Data real-time monitoring method based on intelligent factory management platform system

Abstract

The application relates to the technical field of intelligent factory management platform, and discloses a data real-time monitoring method based on an intelligent factory management platform system, which comprises the following steps: standard format conversion is performed on supplier Excel data, carrier GPS track data flow and warehouse RFID cargo location data in an intelligent factory ERP system to obtain standardized business data; a supplier comprehensive score is calculated based on the standardized business data, and a dynamic sorting list is generated; procurement order information is received, a logistics scheduling plan is created, and optimal cargo locations are pre-assigned for warehousing commodities to obtain a warehousing pre-allocation scheme; the execution progress of the warehousing pre-allocation scheme is monitored in real time, and business state information is synchronously updated; the audit state of the procurement order information is monitored based on the business state information, and an intelligent collaborative decision scheme is generated; the application ensures real-time synchronous updating of the business states of three modules of supplier management, logistics scheduling and warehouse management, and improves the efficiency of supply chain collaborative management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart factory management platform, and particularly relates to a data real-time monitoring method based on a smart factory management platform system. BACKGROUND

[0002] In a modern smart factory management platform system, supplier management, logistics scheduling and warehouse management as the core links of the supply chain involve the processing and collaboration of a large number of heterogeneous data sources. The traditional ERP system often adopts independent modular design, and the business state updates of the three core modules of supplier management, logistics scheduling and warehouse management often have time difference and data inconsistency, resulting in information gaps and decision delays in the business process. When the status of the purchase order changes, the related logistics arrangement and warehouse preparation cannot respond in time, and the supply chain collaboration efficiency is seriously affected. SUMMARY

[0003] The present application provides a data real-time monitoring method based on a smart factory management platform system, which ensures real-time synchronous update of the business state of the three modules of supplier management, logistics scheduling and warehouse management, and improves the efficiency of supply chain collaborative management.

[0004] In a first aspect, the present application provides a data real-time monitoring method based on a smart factory management platform system, which comprises:

[0005] Converting the supplier Excel data, the carrier GPS trajectory data stream and the warehouse RFID location data in the smart factory ERP system into a standard format to obtain standardized business data;

[0006] Calculating the supplier comprehensive score based on the standardized business data and generating a dynamic ranking list;

[0007] Receiving purchase order information and creating a logistics scheduling plan in combination with the dynamic ranking list, and pre-allocating the optimal location for the incoming goods to obtain a warehouse pre-allocation scheme;

[0008] Real-time monitoring of the execution progress of the warehouse pre-allocation scheme, and synchronous update of the business state information;

[0009] Monitoring the audit state of the purchase order information based on the business state information, and generating an intelligent collaborative decision scheme according to the audit state.

[0010] In combination with the first aspect, in a first implementation manner of the first aspect of the present application, the conversion of the supplier Excel data, the carrier GPS trajectory data stream and the warehouse RFID location data in the smart factory ERP system into a standard format to obtain standardized business data comprises:

[0011] extracting supplier Excel data, carrier GPS trajectory data stream and warehouse RFID storage data in a smart factory ERP system;

[0012] respectively performing difference analysis on a supplier information field in the supplier Excel data, a location coordinate field in the carrier GPS trajectory data stream and a storage location code field in the warehouse RFID storage data, to obtain format difference information;

[0013] creating a supplier code unification rule, a GPS coordinate standardization rule and an RFID tag conversion rule based on the format difference information;

[0014] converting the supplier Excel data, the carrier GPS trajectory data stream and the warehouse RFID storage data into standardized business data based on the supplier code unification rule, the GPS coordinate standardization rule and the RFID tag conversion rule.

[0015] In a second implementation manner of the first aspect, the calculating a supplier comprehensive score based on the standardized business data and generating a dynamic ranking list comprises:

[0016] parsing basic profile information and purchase and storage documents of a supplier in the standardized business data, and extracting historical transaction records of each supplier based on the basic profile information and the purchase and storage documents;

[0017] counting the number of on-time deliveries, the quality pass rate and the payment timeliness rate in the historical transaction records, and combining a qualification certification level and a price competitive advantage of the supplier to obtain qualification information of the supplier;

[0018] calculating a supplier comprehensive score based on the qualification information, and screening suppliers whose supplier comprehensive scores exceed a preset score threshold to generate a dynamic ranking list.

[0019] In a third implementation manner of the first aspect, the receiving purchase order information and creating a logistics scheduling plan in combination with the dynamic ranking list, and pre-allocating an optimal storage location for a storage commodity to obtain a storage pre-allocation scheme comprises:

[0020] receiving and parsing supplier address coordinates, commodity volume and weight parameters and delivery time requirements in the purchase order information, and generating order transportation requirement information in combination with transportation requirements of order commodities in the purchase order information;

[0021] The order transportation demand information is matched with the supplier geographical positions in the dynamic sorting list, suppliers with reasonable transportation distance and supplier capacity meeting the order demand are screened out, and a candidate supplier set is obtained;

[0022] The carrier resource list corresponding to the candidate supplier set is traversed, and an optimal carrier and an optimal transportation path are selected from the carrier resource list;

[0023] The vehicle scheduling information of the optimal carrier and the time node arrangement of the optimal transportation path are integrated, and a logistics scheduling plan is obtained;

[0024] According to the logistics scheduling plan, the warehouse inventory state is analyzed, and the optimal storage location is pre-allocated for the warehousing goods, and a warehousing pre-allocation scheme is obtained.

[0025] In combination with the first aspect, in a fourth implementation manner of the first aspect of the application, the traversing of the carrier resource list corresponding to the candidate supplier set and the selection of the optimal carrier and the optimal transportation path from the carrier resource list comprise:

[0026] The carriers associated with each supplier are extracted from the candidate supplier set to obtain a carrier resource list;

[0027] The vehicle carrying capacity, the historical punctuality rate and the transportation cost of each carrier are calculated based on the carrier resource list;

[0028] The vehicle carrying capacity, the historical punctuality rate and the transportation cost are input into a multi-objective optimization algorithm for weight distribution and comprehensive scoring to obtain a plurality of qualified carriers;

[0029] The optimal carrier with the highest comprehensive score is selected from the plurality of qualified carriers, and the optimal transportation path of the optimal carrier from the supplier address to the enterprise warehouse is calculated.

[0030] In combination with the first aspect, in a fifth implementation manner of the first aspect of the application, the analysis of the warehouse inventory state according to the logistics scheduling plan, the pre-allocation of the optimal storage location for the warehousing goods, and the obtaining of the warehousing pre-allocation scheme comprise:

[0031] The expected arrival time, the warehousing goods name, the goods quantity and the volume weight parameters in the logistics scheduling plan are extracted, the storage temperature requirement and the shelf life constraint condition of the order goods in the purchase order information are analyzed, and goods storage demand information is obtained;

[0032] The current inventory, the safety inventory threshold and the reserved inventory data corresponding to the goods storage demand information in the warehouse management module are queried to obtain the warehouse inventory state;

[0033] Scan the occupancy and storage capacity of each location in the warehouse inventory state, calculate the location capacity matching degree combined with the volume requirement of the incoming goods, screen the location options that meet the storage conditions and are convenient for access, and obtain a candidate location list;

[0034] Analyze the distance and access convenience of each location in the candidate location list to the outlet, perform classified location allocation according to the turnover frequency of the incoming goods, and obtain a warehouse pre-allocation scheme.

[0035] In combination with the first aspect, in a sixth implementation manner of the first aspect of the application, the execution progress of the warehouse pre-allocation scheme is monitored in real time, and business state information is updated synchronously, including:

[0036] The execution progress of the location allocation in the warehouse pre-allocation scheme and the completion progress of the goods warehousing are monitored, and the execution progress of the location occupancy state in the warehouse management module is captured;

[0037] The execution progress is received, and state change events of the supplier delivery state in the supplier management module and the vehicle arrival state in the logistics scheduling module are captured;

[0038] Timestamp conflicts in the state change events are processed, and state consistency verification is performed to obtain a synchronous state verification result;

[0039] The synchronous state verification result is applied to update the business state information of the supplier management module, the logistics scheduling module, and the warehouse management module.

[0040] In combination with the first aspect, in a seventh implementation manner of the first aspect of the application, the timestamp conflicts in the state change events are processed, and state consistency verification is performed to obtain a synchronous state verification result, including:

[0041] The timestamp identifiers of the operation records of the supplier management module, the logistics scheduling module, and the warehouse management module in the state change events are analyzed, and timestamp conflicts are identified based on the timestamp identifiers;

[0042] According to the shared resource number involved in the timestamp conflict, a corresponding distributed lock control authority is applied to the intelligent factory ERP system;

[0043] During the period of holding the distributed lock control authority, the current business state data of the supplier management module, the logistics scheduling module, and the warehouse management module are read, and a state consistency verification result is generated based on the current business state data;

[0044] Based on the state consistency verification result, data rollback or re-synchronization operation is performed on inconsistent state information, and after completion, all distributed lock resources are released in turn to obtain a synchronous state verification result.

[0045] In combination with the first aspect, in an eighth implementation manner of the first aspect of the present application, the monitoring of the review state of the purchase order information based on the business state information and the generation of the intelligent collaborative decision-making scheme according to the review state comprises:

[0046] continuously monitoring the review state of the purchase order information from the business state information and generating a state change notification according to the review state;

[0047] starting a supply chain resource evaluation program after receiving the state change notification, and recalculating a supply chain resource configuration analysis result;

[0048] combining the supply chain resource configuration analysis result with historical sales data to perform demand forecasting, and generating a supply chain demand forecasting report;

[0049] based on the resource gap and optimization suggestions in the supply chain demand forecasting report, formulating an intelligent collaborative decision-making scheme.

[0050] In combination with the first aspect, in a ninth implementation manner of the first aspect of the present application, the combination of the supply chain resource configuration analysis result with historical sales data to perform demand forecasting and the generation of a supply chain demand forecasting report comprise:

[0051] fusing the supplier capacity change trend in the supply chain resource configuration analysis result with historical sales data of a sales and delivery module in the intelligent factory ERP system to obtain a comprehensive prediction data source;

[0052] based on the comprehensive prediction data source, performing time series analysis to obtain a multi-dimensional demand forecasting result;

[0053] comparing the multi-dimensional demand forecasting result with an actual capacity upper limit of a current supplier, a transport capacity configuration status of a carrier and a storage capacity present situation of a warehouse to identify resource constraint conditions;

[0054] according to the resource constraint conditions, formulating a supply chain demand forecasting report including supplementing high-priority suppliers, adjusting carrier transportation plans and re-planning warehouse space configuration.

[0055] The technical scheme provided by the application realizes the standardized and unified processing of the supplier Excel data, the carrier GPS track data stream and the warehouse RFID storage location data by establishing a data conversion mapping table, and eliminates the format difference and semantic conflict among the multi-source heterogeneous data. The supplier comprehensive scoring mechanism and the dynamic sorting list constructed based on the standardized business data realize the technical breakthrough from static management to dynamic qualification evaluation, and provide accurate supplier intelligent matching service for the procurement order. The comprehensive evaluation of the vehicle carrying capacity, the historical punctuality rate and the transportation cost of the carrier is realized through the multi-objective optimization algorithm, and the optimal carrier selection and the optimal transportation path are automatically generated. The warehouse pre-allocation mechanism based on the logistics scheduling plan realizes the advance planning and the space optimization configuration of the storage location. The distributed lock mechanism and the timestamp consistency verification ensure the real-time synchronous update of the business states of the supplier management, the logistics scheduling and the warehouse management three modules. The demand prediction algorithm combining the historical sales data and the seasonal change law realizes the intelligent change from passive execution to active prediction decision, and comprehensively improves the efficiency of the supply chain collaborative management. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical scheme of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating labor based on these drawings.

[0057] Figure 1 The step schematic diagram of the data real-time monitoring method based on the intelligent factory management platform system in the embodiments of the present application. DETAILED DESCRIPTION

[0058] The embodiments of the present application provide a data real-time monitoring method based on an intelligent factory management platform system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0059] For the convenience of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1An embodiment of the data real-time monitoring method based on the smart factory management platform system in the embodiment of the application comprises the following steps:

[0060] In step S11, the supplier Excel data in the smart factory ERP system, the GPS trajectory data stream of the carrier, and the warehouse RFID cargo location data are subjected to standard format conversion to obtain standardized business data.

[0061] It can be understood that the execution subject of the application can be a data real-time monitoring device based on the smart factory management platform system, and can also be a terminal or a server, and the specific execution subject is not limited herein. The embodiment of the application takes a server as an execution subject for example.

[0062] Specifically, the data interface module receives an Excel format file exported from a supplier management subsystem, which contains the name, code, tax number, address, credit rating, and other fields of multiple suppliers. Meanwhile, the communication protocol is connected to the vehicle terminal in the carrier dispatching system to collect the GPS trajectory data stream including the longitude, latitude, time stamp, and vehicle number in real time. The RFID reader is deployed to collect the tag data stream on the warehouse site to obtain information including the cargo location number, product code, and warehouse state. The three types of data are subjected to field-level difference analysis to construct a multi-dimensional field comparison model, identify the problems of field redundancy, non-standard naming, or inconsistent format in the supplier Excel data, identify the problems of longitude and latitude format difference, unit inconsistency, or time stamp coding inconsistency in the GPS trajectory data transmitted by different terminals, and identify the structural difference of the lack of direct mapping relationship between the tag ID and the actual product code in the RFID cargo location data to form a format difference information set. Based on the format difference information, a field-level standardization rule library is constructed, which includes a supplier code unification rule, a GPS coordinate standardization rule, and an RFID tag conversion rule. The supplier code unification rule defines a unified field naming method, character length limit, illegal character filtering, and redundant field merging rule according to the main data coding system in the ERP system. The GPS coordinate standardization rule formulates a unified floating point precision constraint, time format conversion scheme, and coordinate range legality verification logic for the longitude and latitude precision and time stamp format provided by different devices. The RFID tag conversion rule is used to establish a mapping relationship between the physical tag code read on site and the product barcode, cargo location number, and inventory data in the ERP system, and complete dynamic binding through the tag-code dictionary and location mapping table. The supplier code unification rule, the GPS coordinate standardization rule, and the RFID tag conversion rule are taken as a conversion engine to perform field replacement, format conversion, illegal value correction, and code completion through the data cleaning module to sequentially perform format standardization processing on the supplier Excel data, the carrier GPS trajectory data stream, and the warehouse RFID cargo location data. The converted results are written into a business standard data structure to form a standardized business data set.

[0063] Step S12, calculate the comprehensive score of the supplier based on the standardized business data and generate a dynamic ranking list;

[0064] Specifically, the basic profile information of the supplier in the standardized business data is parsed, such as enterprise name, supplier code, registered qualification, contact person and contact information, etc. At the same time, the basic profile information of the supplier is parsed, including commodity code, warehousing time, quantity, delivery cycle, warehousing inspection result and corresponding payment time node, etc. The historical transaction analysis module is called to extract the historical transaction details between each supplier and the platform from the purchase warehousing documents, construct a historical transaction record table, and calculate the on-time delivery frequency ratio based on the historical records. The matching situation of actual delivery time and planned delivery time is counted to evaluate the delivery compliance degree, and the data of the quality inspection link is extracted to judge the quality pass rate of the delivered goods. Combined with the matching results of the payment documents in the financial module, the credit indicators of the platform to the supplier are calculated, such as payment cycle and payment timeliness. On the basis of the above dynamic indicators, the static qualification fields recorded in the supplier profile information are introduced, including the enterprise qualification certification level input by artificial or synchronized by third-party authentication platform, such as ISO quality management system certificate, industry association record number, etc., and the price competition advantage parameter calculated by the ratio of commodity pricing strategy and industry average price level, to construct the supplier qualification information vector. Based on the supplier qualification information vector, the comprehensive score calculation is performed according to the multi-dimensional weighted model. The weight parameter is used to fuse the on-time delivery rate, quality pass rate, payment timeliness, qualification level and price competitiveness five indicators to obtain the comprehensive score value of each supplier. The comprehensive score results of all suppliers are uniformly filed and sorted in descending order according to the score. At the same time, the suppliers below the preset score threshold are filtered, only the suppliers with score higher than the threshold and meeting the cooperation conditions are retained, to form a real-time updated dynamic ranking list.

[0065] Step S13, receive the purchase order information and create a logistics scheduling plan combined with the dynamic ranking list, and pre-allocate the optimal storage location for the warehousing goods to obtain a warehouse pre-allocation scheme;

[0066] Specifically, the approved purchase order information is received in real time by the purchase order processing module, and the key fields contained in the order are parsed, including the supplier address coordinates, the volume and weight parameters of various goods, and the upper limit requirement of the delivery time, while analyzing the transportation conditions attached to the order goods, such as temperature control requirements, fragile labels, and stacking restrictions, to automatically generate structured order transportation demand information. The order transportation demand information is linked and matched with the dynamic ranking list of suppliers, the geographical coordinates of all suppliers in the ranking list are extracted, and the relationship between the delivery endpoint of the current order and the address location of each supplier is compared through a spatial distance calculation algorithm, combined with the performance of the supply capacity of the goods categories and quantities in the historical records, the suppliers with acceptable distance range, sufficient performance and high score are selected to build a candidate supplier set. The carrier resource list associated with each supplier in the candidate supplier set is analyzed, including the types of vehicles that can be called, the current location of the vehicle, the load capacity, the GPS trajectory history, the scheduled plan and the remaining scheduling period, etc. Based on the transportation path planning module, the shortest path and minimum time consumption of the road network from the supplier to the warehouse are analyzed, while considering the traffic congestion probability and route passability, the matching degree of the routes and resources provided by different carriers is compared, and the carrier and corresponding path that achieves the optimal balance among time cost, transportation capacity and path stability are selected to form the optimal transportation path option. The vehicle scheduling information of the optimal carrier and the time node arrangement of the selected path are integrated, and scheduling parameters such as loading time, departure time, and estimated arrival time are set, and the required loading resources are recorded to form a logistics scheduling plan. According to the expected arrival time in the logistics scheduling plan and the warehouse information, the occupancy state and historical turnover rate data of the current warehouse in the warehouse management module are retrieved, the available capacity, suitable goods categories, accessibility path length, and partition priority of each storage location are identified, and the use efficiency and operation convenience of the storage location are comprehensively evaluated. For goods with large volume and frequent outbound, high-frequency storage locations close to the inbound channel or main operation channel are preferentially matched; for goods with long storage period and low operation frequency, they are placed in the deep or edge low-priority storage locations. Through the storage location allocation optimization algorithm, all inbound goods are matched and bound to form a warehouse pre-allocation scheme covering all purchase order goods.

[0067] Step S14, real-time monitoring of the execution progress of the warehouse pre-allocation scheme, and synchronous updating of the business state information;

[0068] Specifically, in the warehouse management module, the occupancy status of the pre-allocated storage location and the completion progress of the goods warehousing are continuously tracked, the actual warehousing quantity, the current occupancy identifier and the warehousing completion rate of each target storage location are obtained in real time by calling the storage location allocation and inventory warehousing interface, and the deviation between the pre-allocation scheme and the actual execution record is compared to establish a dynamic execution progress model. At the same time, the delivery status updates from the supplier management module are received, such as whether to ship, delivery time and delivery quantity, etc., and the arrival status of the vehicle in the logistics scheduling module is captured in real time, including whether the GPS positioning enters the specified area, the difference between the expected and actual arrival time, and other key data, and the state change events with time stamps are recorded uniformly. All events are received and processed by a unified state change processor, the time stamps of events from different sources are conflict detected, if there is a situation that the logic of the first and the second does not match, the event reordering mechanism and the source priority strategy are called to execute correction. After the timing adjustment is completed, consistency verification is performed between the supplier shipping status, the transportation status and the warehouse warehousing status, to ensure that the state logic of the cross-module matches each other, to avoid the problem of state misplacement or data lag, and to generate a synchronous state verification result accordingly. The platform updates the supplier performance field, the transportation task status field and the warehouse storage occupancy field in synchronization according to the synchronous state verification result through a unified interface.

[0069] Step S15, based on the business state information, the audit state of the purchase order information is monitored, and an intelligent collaborative decision scheme is generated according to the audit state.

[0070] Specifically, the purchase order audit field in the business status information is continuously monitored in the background task thread, including whether the order is audited, the audit time, the auditor, and the audit note, etc. When the audit state changes, for example, from "unaudited" to "audited" or from "audited" to "unaudited", a state change notification is generated and sent to the supply chain collaborative processing module through the event-driven mechanism. After receiving the state change notification, the supply chain collaborative processing module automatically starts the supply chain resource assessment program, reacquires the core resource data such as warehouse capacity, transportation scheduling resources, and supplier inventory related to the purchase order at the current time point, and performs resource availability analysis and scheduling conflict detection combined with the multi-dimensional constraint conditions configured in the system, to generate the latest resource configuration analysis result. After completing the resource assessment, the historical data analysis engine is called to fuse the latest resource configuration result with the historical sales records, extract the seasonal demand fluctuations, key category trend changes, and periodic purchasing behaviors, generate short-term and medium-term supply chain demand prediction reports through a time series prediction model, including the prediction of the quantity of various types of goods in the future, and the matching difference between the current resource distribution and the predicted demand, clearly pointing out the potential resource gap area, redundant configuration problem, and optimization path. According to the analysis conclusion and optimization suggestion in the supply chain demand prediction report, the platform constructs an intelligent collaborative decision-making scheme, including supplier recommendation sorting adjustment, carrier resource redistribution suggestion, temporary warehouse expansion plan, and emergency replenishment or order postponement strategy, etc., so that each business module can quickly adjust the strategy according to the actual audit state change.

[0071] In a specific embodiment, the process of performing step S11 can specifically include the following steps:

[0072] Extracting supplier Excel data, carrier GPS trajectory data stream, and warehouse RFID location data in the smart factory ERP system;

[0073] Performing difference analysis on the supplier information field in the supplier Excel data, the location coordinate field in the carrier GPS trajectory data stream, and the location code field in the warehouse RFID location data, respectively, to obtain format difference information;

[0074] Creating supplier code unification rules, GPS coordinate standardization rules, and RFID tag conversion rules based on the format difference information;

[0075] Based on the supplier code unification rules, GPS coordinate standardization rules, and RFID tag conversion rules, converting the supplier Excel data, carrier GPS trajectory data stream, and warehouse RFID location data into standardized business data.

[0076] Specifically, a data communication mechanism is established with each subsystem through a preset data acquisition interface. The supplier Excel data is derived from the SRM system or manually maintained basic archives, the carrier GPS trajectory data stream is uploaded in real time by the accessed TMS platform through satellite positioning equipment, and the warehouse RFID location data is collected by the RFID read-write equipment deployed in the WMS system at regular intervals and stored in the warehouse. A data extraction engine is used to access the three types of heterogeneous sources, analyze the original structure and field content carried by each of them, such as the supplier number, company name, tax identification number, address, and contact information in the supplier Excel, the longitude, latitude, timestamp, and vehicle number in the GPS trajectory data, and the tag number, bound product barcode, location code, and storage status in the RFID data. Through a difference analysis module, the field level of different data structures is compared. The supplier information field in the supplier Excel data is compared with the standard supplier master data structure defined in the ERP system to identify inconsistencies in field naming, type mismatch, character set differences, or redundant fields. For GPS trajectory data, analyze whether the position coordinate field uses a unified coordinate system (such as WGS-84) and whether the time field is standardized to ISO timestamp format, and verify whether the coordinate accuracy meets the positioning requirements. The RFID data part is parsed and compared with the standard location number in the warehouse system to identify whether there are differences in label format, missing binding information, or inconsistent coding rules. By analyzing the structure attributes, data length, value range, and coding method of the above fields, a format difference information table is generated. Based on the format difference information, a unified data standardization rule is constructed. For supplier information, a supplier code unification rule is established to specify the bit requirement of the supplier number, the prefix format, the character coding specification, and the illegal character filtering logic. For GPS trajectory data, a GPS coordinate standardization rule is developed to convert all coordinates to a unified coordinate system and standardize the time format to millisecond-level UTC timestamp, while setting the valid precision range. For RFID location data, an RFID tag conversion rule is constructed to associate the tag number with the internal location code mapping table, unify the tag information structure, and ensure that each tag corresponds to a unique location in the warehouse. The standardization conversion engine performs field mapping, format conversion, and content verification operations on the original data according to the above three types of rules, processes all conversion fields one by one, converts the supplier Excel data, GPS trajectory data, and RFID tag data into standardized business data formats with unified structure and consistent semantics, and writes them into the system master data, realizing the standardized fusion of cross-system heterogeneous data.

[0077] The field conversion operation in the multi-source data conversion mapping table is performed to convert the supplier Excel data, the carrier GPS trajectory data stream and the warehouse RFID storage location data into standardized business data, including: establishing an ERP business association graph with a supplier information node, a carrier scheduling node and a warehouse management node as cores, constructing a main business link with a purchase requisition as a starting node, a purchase order as an intermediate node and a purchase warehouse-in order as a terminating node, and simultaneously creating a supplier qualification evaluation subgraph, a carrier scheduling state subgraph and an inventory safety early warning subgraph to obtain a three-dimensional stereoscopic business association network; based on the three-dimensional stereoscopic business association network, semantic analysis is performed on enterprise names, contact information and tax rate information in the supplier Excel data, the business meanings and association relationships of the data fields are identified, association mapping rules of the supplier information and the purchase order and the payment record are established, and a supplier data semantic mapping result is obtained; real-time semantic analysis is performed on longitude and latitude coordinates, time stamps and vehicle numbers in the carrier GPS trajectory data stream, the association relationship between the position data and the transportation task is established in combination with the business logic of route management and vehicle scheduling, the transportation distance and the estimated arrival time are calculated through a geographic information system algorithm, and a carrier data semantic mapping result is obtained; the business semantics of label codes, storage location coordinates and storage capacity information in the warehouse RFID storage location data are analyzed, the association mapping relationship between the storage location information and the product inventory and the safety early warning is established, and the storage location accessibility and the access convenience score are calculated through a warehouse layout algorithm to obtain a warehouse data semantic mapping result; the supplier data semantic mapping result, the carrier data semantic mapping result and the warehouse data semantic mapping result are integrated, the data logic consistency is verified through the node relationship of the business association graph, the cross-module data conflicts are eliminated, and a unified data identification system is established to obtain a business data model with unified semantics; based on the business data model with unified semantics, automatic data format conversion is performed to convert the heterogeneous data sources into an ERP system standard data structure, the integrity and accuracy of the original business semantics are maintained, and standardized business data is obtained.

[0078] In a specific embodiment, the process of performing step S12 can specifically include the following steps:

[0079] Analyzing the basic profile information of the supplier and the purchase warehouse-in document in the standardized business data, and extracting the historical transaction records of each supplier based on the basic profile information and the purchase warehouse-in document;

[0080] Counting the number of on-time deliveries, the quality pass rate and the payment timeliness rate in the historical transaction records, and obtaining the qualification information of the supplier in combination with the qualification certification level and the price competitive advantage of the supplier;

[0081] Calculating the comprehensive score of the supplier based on the qualification information, and screening the suppliers whose comprehensive scores exceed a preset score threshold to generate a dynamic ranking list.

[0082] Specifically, the standardized business data is classified and parsed by the master data management module to extract the supplier basic profile information and the procurement and warehousing documents. The supplier basic profile information includes the fields of supplier code, enterprise name, geographical location, contact person, bank of opening account, tax rate configuration, qualification level, and contract validity period in a unified format; the procurement and warehousing documents record the detailed data of commodity code related to the supplier, actual delivery time, planned delivery time, warehousing inspection result, corresponding procurement order number, and warehousing amount. The procurement and warehousing documents are data-associated with the supplier code, and the mapping relationship of supplier-order-warehousing is established to arrange the historical transaction record set of each supplier. The key indicators in the transaction process are calculated and archived by the statistical analysis engine. Among them, the on-time delivery times are compared according to the planned delivery time and the actual delivery time of each procurement and warehousing document, and if the delivery is completed within the specified time range, it is recorded as one on-time delivery, and the proportion of on-time delivery times to total delivery times is output; the quality pass rate is determined by the warehousing inspection result, and the qualified quantity and the total quantity of each batch are extracted from the warehousing record to calculate the corresponding inspection pass rate of each supplier; the payment timeliness is counted by the payment document in the financial module, and the actual payment time in the payment document is compared with the payment time corresponding to the procurement and warehousing document to determine whether each payment is executed on time, and the proportion of the payment number executed on time to the total payment number is taken as the evaluation indicator. At the same time, the qualification certification level and the price competition advantage index in the supplier profile information are added to the evaluation system. The qualification certification level is determined by whether the enterprise has ISO9001, ISO14001, industry special qualification, or recognized qualification, and is converted into a standard score according to the level; the price competition advantage is measured by comparing the average purchase price of the supplier's goods with the historical average purchase price of the same kind of goods in the system, using a ratio model to measure its advantage or disadvantage in the price dimension, and performing standardization processing. Based on the on-time delivery rate, the quality pass rate, the payment timeliness, the qualification level score, and the price competition advantage coefficient, a weighted comprehensive calculation model is used to generate a unique comprehensive score value for each supplier. All scoring results are archived after completion, sorted in descending order, and all suppliers with a comprehensive score exceeding a preset score threshold are selected to generate a real-time updated dynamic ranking list.

[0083] In a specific embodiment, the process of performing step S13 can specifically include the following steps:

[0084] Receiving and parsing the supplier address coordinates, commodity volume and weight parameters, and delivery time requirements in the procurement order information, and generating order transportation demand information in combination with the transportation requirements of the order commodities in the procurement order information;

[0085] The order transportation demand information is matched with the distance of the supplier geographical position in the dynamic sorting list, the suppliers with reasonable transportation distance and the supply capacity meeting the order demand are screened, and a candidate supplier set is obtained;

[0086] The carrier resource list corresponding to the candidate supplier set is traversed, and the optimal carrier and the optimal transportation path are selected from the carrier resource list;

[0087] The vehicle scheduling information of the optimal carrier and the time node arrangement of the optimal transportation path are integrated to obtain a logistics scheduling plan;

[0088] According to the logistics scheduling plan, the warehouse inventory state is analyzed, the optimal storage space is pre-assigned for the incoming goods, and a warehouse pre-assignment scheme is obtained.

[0089] Specifically, in the procurement management module, the audited procurement order data is received, and the key fields in the order are structured and parsed. The supplier address coordinate information is extracted from the order, and the physical parameters such as the volume, weight, packaging method, and stacking characteristics of each order item are parsed, and combined with the required delivery time node, delivery window length, and whether it is a time-sensitive item, etc., to generate a standardized delivery time requirement description. At the same time, the transportation requirements attached to each item are extracted, such as whether cold chain transportation is required, whether it is a dangerous goods, whether it can be mixed with other materials, etc., to form an order transportation demand information structure. Based on the order transportation demand information, a geographical location matching operation is performed on all suppliers in the dynamic sorting list. By comparing the address coordinates of each supplier in the sorting list with the geographical location of the target delivery warehouse, the straight-line distance and the shortest path length are calculated, and the current inventory, historical response time, and recent performance record are cross-verified through the supply capacity data to filter out suppliers that meet the transportation distance threshold, timely delivery, and high commodity matching degree, and build a candidate supplier set. Traverse the list of carrier resources associated with each supplier in the candidate supplier set, which includes the number of available vehicles, vehicle load specifications, current vehicle status, vehicle location, and estimated available time, etc. Based on the volume and weight of the order items, exclude carriers that do not have the capacity to transport, then call the route planning engine to evaluate multiple optional paths for each carrier from the supplier to the target warehouse, considering historical traffic efficiency, traffic congestion probability, road grade, driving restrictions, and cost factors, and comprehensively evaluating the timeliness and economy of the path, to determine the optimal carrier and the corresponding optimal transportation path. Integrate the vehicle scheduling information of the optimal carrier and the time node arrangement of the optimal transportation path, combine the estimated loading time, travel time, arrival window, and warehouse scheduling period to generate a specific logistics scheduling plan, and push the scheduling plan to the carrier module and warehouse module for joint execution. Based on the expected arrival time, item list, and carrier number in the logistics scheduling plan, call the warehouse management module's bin optimization model to analyze the current inventory status, including bin occupancy rate, spatial distance between each bin and the loading port, classification attributes of compatible item types, etc., and combine the item storage requirements to screen and score the bins. According to the bin score results, pre-allocate the optimal bin for each type of incoming item to generate a warehouse pre-allocation plan.

[0090] The process involves integrating vehicle dispatching information from the optimal carrier with the timeline of the optimal transportation route to obtain a logistics dispatching plan. This includes: extracting the maximum load capacity, fuel consumption rate, driver working hours, and vehicle positioning device number from the optimal carrier's vehicle files; combining this with the vehicle's current location coordinates and remaining load capacity to establish a real-time monitoring database of vehicle resource status, thus obtaining basic vehicle dispatching data; calculating the empty travel time from the vehicle's current location to the supplier's address and the fully loaded transport time from the supplier's address to the company's warehouse based on the basic vehicle dispatching data and the optimal transportation route; considering the impact of traffic conditions, weather factors, and road restrictions on transport time to obtain accurate transport time predictions; and scheduling key time nodes such as vehicle departure time, supplier arrival time, loading completion time, and expected warehousing time using a time window constraint algorithm based on the accurate transport time predictions and the delivery time requirements in the purchase order to ensure smooth transportation. The timing of the planning and procurement plans is matched to obtain a time-node arrangement scheme. Combining the time-node information in the arrangement scheme, the estimated fuel consumption, toll fees, driver wages, and other transportation costs are calculated. A cost control algorithm is used to optimize loading schemes and route selection to reduce overall transportation costs, resulting in a cost-optimized transportation scheme. Based on this cost-optimized scheme, a real-time vehicle tracking mechanism is established. GPS positioning systems monitor vehicle trajectories, speed changes, and estimated arrival times. When transportation delays or route deviations are detected, subsequent time arrangements are automatically adjusted and relevant business modules are notified, resulting in a dynamic scheduling control scheme. Integrating vehicle allocation information, time-node arrangements, cost budget control, and real-time tracking mechanisms from the dynamic scheduling control scheme forms a complete transportation organization scheme that includes transportation task allocation, time schedule management, cost budgeting, and abnormal situation handling, resulting in a logistics scheduling plan.

[0091] In one specific embodiment, the process of traversing the carrier resource list corresponding to the candidate supplier set and selecting the optimal carrier and optimal transportation route from the carrier resource list can specifically include the following steps:

[0092] Extract the carriers associated with each candidate supplier from the candidate supplier set to obtain a list of carrier resources;

[0093] Calculate each carrier's vehicle load capacity, historical on-time rate, and transportation cost based on the carrier's resource list;

[0094] The vehicle's load capacity, historical on-time rate, and transportation cost are input into a multi-objective optimization algorithm for weight allocation and comprehensive scoring, resulting in multiple qualified carriers.

[0095] The optimal carrier with the highest overall score is selected from multiple qualified carriers, and the optimal transportation route from the supplier's address to the company's warehouse is calculated.

[0096] Specifically, the carrier information bound with each candidate supplier is called based on the supply chain collaboration database, the carrier codes, carrier vehicle resources, line coverage areas, transportation types and scheduling frequencies and other data involved in the cooperation history records are extracted one by one by establishing a supplier-carrier mapping relationship table, and a carrier resource list covering all candidate suppliers is constructed. In the carrier resource list, each carrier item contains a group of registered and schedulable transportation resources, including vehicle type, vehicle number, vehicle current state, current loading condition and earliest schedulable time, etc. Based on the transportation scheduling module, the carrier resource list is processed item by item, the theoretical load capacity and average load redundancy rate of all vehicles in each carrier are counted, and combined with the overall volume and weight of the order goods, the carriers without carrying capacity or insufficient schedulable vehicles are screened out. Query the historical transportation records, extract the comparison data of the task completion time and the planned arrival time of the carrier in the near one statistical cycle, calculate the historical punctuality rate of each carrier, and perform weighted smoothing processing according to the day, week, month and other cycles to form the time efficiency performance index. At the same time, the transportation cost parameters of each carrier are standardized modeling, including single-kilometer cost, average fuel consumption of different transportation areas, toll fees and scheduling additional fees, to get the average transportation cost per unit distance. The vehicle load capacity, historical punctuality rate and transportation cost of each carrier are taken as input variables to construct a three-dimensional evaluation space, and input into a multi-objective optimization algorithm for comprehensive scoring. A set of adjustable weight factors are preset to allocate weights to the three types of indexes, for example, the weight of the punctuality rate can be increased for businesses with high transportation time requirements, and the proportion of the cost parameter can be increased for cost-sensitive orders. The multi-objective optimization module takes the linear weighting model or the normalized evaluation function as the calculation method, outputs the normalized scores of all carriers, and removes the carriers with scores lower than the acceptable threshold, and retains a set of qualified carriers with comprehensive scores meeting the standards. Based on the set of qualified carriers, the carriers are ranked from high to low according to the comprehensive scores, and the carrier with the highest score is selected as the optimal carrier for the current order. The transportation route of the optimal carrier is planned, the supplier address coordinates and enterprise warehouse coordinates are combined, the path calculation engine is used to evaluate the time, distance, cost and congestion probability of all feasible paths on the map model, the weighted shortest path algorithm is used for evaluation, the optimal transportation path is determined, and the path information, time nodes and carrier vehicle number are summarized as structured output results.

[0097] In a specific embodiment, the process of performing steps of analyzing the warehouse inventory status according to the logistics scheduling plan, pre-allocating the optimal storage location for the incoming goods, and obtaining the storage pre-allocation scheme can specifically include the following steps:

[0098] The expected arrival time, the name of the warehousing commodity, the quantity and the volume weight parameter in the extract flow scheduling plan are analyzed, the storage temperature requirement and the shelf life constraint condition of the order commodity in the purchase order information are analyzed, and the commodity storage demand information is obtained;

[0099] The current inventory, the safety inventory threshold and the reserved inventory data corresponding to the commodity storage demand information in the warehouse management module are inquired, and the warehouse inventory state is obtained;

[0100] The occupancy and the storage capacity of each storage location in the warehouse inventory state are scanned, the storage location capacity matching degree is calculated in combination with the volume requirement of the warehousing commodity, the storage location options meeting the storage condition and the location convenient for storage and retrieval are screened, and the candidate storage location list is obtained;

[0101] The distance and the storage and retrieval convenience of each storage location in the candidate storage location list to the warehouse outlet are analyzed, the classified storage location allocation is performed according to the turnover frequency of the warehousing commodity, and the warehouse pre-allocation scheme is obtained.

[0102] Specifically, the system extracts core fields from the logistics scheduling plan, such as estimated arrival time, name of incoming goods, quantity, volume, and weight parameters. Through structured association with purchase order information, it analyzes the storage environment requirements listed in the order, including minimum and maximum storage temperatures, whether constant temperature, refrigeration, or freezing is required, and key constraints such as shelf life in days or expiration date after production. By structuring and summarizing these parameters, a storage requirement information set corresponding to each type of goods is formed. Real-time inventory data from the warehouse management module is called to extract the current inventory quantity, safety stock threshold, and reserved inventory data for the warehouse area matching the goods' storage requirements. The available inventory space and remaining replenishment quantity for each type of goods are calculated, establishing a mapping of the current warehouse inventory status. The system scans the real-time occupancy information of each storage location in the inventory status, including allocated but not yet received locations, locked locations, and remaining available capacity for received goods. Combining this with the volume parameters and weight attributes of the incoming goods, the system performs a storage location capacity fit calculation. By comparing the volume of goods with the maximum remaining volume of storage locations, the volume ratio and load-bearing capacity ratio of each storage location are calculated. Storage locations with insufficient space or exceeding load limits are eliminated. Simultaneously, it is verified whether the temperature zone of the storage location meets the temperature control requirements of the goods and whether it supports a management model of outbound shipment in reverse chronological order of shelf life. This process filters out a set of storage locations that meet all environmental and capacity constraints, forming a candidate storage location list. Based on the candidate storage location list, a warehouse map model is used to calculate the path distance between each candidate storage location and the outbound exit. Factors such as the congestion level of the aisle where the storage location is located, forklift accessibility, and feasibility of manual handling are analyzed to quantify the accessibility score of each storage location. The accessibility score is weighted by the actual path length, number of handling operations, and average operation time to form the accessibility evaluation parameters. Simultaneously, the outbound frequency of goods in historical inbound records is incorporated to classify all inbound goods into ABC turnover categories. High-frequency A-category goods are prioritized for allocation to efficient storage locations near the outbound exit with direct access paths; B-category goods are allocated to medium-distance storage locations; and low-turnover C-category goods are assigned to secondary storage locations further from the main aisle but with high utilization rates. The output is a warehouse pre-allocation scheme containing the product name, allocated storage location number, estimated inbound time, and storage constraints.

[0103] In one specific embodiment, the process of performing step S14 may specifically include the following steps:

[0104] Monitor the execution status of warehouse location allocation and the progress of goods warehousing in the warehouse pre-allocation plan, and capture the execution progress of warehouse location occupancy status in the warehouse management module;

[0105] Receive execution progress and capture status change events of supplier delivery status in the supplier management module and vehicle arrival status in the logistics scheduling module;

[0106] The timestamp conflict in the processing state change event is handled and state consistency verification is performed to obtain a synchronization state verification result;

[0107] The synchronization state verification result is applied to update the business state information of the supplier management module, the logistics scheduling module and the warehouse management module.

[0108] Specifically, a unique identifier is established for each storage space allocation instruction in the warehouse pre-allocation scheme, and the execution progress of the storage space allocation instruction is tracked in real time in combination with the warehouse operation system. In each warehousing operation, the occupancy state of each storage space is updated in real time according to the commodity scanning data, the RFID tag identification result or the manual confirmation record, including whether the warehousing has started, the current completed warehousing quantity, the remaining quantity to be warehoused and the completion percentage, so as to continuously obtain the storage space allocation execution and the actual progress of the commodity warehousing, and synchronously upload the progress information to the state monitoring interface of the warehouse management module. At the same time, the delivery state change events such as the supplier has shipped, the partial delivery, the delivery exception and the like from the supplier management module are received, and each state change is bound to the corresponding purchase order; the vehicle arrival state events from the logistics scheduling module are captured, and whether the vehicle has arrived at the preset warehouse position, whether the unloading operation is completed, whether the GPS coordinate is in the specified area and the like are monitored. All these events contain timestamp information, data source module, event type and trigger condition. In order to ensure the uniformity of the cross-module state record, an event bus is constructed to receive and centrally process these state change events. In the centralized processing stage, the timestamp conflict detection is performed on all the state change events to judge whether there is an error in the sequence of the events caused by network delay, clock error or multi-system asynchronous update. If the conflict is detected, the event reordering mechanism is enabled, the events are rearranged according to the timestamp and the business logic, and the time sequence consistency of the state update is ensured. After the sorting is completed, the state consistency verification is performed to check whether the warehouse warehousing state is logically matched with the supplier delivery state and the vehicle arrival state, to ensure that the business rules such as “not delivered cannot be warehoused” and “not arrived cannot be unloaded” are not destroyed, and a synchronization state verification result is generated. According to the verification result, a state update command is automatically pushed to update the performance state field in the supplier management module, the transportation task state field in the logistics scheduling module and the execution state of the storage space allocation and the warehousing task in the warehouse management module.

[0109] The application synchronously updates the business state information of the supplier management module, the logistics scheduling module and the warehouse management module based on the synchronization state verification result, including: performing quality evaluation processing on the supplier basic file integrity in the supplier management module, the GPS coordinate data accuracy in the logistics scheduling module and the RFID storage location data validity in the warehouse management module based on the synchronization state verification result, calculating the comprehensive score of the data integrity index, the accuracy index, the consistency index, the timeliness index and the validity index, and obtaining a data quality evaluation report; detecting data items below the preset quality threshold in the data quality evaluation report, identifying data anomaly types such as missing supplier information fields, abnormal GPS coordinate offset and RFID tag reading error, grouping the detected data problems according to missing data, abnormal data and inconsistent data through an abnormal data classification algorithm, and obtaining a data anomaly classification result; starting a historical data completion algorithm according to the missing data type in the data anomaly classification result, extracting historical valid records of the same supplier or similar carriers for data speculation filling, starting a data correction algorithm for the abnormal data type to verify the reasonable range of GPS coordinates and the specification of RFID tag codes, and obtaining a data repair processing scheme; performing automatic repair operations in the data repair processing scheme, and restoring the business state to the historical state point that passed the last data consistency verification through a data rollback mechanism for key data items that cannot be automatically repaired, triggering data reacquisition and synchronization processes, and obtaining repaired business data sets; recalculating the business state consistency of the three modules of the supplier management, the logistics scheduling and the warehouse management based on the repaired business data sets, verifying the data repair effect and recording repair operation logs, confirming data repair success when all data quality indexes meet the preset standards, and obtaining quality qualified business state data; synchronously updating the quality qualified business state data to the supplier file library of the supplier management module, the carrier state library of the logistics scheduling module and the storage location information library of the warehouse management module, establishing a data quality monitoring and early warning mechanism to continuously track subsequent data changes, and obtaining updated business state information.

[0110] In a specific embodiment, the process of processing timestamp conflicts in state change events and performing state consistency verification to obtain a synchronization state verification result can specifically include the following steps:

[0111] Parsing the timestamp identifiers of the operation records of the supplier management module, the logistics scheduling module and the warehouse management module in the state change event, and identifying timestamp conflicts based on the timestamp identifiers;

[0112] According to the shared resource number involved in the timestamp conflict, applying for the corresponding distributed lock control authority to the smart factory ERP system;

[0113] read the current business state data of the supplier management module, the logistics scheduling module and the warehouse management module during holding the distributed lock control right, and generate a state consistency verification result based on the current business state data;

[0114] based on the state consistency verification result, perform data rollback or re-synchronization operation on inconsistent state information, and after completion, release all distributed lock resources in turn to obtain a synchronization state verification result.

[0115] Specifically, in the event bus, state change events from the supplier management module, the logistics scheduling module, and the warehouse management module are received, each event containing a timestamp identifier of a corresponding operation record, an event type, a module marker to which the event belongs, and a business resource number involved. Through a unified timestamp analysis engine, the event times submitted by multiple modules are logically compared to identify whether there is a timestamp conflict of the same resource generated in different modules, such as a same purchase order being marked as “delivered” in the logistics module but still “not warehoused” in the warehouse module, or a supplier fulfillment state having changed to “delivery completed” but the warehouse side having not yet received the goods, etc. The resource numbers in these conflict events are extracted and normalized. After discovering the timestamp conflict, a lock application is initiated to the distributed lock management module of the smart factory ERP system according to the shared resource number involved in the conflict, such as a purchase order number, a transport order number, or a storage location number. The lock control mechanism is based on an optimistic concurrency model or a distributed middleware such as Redis, and allocates a unique lock identifier to each business resource, judges whether there is a competitor holding the lock of the business resource through a centralized scheduler, grants the current requester the lock permission if the resource is idle, and sets the lock expiration time and operation timeout mechanism. After holding the lock control permission, the state reading, judging, and modifying operations on the business resource during the entire locking period are exclusive, ensuring the atomicity of the state operations among modules. After obtaining the distributed lock, the current business state data is read from the supplier management module, the logistics scheduling module, and the warehouse management module, and the state fields related to the conflict events are extracted, such as the supplier delivery identifier, the vehicle arrival time, the storage location occupation state, and the warehousing completion rate, etc., and a cross-module state dependency relationship graph is constructed according to the process logic model. The current states of each module are compared on the time axis to determine whether there is a process break, an update lag, or a logic inconsistency between the states, and a verification result is generated according to the pre-defined state consistency verification rules. According to the state consistency verification result, the data processing module is automatically called to repair the inconsistent state information. For identifiable delay problems, the state refresh operation of the target module is triggered through a re-synchronization interface; for data abnormalities caused by operation sequence errors or illegal state updates, the state rollback mechanism is called to restore to the last confirmed consistent state snapshot. All state repair or synchronization operations are executed atomically under the current distributed lock control, ensuring that they are not disturbed by other concurrent tasks in the middle. After the state consistency of all conflict resources is repaired, all held distributed lock resources are released in turn, and operation logs and repair summaries are recorded, and a synchronization state verification result is generated.

[0116] In a specific embodiment, the process of performing step S15 can specifically include the following steps:

[0117] continuously monitoring the audit state of the purchase order information from the business state information, and generating a state change notification according to the audit state;

[0118] initiate a supply chain resource assessment procedure, recalculate the supply chain resource configuration analysis results;

[0119] combine the supply chain resource configuration analysis results with historical sales data to make demand forecasts, and generate a supply chain demand forecast report;

[0120] based on the resource gaps and optimization suggestions in the supply chain demand forecast report, develop an intelligent collaborative decision-making scheme.

[0121] Specifically, based on the business state monitoring engine in the smart factory ERP platform, the audit state field of the purchase order in the purchase management module is continuously monitored, and a state change trigger mechanism bound thereto is constructed. The state change trigger mechanism detects the state transition process of the purchase order from "unaudited" to "audited" or from "audited" to "unaudited" and automatically records the time stamp, operator, order number, and audit notes and other key information. When the state change is identified, a structured state change notification event is generated, including the audit action type, the state before and after the change, the trigger time, and the preliminary classification of the affected resources, and is pushed to the supply chain collaborative control engine through the event bus. After receiving the state change notification, the supply chain resource assessment procedure is activated synchronously, locking the corresponding product code, planned arrival time, warehouse, and supplier information of the purchase order, and calling the current supplier's available delivery period, historical on-time delivery record, and current warehouse capacity, storage utilization rate, and in-transit inventory status. Through the fusion of resource information among the supplier management module, the logistics scheduling module, and the warehouse management module, a cross-department, multi-dimensional supply chain resource configuration model is established, and the overall resource carrying capacity, short-term conflict risk, and adjustable space after the introduction of this purchase order are recalculated. A set of data structures covering warehouse, transportation, and supply capacity assessment are generated, and resource configuration analysis results are obtained. The sales analysis engine is called to fuse and operate the current resource configuration data with historical sales data. By introducing sales periodic fluctuations, commodity seasonal demand curves, and specific customer large order probability models, and combining machine learning regression or time series prediction algorithms, the future demand trend of materials within a certain period is simulated and predicted, and a supply chain demand forecast report is generated, which contains the inventory change trend after the addition of materials based on the purchase order, and gives the predicted inventory consumption speed, future critical inventory nodes, types of tight resources, and time windows of resource bottlenecks. Based on the resource gaps and structural optimization suggestions listed in the supply chain demand forecast report, the intelligent collaborative algorithm module is called, and the supplier switching priority, carrier route re-planning, warehouse space reallocation strategy, and cross-warehouse allocation feasibility are considered comprehensively to develop an intelligent collaborative decision-making scheme, which includes supplier replacement recommendations, purchase batch splitting, pre-generation of allocation orders, carrier vehicle supplement suggestions, delayed receipt schemes, and dynamic adjustment strategies for safety stock, etc.

[0122] The intelligent collaborative decision-making scheme is formulated based on the resource gap and optimization suggestions in the supply chain demand prediction report, including: monitoring the audit event of the purchase order in the ERP system from the "to be audited" state to the "audited" state, extracting the timestamp, commodity category, quantity specification and supplier information of the order change as the decision trigger condition to start the prediction decision network algorithm to obtain the order audit trigger signal; based on the order audit trigger signal, automatically activating the optimal path calculation module of the TMS system and the warehouse space pre-allocation module of the WMS system, establishing a multi-dimensional constraint optimization model through the matching constraint of purchase quantity and transportation capacity, the balance constraint of transportation capacity and inventory capacity, and obtaining the resource allocation constraint equation group; solving the supplier capacity allocation variable, the carrier capacity allocation variable and the warehouse space occupation variable in the resource allocation constraint equation group, and finding the optimal solution under the premise of meeting the delivery time constraint, cost control constraint and quality standard constraint through the linear programming algorithm to obtain the resource allocation optimization scheme; inputting the resource allocation optimization scheme and the historical purchase data, sales and delivery data, inventory turnover data in the ERP system into the machine learning prediction algorithm to train the supplier response time prediction model, the transportation path optimization model and the inventory demand prediction model to obtain the business prediction model set; using the business prediction model set to predict and calculate the supplier supply capacity, carrier transportation demand and warehouse space demand in the next 3 months to identify potential supply risk points, logistics bottleneck points and inventory shortage points to obtain forward-looking risk early warning results; based on the forward-looking risk early warning results, formulating comprehensive countermeasures including alternative supplier activation strategies, transportation route adjustment schemes and inventory safety replenishment plans, establishing a decision support mechanism from passive response to active prediction, and obtaining the intelligent collaborative decision-making scheme.

[0123] In a specific embodiment, the process of performing the step of combining the supply chain resource configuration analysis results with the historical sales data for demand prediction to generate a supply chain demand prediction report can specifically include the following steps:

[0124] Fusing the supplier capacity change trend in the supply chain resource configuration analysis result and the historical sales data of the sales and delivery module in the intelligent factory ERP system to obtain a comprehensive prediction data source;

[0125] Based on the comprehensive prediction data source, time series analysis is performed to obtain a multi-dimensional demand prediction result;

[0126] Comparing the multi-dimensional demand prediction result with the actual capacity upper limit of the current supplier, the capacity allocation status of the carrier and the storage capacity status of the warehouse to identify resource constraint conditions;

[0127] According to the resource constraint conditions, a supply chain demand prediction report is formulated, including supplementing high-priority suppliers, adjusting carrier transportation plans and re-planning warehouse space allocation.

[0128] Specifically, dynamic indicators related to supplier capacity are extracted from the supply chain resource configuration analysis results, including capacity change trend, production cycle fluctuation, past seasonal load peak, and scalable capacity boundary, and the dynamic indicators are entered into the prediction model as core production capacity parameters. At the same time, historical sales data in the past one to two years are obtained from the sales and delivery module of the smart factory ERP system, and are classified and arranged through order structure, commodity dimension, and time axis sequence to generate standardized sales behavior data stream. Through the horizontal fusion of sales data and supplier capacity trend, a corresponding relationship is established for the commodity dimension, comprehensive prediction data sources including commodity sales speed, order cycle, customer concentration, and supply cycle adaptation degree are generated, and the comprehensive prediction data is subjected to abnormal cleaning and structure normalization processing. Through the time series analysis engine, ARIMA, LSTM or Prophet algorithm models are used to perform multi-dimensional prediction modeling on the comprehensive prediction data, and daily, weekly, and monthly commodity demand trends are obtained. Layered prediction results are established for different commodity categories, sales regions, and customer groups, and multi-dimensional demand prediction results covering time dimension, category dimension, and business type dimension are output. At the same time, the multi-dimensional demand prediction results are compared and analyzed with the real-time collected supply chain resource status, the actual capacity upper limit of each supplier, the short-term releasable capacity, and the production scheduling restrictions are read from the supply end, the vehicle quantity, route coverage capacity, scheduling interval, and load capacity of the carrier are obtained from the transportation end, the available storage capacity, temperature zone distribution, and warehouse turnover cycle of various commodities are obtained from the warehouse end, and the above three types of resource elements and demand prediction results are matched and verified to identify whether there is a resource constraint, such as a sudden increase in demand for a certain commodity but insufficient capacity of the main supplier, an increase in demand in a certain area but sparse carrier path, or a certain warehouse temperature control area is about to be saturated. After identifying the resource constraint condition, the supply chain optimization strategy generation module is started, and response strategies are developed based on the resource bottleneck position, including sending temporary supplement requests to high-priority suppliers with high comprehensive scores, splitting main line carrier tasks to auxiliary carriers and reconstructing transportation plans, and reallocating warehouse space by adjusting the classification strategy or introducing temporary transit warehouses, to generate a supply chain demand prediction report containing resource supplement suggestions, priority ranking, and executable time window.

[0129] The multi-dimensional demand prediction result is obtained based on time series analysis of the comprehensive prediction data source, including: constructing a double-layer deep belief network prediction architecture, wherein the first layer deep belief network takes the supplier historical delivery data, the carrier transportation time limit data and the warehouse inventory turnover data in the comprehensive prediction data source as an input layer, and learns the unsupervised feature of the supplier delivery rule, the logistics transportation mode and the inventory change mode through a plurality of restricted Boltzmann machine units to obtain a bottom layer business feature representation vector; the hidden layer weight parameter of the first layer deep belief network is trained based on the bottom layer business feature representation vector, the potential distribution characteristics of the supplier delivery capacity, the time variation rule of the carrier transportation efficiency and the seasonal fluctuation characteristics of the warehousing demand are learned through the contrastive divergence algorithm, the original business data is mapped to the abstract representation in the high-dimensional feature space to obtain the first layer network feature output; the first layer network feature output is taken as the input data of the second layer deep belief network, the procurement order review state change and the market demand prediction information in the ERP system are combined, the high-level semantic features of the supply chain collaborative decision are learned through the multi-layer restricted Boltzmann machine of the second layer network, the complex nonlinear relationship mode of the supplier-carrier-warehouse ternary coordination is captured to obtain the high-level decision feature representation; the prediction output layer of the second layer deep belief network is run based on the high-level decision feature representation, the supplier response probability distribution, the carrier capacity demand distribution and the warehouse space demand distribution in the future time window are calculated through the time series prediction algorithm, the confidence interval and the uncertainty level of the prediction result are quantified at the same time, and the multi-dimensional demand prediction result is obtained.

[0130] In the embodiment of the present application, by establishing the data conversion mapping table and the field conversion rule, the standardized conversion of the supplier Excel data, the carrier GPS trajectory data stream and the warehouse RFID storage location data is realized, the format difference and the semantic conflict among different data sources are eliminated, and a unified data foundation is laid for subsequent collaborative processing. Based on the standardized business data, a supplier comprehensive scoring mechanism is established, a dynamic sorting list is generated by combining historical transaction records and real-time qualification information, a technical breakthrough from static supplier management to dynamic qualification evaluation is realized, through candidate supplier set matching and multi-objective optimization algorithm, the comprehensive evaluation of the carrying capacity of the carrier vehicle, the historical punctuality rate and the transportation cost is realized, the optimal carrier selection and the optimal transportation path are automatically generated, and the intelligent level of logistics scheduling is significantly improved. Based on the arrival information in the logistics scheduling plan, the warehouse inventory state is analyzed in advance and the optimal storage location is pre-allocated, the change from passive warehouse entry response to active storage location pre-allocation is realized, and the warehouse space utilization efficiency and the commodity access convenience are optimized. Through the distributed lock mechanism and the timestamp consistency verification, the real-time synchronous update of the business state information of the three modules of supplier management, logistics scheduling and warehouse management is realized, and the technical problems of cross-module data inconsistency and state update delay are eliminated. Based on the business state information, the purchase order audit state change is monitored, the supply chain resource reconfiguration and demand prediction algorithm are automatically triggered, the decision scheme is generated by combining historical sales data and seasonal variation law, and the intelligent change from passive execution to active prediction and decision is realized.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, system and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0132] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0133] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data real-time monitoring method based on a smart factory management platform system, characterized in that, The method comprises the following steps: standard format conversion is performed on supplier Excel data, carrier GPS trajectory data stream and warehouse RFID location data in the smart factory ERP system to obtain standardized business data; a comprehensive score of the supplier is calculated based on the standardized business data, and a dynamic ranking list is generated; purchase order information is received, and a logistics scheduling plan is created in combination with the dynamic ranking list, and an optimal location is pre-assigned for the incoming goods to obtain a warehouse pre-assignment scheme; the execution progress of the warehouse pre-assignment scheme is monitored in real time, and the business status information is updated synchronously; specifically, the execution progress of the location assignment in the warehouse pre-assignment scheme and the completion progress of the goods warehousing are monitored, and the execution progress of the location occupation state in the warehouse management module is captured; the execution progress is received, and the state change events of the supplier delivery state in the supplier management module and the vehicle arrival state in the logistics scheduling module are captured; the timestamp identifiers of the operation records of the supplier management module, the logistics scheduling module and the warehouse management module in the state change events are analyzed, the event times submitted by multiple modules are logically compared through a unified timestamp analysis engine, whether there is a timestamp conflict generated by the same resource in different modules is identified, the business resource numbers in these conflict events are extracted and normalized, after discovering the timestamp conflict, the shared resource numbers involved in the timestamp conflict are applied to the smart factory ERP system for corresponding distributed lock control authority; the current business state data of the supplier management module, the logistics scheduling module and the warehouse management module are read during the period of holding the distributed lock control authority, and a state consistency verification result is generated based on the current business state data; based on the state consistency verification result, data rollback or re-synchronization operation is performed on the inconsistent state information, and after completion, all distributed lock resources are released in turn to obtain a synchronization state verification result; the synchronization state verification result is applied to update the business state information of the supplier management module, the logistics scheduling module and the warehouse management module; the audit state of the purchase order information is monitored based on the business state information, and an intelligent collaborative decision scheme is generated according to the audit state.

2. The data real-time monitoring method based on the smart factory management platform system according to claim 1, characterized in that, The method of performing standard format conversion on the supplier Excel data, carrier GPS trajectory data stream and warehouse RFID location data in the smart factory ERP system to obtain standardized business data comprises the following steps: extracting the supplier Excel data, carrier GPS trajectory data stream and warehouse RFID location data in the smart factory ERP system; performing difference analysis on the supplier information field in the supplier Excel data, the location coordinate field in the carrier GPS trajectory data stream and the location code field in the warehouse RFID location data respectively to obtain format difference information; creating supplier coding uniform rules, GPS coordinate standardization rules and RFID tag conversion rules based on the format difference information; Convert the supplier Excel data, the carrier GPS trajectory data stream and the warehouse RFID storage data into standardized business data based on the supplier coding unified rule, the GPS coordinate standardization rule and the RFID tag conversion rule.

3. The method of claim 1, wherein the method further comprises: The standardized business data is calculated to generate a dynamic ranking list, including: Parse the basic profile information and purchase and storage documents of the suppliers in the standardized business data, and extract the historical transaction records of each supplier based on the basic profile information and the purchase and storage documents; Statistical the on-time delivery times, quality pass rates and payment timeliness in the historical transaction records, and combine the supplier's qualification certification level and price competitive advantage to obtain the supplier's qualification information; Calculate the supplier comprehensive score based on the qualification information, and filter the suppliers whose supplier comprehensive score exceeds the preset score threshold to generate a dynamic ranking list.

4. The data real-time monitoring method based on the smart factory management platform system according to claim 1, characterized in that, The purchase order information is received and combined with the dynamic ranking list to create a logistics scheduling plan, and the optimal storage location is pre-allocated for the incoming goods to obtain a warehouse pre-allocation scheme, including: Receive and parse the supplier address coordinates, commodity volume and weight parameters and delivery time requirements in the purchase order information, and combine the transportation requirements of the order goods in the purchase order information to generate order transportation demand information; Distance match the order transportation demand information with the geographic location of the suppliers in the dynamic ranking list, and filter out suppliers with reasonable transportation distance and sufficient supply capacity to meet the order demand to obtain a candidate supplier set; Iterate through the carrier resource list corresponding to the candidate supplier set, and select the optimal carrier and the optimal transportation path from the carrier resource list; Integrate the vehicle scheduling information of the optimal carrier and the time node arrangement of the optimal transportation path to obtain a logistics scheduling plan; According to the logistics scheduling plan, analyze the warehouse inventory state, and pre-allocate the optimal storage location for the incoming goods to obtain a warehouse pre-allocation scheme.

5. The data real-time monitoring method based on the smart factory management platform system according to claim 4, characterized in that, Iterate through the carrier resource list corresponding to the candidate supplier set, and select the optimal carrier and the optimal transportation path from the carrier resource list, including: Extract the associated carriers of each supplier from the candidate supplier set to obtain a carrier resource list; Calculate the vehicle carrying capacity, historical punctuality rate and transportation cost of each carrier based on the carrier resource list; Input the vehicle carrying capacity, historical punctuality rate and transportation cost into a multi-objective optimization algorithm for weight allocation and comprehensive score to obtain multiple qualified carriers; Select the optimal carrier with the highest comprehensive score from the multiple qualified carriers, and calculate the optimal transportation path from the supplier address to the enterprise warehouse for the optimal carrier.

6. The data real-time monitoring method based on the smart factory management platform system according to claim 5, characterized in that, According to the logistics scheduling plan, analyze the warehouse inventory state, and pre-allocate the optimal storage location for the incoming goods to obtain a warehouse pre-allocation scheme, including: Extract the estimated arrival time, name of goods to be stored, quantity of goods and volume and weight parameters from the logistics scheduling plan, and analyze the storage temperature requirements and shelf life constraints of the goods in the purchase order information to obtain the goods storage demand information; Query the current inventory, safety stock threshold, and reserved inventory data corresponding to the product storage demand information in the warehouse management module to obtain the warehouse inventory status; Scan the occupancy and storage capacity of each storage location in the warehouse inventory status, calculate the storage location capacity matching degree in combination with the volume requirements of the goods to be stored, filter storage location options that meet the storage conditions and are convenient for access, and obtain a candidate storage location list. Analyze the distance and accessibility of each storage location in the candidate storage location list from the outbound gate, and perform classified storage location allocation according to the turnover frequency of the inbound goods to obtain a warehousing pre-allocation plan.

7. The data real-time monitoring method based on the smart factory management platform system according to claim 1, characterized in that, The step of monitoring the review status of the purchase order information based on the business status information and generating an intelligent collaborative decision-making scheme based on the review status includes: The review status of the purchase order information is continuously monitored from the business status information, and a status change notification is generated based on the review status. Upon receiving the status change notification, the supply chain resource assessment procedure is initiated to recalculate the supply chain resource allocation analysis results. Based on the supply chain resource allocation analysis results and historical sales data, demand forecasting is performed to generate a supply chain demand forecasting report; Based on the resource gaps and optimization suggestions in the aforementioned supply chain demand forecast report, an intelligent collaborative decision-making scheme is formulated.

8. The data real-time monitoring method based on the smart factory management platform system according to claim 7, characterized in that, The process of combining the supply chain resource allocation analysis results with historical sales data to perform demand forecasting and generate a supply chain demand forecasting report includes: By integrating the supplier capacity change trend in the supply chain resource allocation analysis results with the historical sales data from the sales outbound module of the smart factory ERP system, a comprehensive predictive data source is obtained. Time series analysis is performed based on the comprehensive forecast data source to obtain multi-dimensional demand forecast results; By comparing the multi-dimensional demand forecast results with the current supplier's actual capacity limit, the carrier's transportation capacity configuration, and the warehouse's current storage capacity, resource constraints are identified. Based on the aforementioned resource constraints, develop supply chain demand forecasting reports that include adding high-priority suppliers, adjusting carrier transportation plans, and reconfiguring warehouse space.

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