[0128] It is understandable that big data analysis can be used to perform the first preliminary classification according to the universal code fields of similar parts in the BOM structure to obtain preliminary classification results; big data analysis can be used to classify part numbers with the same receipt inclusion attributes by intercepting the same part fields to obtain secondary classification results; for differences in the attributes of similar parts, the universal code field + identification code field is used for refinement, and the created part family wildcard rules are compared with the existing part family rules; those that conflict with the existing rules are revised to obtain the revised target part family rules; after improvement, the new part family rules are maintained in the database.

[0129] In the specific implementation, see Figure 6 , Figure 6 This is a schematic representation of the details of the part family and attributes in the intelligent recommendation method for parts logistics attributes of the present invention, such as Figure 6As shown in the figure, affinity analysis can be performed on massive logistics attribute data of "vehicle model + part number" to form a part family list, and a part family logistics attribute database can be constructed with part family as the object; with part family as the object, the logistics attributes of part family are constructed to form a logistics attribute list; the part family attributes mainly include: inclusion attributes (inclusion method, inventory area, unloading space, custodian); feeding attributes (online method, feeding worker, collection and distribution worker); collection and distribution vehicle positioning attributes (protection method, front and rear attributes, left and right attributes). In the BOM structure data, similar parts have their own common fields under each module and each level, and different vehicle model character segments contain personalized vehicle model information. The part code has a total of 14 digits, 1-7 digits are part feature codes, 8 digits are separators, and 9-14 digits are vehicle model feature codes; generally, the part family logistics attribute list can be built into the information system to complete the initialization of the part family attribute library. The specific system interface.

[0130] Step S04, connecting to the manufacturing subsystem corresponding to the current vehicle production plan according to the multi-system interface, obtaining the preset verification process of 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.

[0131] It can be understood that by developing interfaces between multiple systems, multiple factory manufacturing subsystems can be connected, that is, the manufacturing subsystem corresponding to the current vehicle production plan is connected according to the multi-system interface, and the preset verification process of the automated logistics parameters of the manufacturing subsystem is obtained. The automated logistics parameters are verified according to the preset verification process to generate successfully verified logistics data.

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

[0133] Connect the logistics execution system LES, manufacturing execution system MES, material requirement planning MRP system and bill of materials BOM system corresponding to the current vehicle production plan through the multi-system interface;

[0134] Receive the production plan master data sent by the MRP system through the LES, receive the plan execution information of the MES through the LES, receive the BOM structure master data of the BOM system through the LES, and feed back the virtual workstation data and the 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 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.

[0137] Understandably, see Figure 7 , Figure 7 Schematic diagram of the system data interface association in the intelligent recommendation method for parts logistics attributes of the present invention, such as Figure 7 As shown in the figure, 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 can be connected according to the multi-system interface; interfaces between multiple systems are developed to connect multiple subsystems of factory manufacturing, and the data interface requirements between related systems are sorted out to form a system data interface association diagram. The relationship between data systems is shown in the figure. Figure 7 As shown, system data interface development is performed one by one:

[0138] ①MPS system (Master Planning System) → LES system (Logistics Execution System), used to transmit planning information;

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

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

[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 Schematic diagram of the three-level verification process in the intelligent recommendation method for parts logistics attributes of the present invention, such as Figure 8 As shown, a three-level verification process based on the automated logistics parameters of the system is constructed, and tasks are automatically pushed. 2) Three-level verification tasks are automatically pushed

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

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

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

[0146] ④ Generate N+6 (5:00 every day) / N+3 (9:00-10:00 every day) / N+1 (12:00 every day) verification shortage list and push it to the corresponding logistics engineer task interface.

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

[0148] ⑥ The virtual and erroneous data of the workstation are pushed to BOM by LES, and the technical room performs maintenance (node ​​requirements: the virtual workstation verified by N+6 needs to be maintained before the N+3 planned verification, and the erroneous workstation verified by N+3 needs to be maintained before the N+1 planned verification).

[0149] Through the above scheme, this embodiment obtains the internal flow characteristics of parts and components, classifies the internal flow of parts and components material data according to the internal flow characteristics of the parts and components, and obtains material classification results; constructs a parts logistics attribute database according to the logistics attribute data in the material classification results; performs logistics attribute affinity analysis on the material classification results to obtain part family details, and constructs a parts family logistics attribute database according to the part family details; connects to the manufacturing subsystem corresponding to the current vehicle production plan according to the multi-system interface, obtains the preset verification process of 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; can realize process integration and automatic data call between multiple subsystems in the manufacturing field, avoids manual data import, export, and external processing between systems, and reduces a lot of manpower; realizes the online business process, solidifies the business process and business standards, ensures the integrity and accuracy of process data, and improves the speed and efficiency of intelligent recommendation of parts logistics attributes.

[0150] Further, Fig. 9 FIG. 1 is a flow chart of a third embodiment of the method for intelligently recommending parts logistics attributes according to the present invention. Fig. 9 As shown, a third embodiment of the intelligent recommendation method for parts logistics attributes of the present invention is proposed based on the first embodiment. In this embodiment, the step S10 specifically includes the following steps:

[0151] Step S11, receiving BOM data, and when detecting that the logistics attribute of the current component is missing, obtaining 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 it is detected that the logistics attribute of the current component is 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 attributes of the current component match 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 the specific implementation, the newly-incoming BOM data (part number, vehicle model, and assembly station) is matched with the part logistics attribute library using the part number field.

[0156] This embodiment adopts the above scheme, by receiving BOM data, when detecting that the logistics attributes of the current component are missing, obtains the current part number, current vehicle model and current assembly station of the current component corresponding to the BOM data; obtains the part number field of the current component, and judges whether the current part attributes of the current component match the part logistics attribute database according to the part number field, the current part number, the current vehicle model and the current assembly station, so as to realize the process connection and automatic data call between multiple subsystems in the manufacturing field, avoid manual data import, export and external processing between systems, and reduce a lot of manpower; reduce data error rate, improve system response speed, and improve the speed and efficiency of intelligent recommendation of logistics attributes of components.

[0157] Further, Fig.10 FIG. 4 is a flow chart of a fourth embodiment of the method for intelligently recommending parts logistics attributes according to the present invention. Fig.10 As shown, a fourth embodiment of the intelligent recommendation method for parts logistics attributes of the present invention is proposed based on the first embodiment. 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 database, the current part attributes can be judged based on the part family logistics attribute database through a pre-set preset intelligent recommendation auxiliary program.

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

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

[0162] Step S33, receiving the manual determination result, and when it is detected that the manual determination result is that the current part attribute is correct, writing the current part attribute into the part logistics attribute database and the part family logistics attribute library according to the manual determination result.

[0163] It should be understood that after receiving the manual determination result, when it is detected that the manual determination result is 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 library according to the manual determination result.

[0164] In the specific implementation, see Fig.11 , Fig.11 FIG. 1 is a schematic diagram of the intelligent matching recommendation process of the system in the intelligent recommendation method for parts logistics attributes of the present invention. Fig.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, and assembly station) is matched with the parts logistics attribute library using the part number field;

[0166] ②、Part number matching: if successful, perform secondary matching according to "part number & assembly station", if failed, jump to ④part family matching;

[0167] ③、Part number & assembly station matching: If successful, the system will automatically copy the logistics attributes; if failed, it will make intelligent recommendations based on the existing logistics attributes of the part number and match the part family database;

[0168] ④. Parts family matching: If successful, secondary matching will be performed based on "parts family & assembly station". If failed, logistics attributes will be recommended based on similar parts family attributes.

[0169] ⑤、Parts family & assembly station matching: If successful, the logistics attributes are copied according to the part family attributes; if failed, intelligent recommendations are made according to the existing logistics attributes of the part family;

[0170] ⑥ After manually determining the logistics attributes, the system completes the logistics attribute library for parts and part families.

[0171] Step S34: when it is detected that the manual determination result is that the current part attribute is wrong, the error information is recorded and fed back.

[0172] It is understandable that when it is detected that the manual determination result is that the current part attribute is wrong, error information can be recorded and fed back.

[0173] In the specific implementation, we will establish a field problem feedback process to improve the accuracy of data and enable the part family model to have a certain degree of autonomous learning ability; we will establish a data anomaly feedback process to continuously improve data accuracy and enable the part family model to have a certain degree of autonomous learning ability:

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

[0175] ②. The unit that discovers the error records the error information (production line, vehicle model, part name, part number, error phenomenon) to a shared unified management table;

[0176] ③. Determine the cause and follow up the disposal: jump caused by human error ④. Jump caused by incorrect logistics attributes of the assembly station ⑤. Jump caused by incorrect logistics attributes due to assembly station error ⑥;

[0177] ④. Warehousing should strengthen the training of employees and make them work according to the instructions;

[0178] ⑤. Correct the incorrect logistics attributes in the LES system;

[0179] ⑥ Correct the wrong workstation in the BOM system, and the BOM system transmits the corrected data to the LES system, which automatically runs the intelligent matching / recommendation process;

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

[0181] Through the above scheme, this embodiment judges the current part attribute through a preset intelligent recommendation auxiliary program when the current part attribute does not match the part family logistics attribute library; sends a manual judgment request to the terminal when the judgment result is that manual judgment is required; receives the manual judgment result, and when it is detected that the manual judgment result is that the current part attribute is correct, writes the current part attribute into the part logistics attribute database and the part family logistics attribute library according to the manual judgment result; when it is detected that the manual judgment result is that the current part attribute is wrong, records the error information and feedbacks it, so that the process connection and automatic data call between multiple subsystems in the manufacturing field can be realized, avoiding manual data import, export, and out-of-system processing between systems, reducing a lot of manpower; realizing the online business process, solidifying the business process and business standards, so that the integrity and accuracy of the process data can be guaranteed; relying on the part family logistics attribute database, realizing intelligent matching / recommendation of logistics attributes, reducing a lot of manual compilation workload, and greatly reducing the data error rate; ensuring the timeliness of data processing, improving the integrity of data, reducing the data error rate, improving the system response speed, and improving the speed and efficiency of intelligent recommendation of parts logistics attributes.

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

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

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

[0185] The missing judgment module 10 is used to receive BOM data, and when it is detected that the logistics attribute of the current component is missing, judge whether the current part attribute of the current component matches the part logistics attribute database.

[0186] The matching judgment module 20 is used to judge 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.

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

[0188] The missing judgment module 10 is also used to obtain the internal flow characteristics of the parts, classify the internal flow of the parts material data according to the internal flow characteristics of the parts, and obtain the material classification results; construct a parts logistics attribute database according to the logistics attribute data in the material classification results; perform logistics attribute affinity analysis on the material classification results to obtain the parts family details, and construct a parts family logistics attribute database according to the parts family details; connect to the manufacturing subsystem corresponding to the current vehicle production plan according to 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 successfully verified logistics data.

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

[0190] The missing judgment module 10 is also used to perform preliminary classification of the material classification results according to the common code fields of similar parts in the BOM structure to obtain preliminary classification results; to intercept the same part fields of the part numbers with the same receipt inclusion attributes in the preliminary classification results for classification to obtain secondary classification results; to revise the part of the preset part family wildcard rule that conflicts with the existing part family rule to obtain the revised target part family rule; to refine and classify the secondary classification results according to the target part family rule to obtain each part family; to construct the corresponding logistics attributes of each part family with each part family as the object, to analyze the logistics attribute affinity of each part family according to the logistics attributes of each part family, and to generate the part family details of the corresponding part family master data, part family inclusion attributes, part family feeding attributes and part family assembly vehicle attributes; to construct a part family logistics attribute database according to the part family logistics attribute list corresponding to the part family details.

[0191] The missing judgment module 10 is also used to connect the logistics execution system LES, the manufacturing execution system MES, the material requirement planning MRP system and the bill of materials BOM system corresponding to the current vehicle production plan according to the multi-system interface; receive the production plan master data sent by the MRP system through the LES, receive the plan execution information of the MES through the LES, receive the BOM structure master data of the BOM system through the LES, and feedback the virtual workstation data and the 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 the 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 judgment module 10 is also used to receive BOM data, and when it is detected that 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; obtain the part number field of the current component, and judge whether the current part attributes of the current component match 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 also used to judge the current part attributes through a preset intelligent recommendation auxiliary program when the current part attributes do not match the part family logistics attribute library; when the judgment result is that manual judgment is required, send a manual judgment request to the terminal; receive the manual judgment result, and when it is detected that the manual judgment result is that the current part attributes are correct, write the current part attributes into the part logistics attribute database and the part family logistics attribute library according to the manual judgment result; when it is detected that the manual judgment result is that the current part attributes are wrong, record error information and feedback.

[0194] Among them, the steps implemented by each functional module of the component logistics attribute intelligent recommendation device can refer to the various embodiments of the component logistics attribute intelligent recommendation method of the present invention, and will not be repeated here.

[0195] In addition, an embodiment of the present invention further provides a storage medium, on which a component logistics attribute intelligent recommendation program is stored. When the component logistics attribute intelligent recommendation program is executed by a processor, the following operations are implemented:

[0196] Receiving BOM data, and when detecting that the logistics attribute of the current component is missing, determining whether the current part attribute of the current component matches a part logistics attribute database;

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

[0198] When the current part attribute does not match the part family logistics attribute library, an intelligent recommendation is performed on the current part attribute through a preset intelligent recommendation auxiliary program.

[0199] Furthermore, when the component logistics attribute intelligent recommendation program is executed by the processor, the following operations are also implemented:

[0200] Acquire the internal flow characteristics of the parts, and classify the internal flow of the parts material data according to the internal flow characteristics of the parts to obtain the material classification result;

[0201] Constructing a parts logistics attribute database according to the logistics attribute data in the material classification results;

[0202] 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;

[0203] The manufacturing subsystem corresponding to the current vehicle production plan is connected according to the multi-system interface, a preset verification process of 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 with successful verification is generated.

[0204] Furthermore, when the component logistics attribute intelligent recommendation program is executed by the processor, the following operations are also implemented:

[0205] Obtain the internal flow characteristics of the parts, classify the internal flow of the parts material data according to the internal flow characteristics of the parts, obtain the general volume classification results, and incorporate them into the material feeding classification results and the online time lead time classification results;

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

[0207] Furthermore, when the component logistics attribute intelligent recommendation program is executed by the processor, the following operations are also implemented:

[0208] Preliminarily classify the material classification results according to the common code field of similar parts in the BOM structure to obtain preliminary classification results;

[0209] For the part numbers with the same receiving inclusion attribute in the preliminary classification results, the same partial fields are intercepted and classified to obtain a secondary classification result;

[0210] Revise the part of the preset part family wildcard rule that conflicts with the existing part family rule to obtain the revised target part family rule;

[0211] Refining and classifying the secondary classification results 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 of each part family, conduct logistics attribute affinity analysis on each part family according to the logistics attributes of each part family, and generate the part family details of the corresponding part family master data, part family inclusion attributes, part family feeding attributes and part family assembly vehicle attributes;

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

[0214] Furthermore, when the component logistics attribute intelligent recommendation program is executed by the processor, the following operations are also implemented:

[0215] Connect the logistics execution system LES, manufacturing execution system MES, material requirement planning MRP system and bill of materials BOM system corresponding to the current vehicle production plan through the multi-system interface;

[0216] Receive the production plan master data sent by the MRP system through the LES, receive the plan execution information of the MES through the LES, receive the BOM structure master data of the BOM system through the LES, and feed back the virtual workstation data and the 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 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.

[0219] Furthermore, when the component logistics attribute intelligent recommendation program is executed by the processor, the following operations are also implemented:

[0220] Receiving BOM data, and when detecting that the logistics attribute of the current component is missing, obtaining the current part number, current vehicle model, and current assembly station of the current component corresponding to the BOM data;

[0221] The part number field of the current component is obtained, and whether the current part attribute of the current component matches the part logistics attribute database is determined according to the part number field, the current part number, the current vehicle model and the current assembly station.

[0222] Furthermore, when the component logistics attribute intelligent recommendation program is executed by the processor, the following operations are also implemented:

[0223] 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;

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

[0225] receiving a manual determination result, and when it is detected that the manual determination result indicates that the current part attribute is correct, writing the current part attribute into the part logistics attribute database and the part family logistics attribute library according to the manual determination result;

[0226] When it is detected that the manual determination result is that the current part attribute is wrong, the error information is recorded and fed back.

[0227] Those skilled in the art will appreciate that all or part of the steps in the above-mentioned implementation method can be accomplished by instructing related hardware through a program, and the program is stored in a storage medium, including a number of instructions for enabling a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each implementation method of the present application; and the aforementioned storage medium is a computer-readable storage medium, including: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0228] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0229] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0230] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for intelligently recommending parts logistics attributes, characterized in that: The intelligent recommendation method for parts logistics attributes includes: Receiving BOM data, and when detecting that the logistics attribute of the current component is missing, determining whether the current part attribute of the current component matches a part logistics attribute database; When it is detected that the current part attribute does not match the part logistics attribute database, determining 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 library, an intelligent recommendation is performed on the current part attribute through a preset intelligent recommendation auxiliary program.

2. The intelligent recommendation method for parts logistics attributes according to claim 1, characterized in that: The receiving of BOM data, when detecting that the logistics attribute of the current component is missing, before determining whether the current part attribute of the current component matches the part logistics attribute database, the part logistics attribute intelligent recommendation method further includes: Acquire the internal flow characteristics of the parts, and classify the internal flow of the parts material data according to the internal flow characteristics of the parts to obtain the material classification result; Constructing a parts logistics attribute database according to the logistics attribute data in the material classification results; 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; The manufacturing subsystem corresponding to the current vehicle production plan is connected according to the multi-system interface, a preset verification process of 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 with successful verification is generated.

3. The intelligent recommendation method for parts logistics attributes according to claim 2, characterized in that: The obtaining of the internal flow characteristics of the component, and performing internal flow classification on the component material data according to the internal flow characteristics of the component to obtain the material classification result includes: Obtain the internal flow characteristics of the parts, classify the internal flow of the parts material data according to the internal flow characteristics of the parts, obtain the general volume classification results, and incorporate them into the material feeding classification results and the online time lead time classification results; The general volume classification result, the input material classification result and the online time lead time classification result are used as the material classification result.

4. The intelligent recommendation method for parts logistics attributes according to claim 2, characterized in that: The material classification results are subjected to logistics attribute affinity analysis to obtain part family details, and a part family logistics attribute database is constructed according to the part family details, including: Preliminarily classify the material classification results according to the common code field of similar parts in the BOM structure to obtain preliminary classification results; For the part numbers with the same receiving inclusion attribute in the preliminary classification results, the same partial fields are intercepted and classified to obtain a secondary classification result; Revise the part of the preset part family wildcard rule that conflicts with the existing part family rule to obtain the revised target part family rule; Refining and classifying the secondary classification results according to the target part family rules to obtain each part family; Taking each part family as the object, construct the corresponding logistics attributes of each part family, conduct logistics attribute affinity analysis on each part family according to the logistics attributes of each part family, and generate the part family details of the corresponding part family master data, part family inclusion attributes, part family feeding attributes and part family assembly vehicle attributes; A parts family logistics attribute database is constructed according to the parts family logistics attribute list corresponding to the parts family details.

5. The intelligent recommendation method for parts logistics attributes according to claim 2, characterized in that: The method of connecting the manufacturing subsystem corresponding to the current vehicle production plan according to the multi-system interface, obtaining a preset verification process of 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: Connect the logistics execution system LES, manufacturing execution system MES, material requirement planning MRP system and bill of materials BOM system corresponding to the current vehicle production plan through the multi-system interface; Receive the production plan master data sent by the MRP system through the LES, receive the plan execution information of the MES through the LES, receive the BOM structure master data of the BOM system through the LES, and feed back the virtual workstation data and the 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 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.

6. The intelligent recommendation method for parts logistics attributes according to claim 1, characterized in that: The receiving of BOM data, when detecting that the logistics attribute of the current component is missing, determining whether the current part attribute of the current component matches the part logistics attribute database, includes: Receiving BOM data, and when detecting that the logistics attribute of the current component is missing, obtaining the current part number, current vehicle model, and current assembly station of the current component corresponding to the BOM data; The part number field of the current component is obtained, and whether the current part attribute of the current component matches the part logistics attribute database is determined according to the part number field, the current part number, the current vehicle model and the current assembly station.

7. The intelligent recommendation method for parts logistics attributes according to claim 1, characterized in that: 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 auxiliary program, including: 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; When the judgment result is that manual judgment is required, a manual judgment request is sent to the terminal; receiving a manual determination result, and when it is detected that the manual determination result indicates that the current part attribute is correct, writing the current part attribute into the part logistics attribute database and the part family logistics attribute library according to the manual determination result; When it is detected that the manual determination result is that the current part attribute is wrong, the error information is recorded and fed back.

8. An intelligent recommendation device for parts logistics attributes, characterized in that: The intelligent recommendation device for parts logistics attributes includes: A missing judgment module is used to receive BOM data and, when detecting that the logistics attribute of the current component is missing, judge whether the current part attribute of the current component matches the part logistics attribute database; A matching judgment module, used for judging 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 on the current part attributes through a preset intelligent recommendation auxiliary program when the current part attributes do not match the part family logistics attribute library.

9. An intelligent recommendation device for parts logistics attributes, characterized in that: The intelligent recommendation device for parts logistics attributes includes: a memory, a processor, and an intelligent recommendation program for parts logistics attributes stored in the memory and executable on the processor. The intelligent recommendation program for parts logistics attributes is configured to implement the steps of the intelligent recommendation method for parts logistics attributes as described in any one of claims 1 to 7.

10. 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 7.

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