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

Abstract

The invention relates to the technical field of smart factory management platforms, and discloses a data real-time monitoring method based on a smart factory management platform system, and the method comprises the steps: carrying out the standard format conversion of supplier Excel data, carrier GPS track data flow and warehouse RFID goods location data in a smart factory ERP system, and obtaining standardized business data; calculating comprehensive scores of suppliers based on the standardized business data and generating a dynamic sorting list; receiving purchase order information and creating a logistics scheduling plan, and pre-allocating an optimal goods allocation for the warehousing commodities to obtain a warehousing pre-allocation scheme; monitoring the execution progress of the storage pre-distribution scheme in real time, and synchronously updating service state information; the auditing state of the purchase order information is monitored based on the service state information, and an intelligent collaborative decision-making scheme is generated, real-time synchronous updating of the service states of the supplier management module, the logistics scheduling module and the warehouse management module is ensured, and the supply chain collaborative management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart factory management platforms, and in particular to a real-time data monitoring method based on a smart factory management platform system. Background Art

[0002] In modern smart factory management platforms, supplier management, carrier logistics scheduling, and warehouse management, as core components of the supply chain, involve the processing and collaboration of numerous heterogeneous data sources. Traditional ERP systems often utilize independent modular designs. Business status updates in these three core modules—supplier management, logistics scheduling, and warehouse management—often experience time lags and data inconsistencies, leading to information gaps and decision-making delays in business processes. When purchase order status changes, related logistics arrangements and warehouse preparations cannot respond in a timely manner, severely impacting the overall collaborative efficiency of the supply chain. Summary of the Invention

[0003] The present invention provides a real-time data monitoring method based on a smart factory management platform system. The present invention ensures real-time synchronous updating of the business status of the three modules of supplier management, logistics scheduling and warehousing management, thereby improving the efficiency of supply chain collaborative management.

[0004] In a first aspect, the present invention provides a method for real-time data monitoring based on a smart factory management platform system, the method comprising: Convert supplier Excel data, carrier GPS track data streams, and warehouse RFID location data in the smart factory ERP system into standard formats to obtain standardized business data. Calculate the supplier's comprehensive score based on the standardized business data and generate a dynamic ranking list; Receive purchase order information and create a logistics scheduling plan based on the dynamic sorting list, and pre-allocate the optimal storage location for the incoming goods to obtain a warehouse pre-allocation plan; Monitor the execution progress of the warehouse pre-allocation plan in real time and update business status information synchronously; The review status of the purchase order information is monitored based on the business status information, and an intelligent collaborative decision-making solution is generated according to the review status.

[0005] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, converting the supplier Excel data, carrier GPS track data stream, and warehouse RFID cargo location data in the smart factory ERP system into a standard format to obtain standardized business data includes: Extract supplier Excel data, carrier GPS track data streams, and warehouse RFID location data from 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 track data stream, and the cargo location code field in the warehouse RFID cargo location data to obtain format difference information; Creating supplier coding uniformity rules, GPS coordinate standardization rules, and RFID tag conversion rules based on the format difference information; Based on the supplier coding uniform rules, the GPS coordinate standardization rules and the RFID tag conversion rules, the supplier Excel data, the carrier GPS track data stream and the warehouse RFID cargo location data are converted into standardized business data.

[0006] In combination with the first aspect, in a second implementation of the first aspect of the present invention, calculating the supplier comprehensive score based on the standardized business data and generating a dynamic ranking list includes: Parsing the basic file information and purchase receipt documents of suppliers in the standardized business data, and extracting historical transaction records of each supplier based on the basic file information and the purchase receipt documents; Counting the number of on-time deliveries, quality compliance rates, and timely payment rates in the historical transaction records, and combining the supplier's qualification certification level and price competitiveness to obtain the supplier's qualification information; The supplier comprehensive score is calculated based on the qualification information, and suppliers whose comprehensive scores exceed a preset score threshold are screened to generate a dynamic ranking list.

[0007] In conjunction with the first aspect, in a third implementation of the first aspect of the present invention, the receiving of purchase order information and creating a logistics scheduling plan in combination with the dynamic sorting list, while pre-allocating optimal storage locations for incoming goods to obtain a warehousing pre-allocation plan, includes: Receive and parse the supplier address coordinates, product volume and weight parameters, and delivery time requirements in the purchase order information, and generate order transportation requirement information based on the transportation requirements of the ordered products in the purchase order information; Matching the order transportation demand information with the geographical locations of suppliers in the dynamic sorting list, screening out suppliers with reasonable transportation distances and supply capabilities that meet the order requirements, and obtaining a set of candidate suppliers; Traversing the carrier resource list corresponding to the candidate supplier set, and selecting the optimal carrier and the optimal transportation route from the carrier resource list; Integrate the vehicle dispatch information of the optimal carrier and the time node arrangement of the optimal transportation route to obtain a logistics dispatch plan; The warehouse inventory status is analyzed according to the logistics scheduling plan, and the optimal storage location is pre-allocated for the incoming goods to obtain a warehouse pre-allocation plan.

[0008] In combination with the first aspect, in a fourth implementation of the first aspect of the present invention, traversing the carrier resource list corresponding to the set of candidate suppliers and selecting the optimal carrier and the optimal transportation route from the carrier resource list includes: Extracting the carriers associated with each supplier from the candidate supplier set to obtain a carrier resource list; Calculating each carrier's vehicle load capacity, historical on-time performance, and transportation cost based on the carrier resource list; Inputting the vehicle load capacity, the historical on-time rate, and the transportation cost into a multi-objective optimization algorithm for weight allocation and comprehensive scoring to obtain a plurality of qualified carriers; An optimal carrier with the highest comprehensive score is selected from the multiple qualified carriers, and an optimal transportation route from the supplier address to the enterprise warehouse of the optimal carrier is calculated.

[0009] In conjunction with the first aspect, in a fifth implementation of the first aspect of the present invention, analyzing the warehouse inventory status according to the logistics scheduling plan, pre-allocating optimal storage locations for incoming goods, and obtaining a warehouse pre-allocation plan includes: Extracting the estimated arrival time, incoming commodity name, commodity quantity, and volume weight parameters from the logistics scheduling plan, parsing the storage temperature requirements and shelf life constraints of the ordered commodities from the purchase order information, and obtaining commodity storage demand information; Query the current inventory, safety stock threshold, and reserved inventory data corresponding to the commodity storage demand information in the warehouse management module to obtain the warehouse inventory status; Scan the occupancy and storage capacity of each cargo location in the warehouse inventory status, calculate the cargo location capacity matching degree based on the volume requirements of the incoming goods, select cargo location options that meet the storage conditions and are conveniently located for storage and access, and obtain a list of candidate cargo locations; The distance from each storage location in the candidate storage location list to the outbound port and the access convenience are analyzed, and classified storage location allocation is performed according to the turnover frequency of the incoming goods to obtain a warehouse pre-allocation plan.

[0010] In conjunction with the first aspect, in a sixth implementation of the first aspect of the present invention, the real-time monitoring of the execution progress of the warehouse pre-allocation plan and the synchronous updating of the business status information include: Monitor the execution of the storage space allocation and the progress of the goods entering the warehouse in the warehouse pre-allocation plan, and capture the execution progress of the storage space occupancy status in the warehouse management module; Receive the execution progress and simultaneously capture status change events of supplier delivery status in the supplier management module and vehicle arrival status in the logistics scheduling module; Processing the timestamp conflict in the state change event and performing state consistency verification to obtain a synchronization state verification result; The synchronization status verification result is applied to update the business status information of the supplier management module, the logistics scheduling module and the warehouse management module.

[0011] In combination with the first aspect, in a seventh implementation of the first aspect of the present invention, processing the timestamp conflict in the state change event and performing state consistency verification to obtain a synchronization state verification result includes: Parsing timestamp identifiers of 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; Apply for the corresponding distributed lock control authority from the smart factory ERP system according to the shared resource number involved in the timestamp conflict; Read the current business status data of the supplier management module, logistics scheduling module and warehouse management module during the period of holding the distributed lock control authority, and generate a state consistency verification result based on the current business status data; Based on the state consistency verification result, data rollback or resynchronization operation is performed on the inconsistent state information. After completion, all distributed lock resources are released in sequence to obtain the synchronization state verification result.

[0012] In combination with the first aspect, in an eighth implementation of the first aspect of the present invention, monitoring the review status of the purchase order information based on the business status information and generating an intelligent collaborative decision-making solution based on the review status include: Continuously monitoring the review status of the purchase order information from the business status information, and generating a status change notification based on the review status; After receiving the status change notification, the supply chain resource evaluation program is started to recalculate the supply chain resource allocation analysis results; Combining the supply chain resource allocation analysis results with historical sales data to perform demand forecasting and generate a supply chain demand forecast report; Based on the resource gaps and optimization suggestions in the supply chain demand forecast report, an intelligent collaborative decision-making plan is formulated.

[0013] In conjunction with the first aspect, in a ninth implementation of the first aspect of the present invention, the step of combining the supply chain resource configuration analysis results with historical sales data to perform demand forecasting and generate a supply chain demand forecast report includes: Integrate the supplier capacity change trend in the supply chain resource allocation analysis results with the historical sales data of the sales and delivery module in the smart factory ERP system to obtain a comprehensive forecast data source; Performing time series analysis based on the comprehensive forecast data source to obtain multi-dimensional demand forecast results; Comparing the multi-dimensional demand forecast results with the actual capacity limits of current suppliers, the capacity allocation of carriers, and the storage capacity status of warehouses to identify resource constraints; Based on the resource constraints, a supply chain demand forecast report is formulated, including adding high-priority suppliers, adjusting carrier transportation plans, and replanning warehouse space configurations.

[0014] In the technical solution provided by the present invention, standardized and unified processing of supplier Excel data, carrier GPS trajectory data stream and warehouse RFID cargo location data is achieved by establishing a data conversion mapping table, eliminating format differences and semantic conflicts between multi-source heterogeneous data. The supplier comprehensive scoring mechanism and dynamic ranking list constructed based on standardized business data have achieved a technological breakthrough from static management to dynamic qualification evaluation, providing accurate supplier intelligent matching services for purchase orders. A comprehensive evaluation of the carrier's vehicle load capacity, historical punctuality and transportation cost is achieved through a multi-objective optimization algorithm, and the optimal carrier selection and optimal transportation route are automatically generated. The warehouse pre-allocation mechanism based on the logistics scheduling plan realizes the advance planning and space optimization configuration of cargo locations. The distributed lock mechanism and timestamp consistency verification ensure the real-time synchronous update of the business status of the three modules of supplier management, logistics scheduling and warehouse management. The demand forecasting algorithm combined with historical sales data and seasonal variation patterns realizes the intelligent transformation from passive execution to active forecasting decision-making, and comprehensively improves the efficiency of supply chain collaborative management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 Schematic diagram of the steps of a real-time data monitoring method based on a smart factory management platform system in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] An embodiment of the present invention provides a method for real-time data monitoring based on a smart factory management platform system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In one embodiment of the present invention, a method for real-time data monitoring based on a smart factory management platform system includes: Step S11: Convert the supplier Excel data, carrier GPS track data stream, and warehouse RFID location data in the smart factory ERP system into a standard format to obtain standardized business data; It is understood that the execution subject of the present invention can be a real-time data monitoring device based on the smart factory management platform system, or a terminal or server, which is not limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example.

[0019] Specifically, the data interface module receives Excel files exported from the supplier management subsystem, containing fields such as the names, codes, tax numbers, addresses, and credit ratings of multiple suppliers. Simultaneously, the module connects to the vehicle terminals in the carrier's dispatch system through a communication protocol to collect GPS trajectory data streams, including longitude, latitude, timestamp, and vehicle number, in real time. RFID readers are deployed to collect tag data streams at the warehouse site, obtaining information such as cargo location numbers, product codes, and warehousing status. Field-level difference analysis is performed on the three types of data mentioned above, and a multidimensional field comparison model is constructed to identify issues such as field redundancy, non-standard naming, or inconsistent formats in the supplier's Excel data. Issues such as differences in latitude and longitude formats, inconsistent units, or inconsistent timestamp encoding methods in GPS trajectory data transmitted from different terminals are also identified. Furthermore, structural differences in the lack of a direct mapping relationship between tag IDs and actual product codes in RFID cargo location data are identified, forming a format difference information set. Based on format difference information, a field-level standardization rule library is constructed. These include unified supplier code rules, which define unified field naming, character length restrictions, illegal character filtering, and redundant field merging rules based on the master data encoding system in the ERP system. GPS coordinate standardization rules address the latitude and longitude accuracy and timestamp formats provided by different devices, establishing unified floating-point precision constraints, time format conversion schemes, and coordinate range validity verification logic. RFID tag conversion rules are used to map physical tag codes read in the field to product barcodes, shelf locations, and inventory data in the ERP system, dynamically binding them through a label-code dictionary and location mapping table. Using these unified supplier code rules, GPS coordinate standardization rules, and RFID tag conversion rules as the conversion engine, a data cleansing module performs operations such as field replacement, format conversion, illegal value correction, and code completion, standardizing the formats of supplier Excel data, carrier GPS tracking data streams, and warehouse RFID shelf location data. The conversion results are written into a standard business data structure, forming a standardized business data set.

[0020] Step S12: Calculate the supplier's comprehensive score based on the standardized business data and generate a dynamic ranking list; Specifically, the platform parses standardized business data for basic supplier profile information, such as company name, supplier code, registration qualifications, contact person, and contact information. It also parses basic supplier profile information, including product code, warehousing time, quantity, delivery cycle, acceptance results, and corresponding payment time points. The platform then uses the historical transaction analysis module to extract transaction details between each supplier and the platform from purchase receipts. This creates a historical transaction record table and calculates the percentage of on-time deliveries based on historical records. The platform also measures the matching of actual delivery times with planned delivery times to assess delivery compliance. It also extracts quality inspection data to determine the quality acceptance rate of delivered goods. Combined with the matching results of payment documents in the finance module, the platform calculates creditworthiness indicators such as payment cycle and on-time payment rate for suppliers. Building on these dynamic indicators, the platform introduces static qualification fields recorded in supplier profile information. These include company certification levels (e.g., ISO quality management system certificates and industry association registration numbers) entered manually or synchronized through third-party certification platforms, as well as a price competitiveness parameter calculated by comparing product pricing strategies to the industry average price level. This constructs a supplier qualification information vector. Based on the supplier qualification information vector, a comprehensive score is calculated using a multi-dimensional weighted model. Weighted parameters are used to integrate five indicators: on-time delivery rate, quality compliance rate, timely payment rate, qualification level, and price competitiveness, to obtain a comprehensive score for each supplier. The comprehensive scores of all suppliers are archived and sorted in descending order by score. Suppliers with scores below a preset threshold are filtered out, retaining only those with scores above the threshold and meeting the cooperation conditions, forming a dynamically ranked list that is updated in real time.

[0021] Step S13: Receive purchase order information and create a logistics scheduling plan based on the dynamic sorting list, and pre-allocate the optimal storage location for the incoming goods to obtain a warehousing pre-allocation plan; Specifically, the purchase order processing module receives approved purchase order information in real time and parses key fields contained in the order, including the supplier's address coordinates, the volume and weight parameters of each product, and the upper limit of the delivery time. It also analyzes the transportation conditions associated with the ordered products, such as temperature control requirements, fragile labels, and stacking restrictions, to automatically generate structured order transportation requirements. This order transportation requirement information is then linked and matched with a dynamically ranked list of suppliers. The geographic coordinates of all suppliers in the ranked list are extracted, and a spatial distance calculation algorithm is used to compare the current order's delivery destination with the address location of each supplier. Combined with the supply capacity performance of product types and quantities as reflected in historical records, suppliers with acceptable distance range, sufficient fulfillment capabilities, and high scores are selected to construct a candidate supplier set. The carrier resource list associated with each supplier in the candidate supplier set is analyzed, including available vehicle types, current vehicle locations, load capacities, GPS track history, scheduled shifts, and remaining dispatch time. The transportation route planning module comprehensively analyzes the road network from suppliers to warehouses for the shortest path and minimum travel time, taking into account traffic congestion probability and route accessibility. It compares the routes and resource compatibility offered by different carriers, selecting the carrier and corresponding route that strikes the best balance between time cost, transportation capacity, and route stability to determine the optimal transportation route. The optimal carrier's vehicle scheduling information is integrated with the selected route's timeline, and scheduling parameters such as loading time, departure time, and estimated arrival time are set. The required loading resources are recorded to form a logistics scheduling plan. Based on the estimated arrival time and incoming product information in the logistics scheduling plan, the warehouse management module retrieves the current warehouse storage location occupancy status and historical turnover rate data. The module identifies each storage location's available capacity, compatible product types, accessible path length, and zoning priority, and comprehensively evaluates storage location efficiency and ease of operation. Larger, more frequently shipped items are prioritized for placement in high-frequency storage locations near inbound aisles or main operating channels. Items with longer storage cycles and less frequent operations are placed in lower-priority locations deep within the warehouse or on the edges. All incoming goods are matched and bound through the cargo space allocation optimization algorithm to form a warehouse pre-allocation plan covering all purchase order goods.

[0022] Step S14: monitor the execution progress of the warehouse pre-allocation plan in real time and update the business status information synchronously; Specifically, the warehouse management module continuously tracks the occupancy status of pre-allocated storage locations and the progress of merchandise inbound. By invoking the storage location allocation and inventory inbound interfaces, the module obtains the actual inbound quantity, current occupancy indicator, and inbound completion rate for each target storage location in real time. The module compares the deviations between the pre-allocation plan and the actual execution records to establish a dynamic execution progress model. Simultaneously, the module receives delivery status updates from the supplier management module, such as shipment status, delivery time, and quantity. It also captures the arrival status of vehicles in the logistics scheduling module in real time, including key data such as whether GPS positioning has entered the designated area and the difference between the estimated and actual arrival times. These events are then uniformly recorded with timestamps. All events are received and processed by a unified state change processor, which detects conflicting timestamps between event timestamps from different sources. If there are logical discrepancies in the order of precedence, the event reordering mechanism and source priority strategy are used to correct them. After the timing adjustments are completed, consistency verification is performed between the supplier shipment status, transportation status, and warehouse inbound status to ensure cross-module state logic matches, avoiding state misalignment or data lag. Synchronous state verification results are generated based on this information. Based on the synchronization status verification results, the platform synchronizes and updates the supplier fulfillment field, transportation task status field, and warehouse space occupancy field through a unified interface.

[0023] Step S15: Monitor the review status of the purchase order information based on the business status information, and generate an intelligent collaborative decision-making plan based on the review status.

[0024] Specifically, the purchase order review field in the business status information is continuously monitored in the background task thread, including whether the order has been reviewed, the review time, the reviewer, and the review notes. When a change in the review status is detected, such as from "unreviewed" to "reviewed" or from "reviewed" back to "unreviewed", a status change notification is generated and sent to the supply chain collaborative processing module through an event-driven mechanism. After receiving the status change notification, the supply chain collaborative processing module automatically starts the supply chain resource assessment program, re-acquires core resource data such as storage capacity, transportation scheduling resources, and supplier inventory status associated with the purchase order at the current point in time, and performs resource availability analysis and scheduling conflict detection in combination with the multi-dimensional constraints configured in the system to generate the latest resource configuration analysis results. After completing the resource assessment, the historical data analysis engine is called to integrate the latest resource allocation results with historical sales records, extract patterns such as seasonal demand fluctuations, key category trend changes, and cyclical purchasing behavior, and generate short-term and medium-term supply chain demand forecast reports through time series forecasting models. These reports include forecasts of future demand quantities for various commodities, as well as matching differences between current resource distribution and forecasted demand, clearly identifying potential resource gap areas, redundant configuration issues, and optimizable paths. Based on the analysis conclusions and optimization suggestions in the supply chain demand forecast report, the platform builds an intelligent collaborative decision-making solution, which includes supplier recommendation ranking adjustments, carrier resource reallocation suggestions, temporary warehouse expansion plans, and emergency replenishment or order extension strategies, enabling each business module to quickly adjust its strategy based on actual review status changes.

[0025] In a specific embodiment, the process of executing step S11 may specifically include the following steps: Extract supplier Excel data, carrier GPS track data streams, and warehouse RFID location data from the smart factory ERP system; Perform difference analysis on the supplier information field in the supplier Excel data, the location coordinate field in the carrier GPS track data stream, and the cargo location code field in the warehouse RFID cargo location data to obtain format difference information; Create unified supplier coding rules, GPS coordinate standardization rules, and RFID tag conversion rules based on format difference information; Based on unified supplier coding rules, GPS coordinate standardization rules, and RFID tag conversion rules, supplier Excel data, carrier GPS track data stream, and warehouse RFID cargo location data are converted into standardized business data.

[0026] Specifically, a data connection mechanism with each subsystem is established through a pre-set data collection interface. Supplier Excel data originates from the SRM system or manually maintained basic files. Carrier GPS trajectory data streams are uploaded in real time by the connected TMS platform via satellite positioning equipment. Warehouse RFID location data is periodically collected and stored by RFID readers and writers deployed in the WMS system. A unified data extraction engine is used to access data from these three heterogeneous sources, parsing the original structure and field content contained in each. Examples include the supplier number, company name, tax identification number, address, and contact information contained in the supplier Excel file; the longitude, latitude, timestamp, and vehicle number contained in the GPS trajectory data; and the tag number, bound product barcode, location code, and in-warehouse status contained in the RFID data. A difference analysis module is used to perform field-level comparisons of data structures from different sources. The supplier information fields in the supplier Excel data are compared with the standard supplier master data structure defined in the ERP system to identify issues such as inconsistent field naming, type mismatches, character set differences, or redundant fields. For GPS trajectory data, the location coordinate fields are analyzed to ensure they use a unified coordinate system (such as WGS-84), and the time fields are standardized to the ISO timestamp format, and the coordinate accuracy is verified to meet positioning requirements. For RFID data, the location code fields stored in the tags are parsed and compared with the standard location codes in the warehouse system to identify any discrepancies in label formats, missing binding information, or inconsistent encoding rules. By analyzing the structural attributes, data length, value range, and encoding method of these fields, a format difference information table is generated. Based on this format difference information, unified data standardization rules are constructed for each field. For supplier information, unified supplier coding rules are established, clarifying the number of digits required for supplier numbers, prefix formats, character encoding specifications, and illegal character filtering logic. For GPS trajectory data, GPS coordinate standardization rules are formulated, converting all coordinates into a unified coordinate system, standardizing the time format to millisecond-level UTC timestamps, and setting an effective accuracy range. For RFID cargo location data, RFID tag conversion rules are constructed, establishing an association between the tag number and the cargo location code mapping table within the system, unifying the tag information structure, and ensuring that each tag uniquely corresponds to the actual cargo location in the warehouse. Based on the above three types of rules, the standardized conversion engine performs field mapping, format conversion, and content verification operations on the original data, processing all fields to be converted item by item, converting the supplier Excel data, GPS trajectory data, and RFID tag data into standardized business data formats with unified structure and consistent semantics, and writing them into the system master data to achieve standardized integration of heterogeneous data across systems.

[0027] Among them, the field conversion operation in the multi-source data conversion mapping table is executed to convert the supplier Excel data, carrier GPS track data flow and warehouse RFID cargo location data into standardized business data, including: establishing an ERP business association map with supplier information node, carrier scheduling node and warehouse management node as the core, building a main business chain with purchase requisition as the starting node, purchase order as the intermediate node and purchase receipt as the ending node, and creating a supplier qualification evaluation subgraph, a carrier scheduling status subgraph and an inventory safety warning subgraph to obtain a three-dimensional business association network; based on the three-dimensional business association network, the company name, contact information and tax rate information in the supplier Excel data are semantically parsed to identify the business meaning and association relationship of the data field, and the association mapping rules between supplier information and purchase order and payment record are established to obtain the supplier data semantic mapping result; the latitude and longitude coordinates, timestamp and vehicle number in the carrier GPS track data flow are semantically analyzed in real time, and the results are summarized. The business logic of route management and vehicle scheduling is combined to establish the association between location data and transportation tasks, and the transportation distance and estimated arrival time are calculated through the geographic information system algorithm to obtain the semantic mapping result of the carrier data; the business semantics of the label code, cargo location coordinates, and storage capacity information in the warehouse RFID cargo location data are analyzed, and the association mapping relationship between cargo location information and commodity inventory and safety warning is established. The cargo location accessibility and access convenience score are calculated through the warehouse layout algorithm to obtain the semantic mapping result of the warehouse data; the semantic mapping results of supplier data, carrier data and warehouse data are integrated, and the data logical consistency is verified through the node relationship of the business association graph, cross-module data conflicts are eliminated and a unified data identification system is established to obtain a semantically unified business data model; based on the semantically unified business data model, automatic data format conversion is performed to convert heterogeneous data sources into the standard data structure of the ERP system, maintaining the integrity and accuracy of the original business semantics to obtain standardized business data.

[0028] In a specific embodiment, the process of executing step S12 may specifically include the following steps: Parse the basic supplier file information and purchase receipts in the standardized business data, and extract each supplier's historical transaction records based on the basic file information and purchase receipts; Count the number of on-time deliveries, quality compliance rates, and timely payment rates in historical transaction records, and combine this with the supplier's qualification certification level and price competitiveness to obtain supplier qualification information; Calculate the supplier's comprehensive score based on qualification information, filter out suppliers whose comprehensive scores exceed the preset score threshold, and generate a dynamic sorting list.

[0029] Specifically, the master data management module is used to classify and analyze standardized business data, extracting basic supplier file information and purchase receipts. The basic supplier file information includes fields such as supplier code, company name, geographic location, contact person, bank account, tax rate configuration, qualification level, and contract validity period in a unified format; the purchase receipt records detailed data related to the supplier, such as the product code, actual arrival time, planned arrival time, receipt acceptance results, corresponding purchase order number, and receipt amount. The purchase receipt is associated with the supplier code, and by establishing a mapping relationship between supplier, order, and receipt, a collection of historical transaction records based on the supplier is compiled. The statistical analysis engine is used to calculate and archive key indicators in the transaction process. The on-time delivery rate is determined by comparing the planned delivery time and actual arrival time of each purchase receipt. If a delivery is completed within the specified timeframe, it is recorded as an on-time delivery and output as the ratio of on-time deliveries to total deliveries. The quality acceptance rate is determined by the incoming acceptance results. The qualified quantity of each batch and the total arrival quantity are extracted from the incoming records to calculate the acceptance rate for each supplier. The timely payment rate is calculated based on the payment documents in the financial module. The actual payment time on the payment document is compared with the corresponding payment due time on the purchase receipt to determine whether each payment is executed on time. The ratio of on-time payments to total payments is used as an evaluation indicator. Furthermore, the qualification certification level and price competitiveness indicators in the supplier profile information are incorporated into the evaluation system. Qualification certification levels are determined by a combination of factors, including whether a company possesses ISO9001, ISO14001, or industry-specific or recognized qualifications, and are converted into standardized scores based on these levels. Price competitiveness is assessed by comparing a supplier's average purchase price with the historical average purchase price of similar products within the system, using a ratio model to measure and standardize their price advantages or disadvantages. A weighted comprehensive calculation model generates a unique overall score for each supplier, based on on-time delivery rate, quality compliance rate, prompt payment rate, qualification level score, and price competitiveness coefficient. Upon completion, all scoring results are archived and sorted in descending order. Suppliers with overall scores exceeding the preset threshold are screened out, generating a dynamically updated ranking list.

[0030] In a specific embodiment, the process of executing step S13 may specifically include the following steps: Receive and parse the supplier address coordinates, product volume and weight parameters, and delivery time requirements in the purchase order information, and combine the transportation requirements of the ordered products in the purchase order information to generate order transportation demand information; The order transportation demand information is matched with the geographical locations of suppliers in the dynamic sorting list, and suppliers with reasonable transportation distances and supply capabilities that meet the order requirements are selected to obtain a set of candidate suppliers; Traverse the carrier resource list corresponding to the candidate supplier set, and select the optimal carrier and optimal transportation route from the carrier resource list; Integrate the optimal carrier's vehicle dispatch information and the optimal transport route's time node arrangement to obtain a logistics dispatch plan; Analyze warehouse inventory status according to the logistics scheduling plan, pre-allocate the optimal storage location for incoming goods, and obtain a warehouse pre-allocation plan.

[0031] Specifically, the procurement management module receives audited purchase order data and performs a structured analysis of key fields within the order. Supplier address coordinates are extracted from the order. Physical parameters such as volume, weight, packaging method, and stacking characteristics for each ordered item are analyzed. Combined with the order's required delivery timeframe, delivery window, and whether the item is time-sensitive, a standardized description of delivery time requirements is generated. Furthermore, transportation requirements associated with each item are extracted, such as whether it requires cold chain transport, whether it is hazardous material, and whether it can be mixed with other materials, to form an order transportation requirement information structure. Based on this order transportation requirement information, a geolocation matching operation is performed on all suppliers in a dynamically ranked list. By comparing each supplier's address coordinates in the ranked list with the geographic location of the target delivery warehouse, the straight-line distance and shortest path length are calculated. Furthermore, supply capacity data is cross-checked against current inventory, historical response times, and recent fulfillment records. This allows the selection of suppliers who meet the transportation distance threshold, can deliver on time, and offer a high degree of product compatibility to construct a candidate supplier set. Traverse the carrier resource list 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 availability time, etc. Based on the volume and weight of the ordered goods, screen out carriers that do not have the transportation capacity, then call the route planning engine to evaluate the various optional paths for each carrier from the supplier to the target warehouse, considering factors such as historical traffic efficiency, traffic congestion probability, road grade, driving restrictions and cost, comprehensively evaluate the timeliness and economy of the path, and 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 expected loading time, driving time, arrival window and warehousing scheduling period, generate a specific logistics scheduling plan, and push the scheduling plan to the carrier module and warehousing module for joint execution. Based on the estimated arrival time, product list, and carrier vehicle number in the logistics scheduling plan, the warehouse management module's storage location optimization model is used to analyze the current inventory status, including storage location occupancy, the spatial distance between each storage location and the storage entrance, and the classification attributes of the applicable product types. The storage locations are then screened and ranked based on the product storage requirements. Based on the storage location scoring results, the optimal storage location is pre-allocated for each type of incoming goods, generating a storage pre-allocation plan.

[0032] Among them, the vehicle dispatch information of the optimal carrier and the time node arrangement of the optimal transportation route are integrated to obtain the logistics dispatch plan, including: extracting the load limit, fuel consumption rate, driver working hours and vehicle positioning device number from the vehicle file of the optimal carrier, combining the current position coordinates of the vehicle and the remaining load capacity, establishing a real-time monitoring database for vehicle resource status, and obtaining basic vehicle dispatch data; based on the basic vehicle dispatch data and the optimal transportation route, calculating the empty driving time of the vehicle from the current position to the supplier address and the full-load transportation time from the supplier address to the enterprise warehouse, taking into account the impact of traffic conditions, weather factors and road restrictions on transportation time, and obtaining an accurate transportation time forecast; according to the accurate transportation time forecast and the delivery time requirements in the purchase order, the time window constraint algorithm is used to arrange key time nodes such as vehicle departure time, arrival time at the supplier, loading completion time and expected warehousing time to ensure transportation The time of the plan is matched with the procurement plan to obtain a time node arrangement plan; combined with the information of each time node in the time node arrangement plan, the transportation cost components such as expected fuel consumption, tolls, driver wages, etc. during the transportation process are calculated, and the loading plan and route selection are optimized through the cost control algorithm to reduce the overall transportation cost, and a cost-optimized transportation plan is obtained; based on the cost-optimized transportation plan, a vehicle real-time tracking mechanism is established, and the vehicle operation trajectory, speed changes and expected arrival time are monitored through the GPS positioning system. When a transportation delay or route deviation is detected, the subsequent time schedule is automatically adjusted and the relevant business modules are notified to obtain a dynamic scheduling control plan; the vehicle allocation information, time node arrangement, cost budget control and real-time tracking mechanism in the dynamic scheduling control plan are integrated to form a complete transportation organization plan including transportation task allocation, time progress control, cost budget and abnormal situation handling, and obtain a logistics scheduling plan.

[0033] In a specific embodiment, the process of traversing the carrier resource list corresponding to the candidate supplier set and selecting the optimal carrier and the optimal transportation route from the carrier resource list may specifically include the following steps: Extract the carriers associated with each supplier from the candidate supplier set to obtain a carrier resource list; Calculate each carrier's vehicle capacity, historical on-time performance, and transportation costs based on the carrier resource list; The vehicle load capacity, historical on-time performance, and transportation costs are input into a multi-objective optimization algorithm for weight allocation and comprehensive scoring to obtain multiple qualified carriers; Select the best carrier with the highest comprehensive score from multiple qualified carriers, and calculate the optimal transportation route from the supplier's address to the enterprise warehouse for the best carrier.

[0034] Specifically, the supply chain collaboration database retrieves the carrier information associated with each candidate supplier. By establishing a supplier-carrier mapping table, the system extracts data from each candidate supplier's historical collaboration history, including carrier codes, vehicle resources, route coverage, transportation type, and dispatch frequency. This creates a comprehensive list of carrier resources covering all candidate suppliers. Each carrier in the list contains a set of registered and dispatchable transportation resources, including vehicle type, vehicle number, current vehicle status, current loading status, and earliest dispatchable time. The transportation scheduling module processes each carrier's list item by item, calculating the theoretical load capacity and average load redundancy rate of all vehicles within each carrier. Based on the overall volume and weight of the ordered goods, carriers with insufficient load capacity or insufficient dispatchable vehicles are eliminated. Historical transportation records are then queried to extract data comparing carriers' task completion times and scheduled arrival times over the most recent statistical period. Each carrier's historical on-time performance is calculated and weighted and smoothed by daily, weekly, and monthly periods to form a timeliness performance indicator. At the same time, standardized modeling is performed on each carrier's transportation cost parameters, including per-kilometer cost, average fuel consumption across different transportation zones, tolls, and dispatch surcharges, to derive the average transportation cost per unit distance. Each carrier's vehicle load capacity, historical on-time performance, and transportation cost are used as input variables to construct a three-dimensional evaluation space, which is then fed into a multi-objective optimization algorithm for comprehensive scoring. A set of adjustable weighting factors is pre-set to assign weights to the three metrics. For example, for time-critical orders, the weighting of on-time performance can be increased, while for cost-sensitive orders, the weighting of cost parameters can be increased. The multi-objective optimization module uses a linear weighted model or a normalized evaluation function to output a normalized score for all carriers. Carriers with scores below an acceptable threshold are eliminated, retaining a set of qualified carriers with satisfactory comprehensive scores. Based on this set of qualified carriers, the carrier with the highest comprehensive score is ranked from highest to lowest, and the carrier with the highest score is selected as the optimal carrier for the current order. Path planning is performed for the optimal carrier's transportation route. Combining the supplier's address coordinates and the company's warehouse coordinates, the path calculation engine evaluates the time, distance, cost, and congestion probability of all feasible paths on the map model. The weighted shortest path algorithm is used for evaluation to determine the optimal transportation path. The path information, time nodes, and carrier vehicle numbers are summarized into a structured output result.

[0035] In a specific embodiment, the execution step of analyzing the warehouse inventory status according to the logistics scheduling plan, pre-allocating the optimal storage location for incoming goods, and obtaining the warehouse pre-allocation plan may specifically include the following steps: Extract the estimated arrival time, incoming product name, product quantity, and volume weight parameters from the logistics scheduling plan, parse the storage temperature requirements and shelf life constraints of the ordered products in the purchase order information, and obtain product storage demand information; Query the current inventory, safety stock threshold, and reserved inventory data corresponding to the commodity storage demand information in the warehouse management module to obtain the warehouse inventory status; Scan the warehouse inventory status to determine the occupancy and storage capacity of each cargo location, calculate the cargo location capacity matching degree based on the volume requirements of incoming goods, select cargo locations that meet the storage conditions and are easily accessible, and obtain a list of candidate cargo locations; Analyze the distance from each storage location in the candidate storage location list to the outbound port and the access convenience, perform classified storage location allocation based on the turnover frequency of incoming goods, and obtain a warehouse pre-allocation plan.

[0036] Specifically, the system extracts core fields from the logistics scheduling plan, such as the estimated arrival time, incoming product name, product quantity, and volume and weight parameters. By structured association with purchase order information, it parses the product storage environment requirements specified in the order, including key constraints such as minimum and maximum storage temperatures, whether constant temperature, refrigerated, or frozen storage is required, and the product's shelf life in days or expiration date after the production date. By structured summarizing these parameters, a storage requirement information set corresponding to each product category is generated. The system then accesses real-time inventory data from the warehouse management module and extracts the current inventory level, safety stock threshold, and reserved inventory data for the warehouse areas that match the product storage requirements. It then calculates the available inventory space and remaining replenishment quantity for each product, establishing a current warehouse inventory status map. The system then scans the inventory status to identify real-time occupancy information for each shelf, including allocated but incomplete inventory, locked shelves, and remaining available capacity for incoming items. This information is then combined with the volume and weight attributes of incoming items to calculate shelf capacity adaptability. By comparing the volume of the goods with the maximum remaining volume of the cargo location, the volume capacity ratio and load carrying ratio of each cargo location are calculated, and cargo locations with insufficient space or exceeding the load limit are eliminated. At the same time, it is verified whether the temperature zone to which the cargo location belongs meets the temperature control requirements of the goods and whether it supports the management mode of outbound delivery in reverse order of shelf life. In this way, a set of cargo locations that meet all environmental and capacity constraints are screened out to form a list of candidate cargo locations. Based on the list of candidate cargo locations, the warehouse map model is called to calculate the path distance between each candidate cargo location and the outbound port, and factors such as the congestion level of the channel where the cargo location is located, forklift accessibility, and feasibility of manual handling are analyzed to quantify the storage and access convenience score of each cargo location. The storage and access convenience score is weighted and calculated based on the actual path length, number of handling times, and average operation time to form a convenience evaluation parameter. The system also incorporates the product's outbound frequency from historical inbound records and applies an ABC turnover classification to all incoming goods. High-turnover Category A products are prioritized for allocation to efficient locations near the outbound exit and with direct access routes. Category B products are allocated to mid-range locations, while Category C products with low turnover are placed in secondary locations farther from the main aisle but with high utilization rates. The system then outputs a set of warehouse pre-allocation plans, including the product name, assigned location number, estimated inbound time, and storage constraints.

[0037] In a specific embodiment, the process of executing step S14 may specifically include the following steps: Monitor the execution of storage space allocation and the progress of goods entering the warehouse in the warehouse pre-allocation plan, and capture the execution progress of storage space occupancy status in the warehouse management module; 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; Handle timestamp conflicts in state change events and perform state consistency verification to obtain synchronization state verification results; Apply the synchronization status verification results to update the business status information of the supplier management module, logistics scheduling module and warehouse management module.

[0038] Specifically, a unique identifier is established for each shelf allocation instruction in the warehouse pre-allocation plan, and the progress of shelf allocation execution is tracked in real time in conjunction with the warehouse operation system. During each incoming warehousing operation, the occupancy status of each shelf is updated in real time based on product scan data, RFID tag recognition results, or manual confirmation records. This information includes whether incoming goods have started, the current number of incoming goods, the number remaining to be received, and the completion percentage. This continuously monitors the execution status of shelf allocation and the actual progress of incoming goods, and this progress information is synchronously uploaded to the status monitoring interface of the warehouse management module. Simultaneously, delivery status change events are received from the supplier management module, such as status indicators such as supplier shipped, partially delivered, and delivery exception, and each status change is associated with the corresponding purchase order. Vehicle arrival status events are captured from the logistics scheduling module to monitor whether the vehicle has arrived at the preset warehouse location, whether unloading has been completed, and whether the GPS coordinates are within the specified area. All these events include timestamp information, data source module, event type, and trigger conditions. To ensure the consistency of status records across modules, an event bus is built to receive and centrally process these status change events. During the centralized processing phase, all status change events are checked for timestamp conflicts to determine whether there are errors in the order of events caused by network delays, clock errors, or asynchronous updates of multiple systems. If a conflict is detected, the event reordering mechanism is enabled to rearrange the events based on timestamps and business logic to ensure the consistency of the timing of status updates. After the sorting is completed, the status consistency verification is performed to check whether the warehouse entry status matches the supplier delivery status and vehicle arrival status logic, to ensure that business rules such as "no entry if not delivered" and "no unloading if not arrived" are not violated, and to generate a synchronization status verification result. Based on the verification results, the status update command is automatically pushed to update the fulfillment status field in the supplier management module, the transportation task status field in the logistics scheduling module, and the execution status of the cargo space allocation and entry tasks in the warehouse management module.

[0039] Among them, the synchronization status verification results are applied to update the business status information of the supplier management, logistics scheduling and warehouse management modules, including: based on the synchronization status verification results, quality assessment processing is performed on the integrity of the supplier basic files in the supplier management module, the accuracy of the GPS coordinate data in the logistics scheduling module and the validity of the RFID cargo location data in the warehouse management module, and the comprehensive score of the data integrity index, accuracy index, consistency index, timeliness index and validity index is calculated to obtain a data quality assessment report; the data items in the data quality assessment report that are lower than the preset quality threshold are detected, and data anomaly types such as missing supplier information fields, abnormal GPS coordinate offsets and RFID tag reading errors are identified. The detected data problems are grouped and processed according to missing data, abnormal data and inconsistent data through the abnormal data classification algorithm to obtain data anomaly classification results; the historical data completion algorithm is started according to the missing data type in the data anomaly classification result, and the historical valid records of the same supplier or similar carrier are extracted for data speculation and filling. , start the data correction algorithm for abnormal data types to verify the rationality range of GPS coordinates and the standardization of RFID tag encoding, and obtain the data repair processing plan; execute the automatic repair operation in the data repair processing plan, and restore the business status of key data items that cannot be automatically repaired to the historical status point where the data consistency check passed the most recent data consistency test through the data rollback mechanism, and at the same time trigger the data re-collection and synchronization process to obtain the repaired business data set; based on the repaired business data set, recalculate the business status consistency of the three modules of supplier management, logistics scheduling and warehouse management, verify the data repair effect and record the repair operation log, and confirm that the data repair is successful when all data quality indicators meet the preset standards, and obtain qualified business status data; synchronously update the qualified business status data to the supplier archive of the supplier management module, the carrier status library of the logistics scheduling module and the cargo location information library of the warehouse management module, establish a data quality monitoring and early warning mechanism to continuously track subsequent data changes, and obtain updated business status information.

[0040] In a specific embodiment, the execution step handles timestamp conflicts in state change events and performs state consistency verification to obtain a synchronization state verification result, which may specifically include the following steps: Parse the timestamp identifiers of the operation records of the supplier management module, logistics scheduling module, and warehouse management module in the status change event, and identify timestamp conflicts based on the timestamp identifiers; Apply for the corresponding distributed lock control permission from the smart factory ERP system based on the shared resource number involved in the timestamp conflict; While holding the distributed lock control authority, the current business status data of the supplier management module, logistics scheduling module, and warehouse management module are read, and the status consistency verification results are generated based on the current business status data; Based on the state consistency verification results, data rollback or resynchronization operations are performed on the inconsistent state information. After completion, all distributed lock resources are released in sequence to obtain the synchronization state verification results.

[0041] Specifically, the event bus receives status change events from the supplier management module, logistics scheduling module, and warehouse management module. Each event contains the timestamp identifier of the corresponding operation record, the event type, the module tag, and the business resource number involved. A unified timestamp parsing engine is used to logically compare the event times submitted by multiple modules to identify whether there are timestamp conflicts generated by the same resource in different modules, such as when a purchase order is marked as "delivered" in the logistics module but still "not in storage" in the warehouse module, or when the supplier's fulfillment status has changed to "delivery completed", but the warehouse has not yet received the materials. The resource numbers in these conflicting events are extracted and normalized. After a timestamp conflict is discovered, a lock application is initiated to the distributed lock management module of the smart factory ERP system based on the shared resource numbers involved in the conflict, such as the purchase order number, transport order number, or cargo location number. The lock control mechanism is implemented based on an optimistic concurrency model or distributed middleware such as Redis. Each business resource is assigned a unique lock identifier. A centralized scheduler determines whether a competitor already holds the lock on the business resource. If the resource is idle, the lock is granted to the current requester. A lock expiration time and operation timeout mechanism are also set. Once the lock control authority is granted, the system maintains exclusive access to read, check, and modify the business resource's status throughout the lock period, ensuring atomicity of state operations across modules. After obtaining the distributed lock, the system reads current business status data from the supplier management module, logistics scheduling module, and warehouse management module, extracting status fields related to conflicting events, such as supplier delivery ID, vehicle arrival time, cargo space occupancy status, and warehousing completion rate. A cross-module state dependency graph is constructed based on the process logic model. The current states of each module are compared on a timeline to determine whether there are process gaps, update delays, or logical inconsistencies between states. Verification results are generated based on predefined state consistency verification rules. Based on the state consistency verification results, the data processing module is automatically invoked to repair any inconsistent state information. For identifiable latency issues, a state refresh operation for the target module is triggered via the resynchronization interface. For data anomalies caused by incorrect operation sequences or illegal state updates, a state rollback mechanism is invoked to restore the state to the most recently confirmed consistent snapshot. All state repair or synchronization operations are executed atomically under the control of the current distributed lock, ensuring that they are not interfered with by other concurrent tasks. Once the state consistency of all conflicting resources is repaired, all held distributed lock resources are released in sequence, and the operation log and repair summary are recorded to generate the synchronization status verification results.

[0042] In a specific embodiment, the process of executing step S15 may specifically include the following steps: Continuously monitor the review status of purchase order information from business status information and generate status change notifications based on the review status; After receiving the status change notification, the supply chain resource evaluation program is started and the supply chain resource allocation analysis results are recalculated; Combine supply chain resource allocation analysis results with historical sales data to forecast demand and generate a supply chain demand forecast report; Develop intelligent collaborative decision-making solutions based on resource gaps and optimization suggestions in supply chain demand forecast reports.

[0043] Specifically, based on the business status monitoring engine within the smart factory ERP platform, the review status field of purchase orders in the procurement management module is continuously monitored, and a state change trigger mechanism is built that is tied to it. This state change trigger mechanism detects in real time the transition of a purchase order from "unreviewed" to "reviewed" or from "reviewed" to "unreviewed," and automatically records key information such as the timestamp, operator, order number, and review notes. When a state change is identified, a structured state change notification event is generated. This notification includes the review action type, pre- and post-change states, trigger time, and a preliminary classification of affected resources. The notification is then pushed to the supply chain collaborative control engine via the event bus. Upon receiving the state change notification, the supply chain resource assessment program is simultaneously activated, locking the product code, planned arrival time, receiving warehouse, and supplier information corresponding to the purchase order. It also retrieves the current supplier's available delivery cycle, historical on-time delivery records, current warehouse capacity, storage space utilization, and in-transit inventory status. By integrating resource information between the supplier management module, logistics scheduling module, and warehousing management module, a cross-departmental, multi-dimensional supply chain resource allocation model is established. The overall resource carrying capacity, short-term conflict risk, and adjustable space after the introduction of this purchase order are recalculated, generating a set of data structures covering warehousing, transportation, and supply capacity assessments to obtain resource allocation analysis results. The sales analysis engine is called to integrate the current resource allocation data with historical sales data. By introducing sales cyclical fluctuations, seasonal demand curves for commodities, and probability models for large orders from specific customers, combined with machine learning regression or time series prediction algorithms, material demand trends in the next several cycles are simulated and predicted, generating a supply chain demand forecast report. This report includes inventory change trends after the addition of new materials to the purchase order, and combines the sales rhythm to provide the expected inventory consumption rate, future critical inventory nodes, types of tight resources, and the time window for 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 to comprehensively consider supplier switching priorities, carrier route replanning, warehouse space reallocation strategies and cross-warehouse transfer feasibility to formulate an intelligent collaborative decision-making plan, which includes supplier replacement recommendations, purchase batch splitting, transfer order pre-generation, carrier vehicle addition suggestions, delayed delivery plans and safety stock dynamic adjustment strategies.

[0044] Among them, based on the resource gaps and optimization suggestions in the supply chain demand forecast report, an intelligent collaborative decision-making plan is formulated, including: monitoring the review events of purchase orders in the ERP system that change from the "pending review" status to the "reviewed" status, extracting the timestamp, product category, quantity specification and supplier information of the order change as the decision trigger condition to start the prediction decision network algorithm and obtain the order review trigger signal; based on the order review trigger signal, the optimal path calculation module of the TMS system and the cargo location pre-allocation module of the WMS system are automatically activated, and a multi-dimensional constraint optimization model is established through the matching constraints of purchase quantity and transportation capacity, and the balance constraints of transportation capacity and inventory capacity to obtain a resource allocation constraint equation group; solving the supplier capacity allocation variables, carrier capacity configuration variables and storage space occupancy variables in the resource allocation constraint equation group, and using the linear programming algorithm to meet the delivery time constraints. , cost control constraints and quality standard constraints to find the optimal solution and obtain the resource allocation optimization plan; input the resource allocation optimization plan and historical procurement data, sales outbound data and inventory turnover data in the ERP system into the machine learning prediction algorithm, train the supplier response time prediction model, transportation path optimization model and inventory demand prediction model, and obtain a set of business prediction models; run the business prediction model set to predict the supplier's supply capacity, carrier transportation demand and storage space demand in the next three months, identify potential supply risk points, logistics bottlenecks and inventory shortages, and obtain forward-looking risk warning results; formulate comprehensive response measures including alternative supplier activation strategies, transportation route adjustment plans and inventory safety replenishment plans based on the forward-looking risk warning results, establish a decision support mechanism from passive response to active prediction, and obtain an intelligent collaborative decision-making plan.

[0045] In a specific embodiment, the execution step combines the supply chain resource allocation analysis results with historical sales data to perform demand forecasting, and the process of generating a supply chain demand forecast report may specifically include the following steps: Integrate the supplier capacity change trends from the supply chain resource allocation analysis results with the historical sales data from the sales and delivery module in the smart factory ERP system to obtain a comprehensive forecast data source; Conduct time series analysis based on comprehensive forecast data sources to obtain multi-dimensional demand forecast results; Compare multi-dimensional demand forecasts with current suppliers' actual capacity limits, carriers' capacity allocations, and warehouse storage capacity to identify resource constraints. Develop supply chain demand forecasts based on resource constraints, including adding high-priority suppliers, adjusting carrier transportation plans, and replanning warehouse space allocation.

[0046] Specifically, dynamic indicators related to supplier capacity are extracted from the supply chain resource allocation analysis results, including capacity trends, production cycle fluctuations, past quarterly load peaks, and scalable capacity boundaries. These dynamic indicators are then entered into the forecasting model as core production capacity parameters. Simultaneously, historical sales data from the past one to two years is obtained from the sales and delivery module of the smart factory ERP system. This data is categorized and organized by order structure, product dimension, and timeline sequence to generate a standardized sales behavior data stream. By horizontally integrating sales data with supplier capacity trends, a corresponding relationship is established between product dimensions to generate a comprehensive forecast data source covering multiple indicators such as product sales velocity, order cycle, customer concentration, and supply cycle fit. This comprehensive forecast data is then cleaned of anomalies and structurally normalized. A time series analysis engine employs algorithms such as ARIMA, LSTM, or Prophet to generate multidimensional forecasting models on the comprehensive forecast data, generating daily, weekly, and monthly demand trends. Layered forecasts are then generated for different product categories, sales regions, and customer groups, outputting multidimensional demand forecasts covering time, category, and business type. At the same time, the multi-dimensional demand forecast results are compared and analyzed with the current supply chain resource status collected in real time. From the supply side, the actual capacity limit, short-term available capacity, and scheduling constraints of each supplier are obtained. From the transportation side, the carrier's vehicle quantity, route coverage, dispatch interval, and load capacity are obtained. From the warehousing side, the available cargo space capacity, temperature zone distribution, and inbound and outbound turnover cycle of each commodity are obtained. These three resource elements are matched and verified with the demand forecast results to identify resource constraints. For example, if demand for a certain commodity surges but the main supplier's capacity is insufficient, demand in a certain region increases but shipping routes are sparse, or the cargo space in a certain warehouse temperature-controlled area is approaching saturation. After identifying resource constraints, the supply chain optimization strategy generation module is activated to formulate response strategies based on the location of resource bottlenecks. These strategies may include issuing temporary replenishment requests to high-priority suppliers with high comprehensive scores, splitting the main route carrier tasks to auxiliary carriers and reconfiguring the transportation plan, and reallocating storage space by adjusting cargo space classification strategies or introducing temporary transit warehouses. The resulting supply chain demand forecast report includes resource replenishment recommendations, priority rankings, and executable time windows.

[0047] Among them, time series analysis is performed based on the comprehensive forecast data source to obtain multi-dimensional demand forecast results, including: constructing a two-layer deep belief network forecasting architecture, in which the first-layer deep belief network uses the supplier's historical delivery data, carrier transportation time data and warehouse inventory turnover data in the comprehensive forecast data source as the input layer, and uses multiple restricted Boltzmann machine units to perform unsupervised feature learning on the supplier's supply rules, logistics transportation patterns and inventory change patterns to obtain the underlying business feature representation vector; based on the underlying business feature representation vector, the hidden layer weight parameters of the first-layer deep belief network are trained, and the potential distribution characteristics of the supplier's supply capacity, the time change law of the carrier's transportation efficiency and the seasonal fluctuation characteristics of the warehousing demand are learned through the contrast divergence algorithm, and the original business data is mapped into high-dimensional features. The first-layer network feature output is obtained by using the abstract representation in the space; the first-layer network feature output is used as the input data of the second-layer deep belief network, and combined with the purchase order review status changes and market demand forecast information in the ERP system, the high-level semantic features of supply chain collaborative decision-making are learned through the multi-layer restricted Boltzmann machine of the second-layer network, and the complex nonlinear relationship pattern of the supplier-carrier-warehouse ternary collaboration is captured to obtain the high-level decision feature representation; based on the high-level decision feature representation, the prediction output layer of the second-layer deep belief network is run, and the supplier response probability distribution, carrier capacity demand distribution and storage space demand distribution in the future time window are calculated through the time series prediction algorithm, and the confidence interval and uncertainty level of the prediction results are quantified to obtain multi-dimensional demand forecast results.

[0048] In this embodiment of the present invention, by establishing data conversion mapping tables and field conversion rules, standardized conversion is achieved between supplier Excel data, carrier GPS trajectory data streams, and warehouse RFID storage location data. This eliminates format differences and semantic conflicts between different data sources, laying a unified data foundation for subsequent collaborative processing. A comprehensive supplier scoring mechanism is established based on standardized business data, combining historical transaction records with real-time qualification information to generate a dynamic ranking list, achieving a technological breakthrough from static supplier management to dynamic qualification assessment. Through candidate supplier set matching and a multi-objective optimization algorithm, a comprehensive assessment of carrier vehicle load capacity, historical on-time performance, and transportation costs is achieved, automatically generating the optimal carrier selection and optimal transportation route, significantly improving the intelligent level of logistics scheduling. Based on arrival information in the logistics scheduling plan, warehouse inventory status is analyzed in advance and optimal storage locations are pre-allocated, achieving a shift from passive warehousing response to active storage location pre-allocation, optimizing storage space utilization efficiency and product access convenience. Through a distributed locking mechanism and timestamp consistency verification, real-time synchronous updating of business status information across the three modules of supplier management, logistics scheduling, and warehouse management is achieved, eliminating the technical challenges of cross-module data inconsistency and status update delays. Based on business status information, it monitors changes in the review status of purchase orders, automatically triggers supply chain resource reconfiguration and demand forecasting algorithms, and generates decision-making plans based on historical sales data and seasonal changes, realizing an intelligent transformation from passive execution to active predictive decision-making.

[0049] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0050] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0051] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time data monitoring method based on a smart factory management platform system, characterized in that: include: Convert supplier Excel data, carrier GPS track data streams, and warehouse RFID location data in the smart factory ERP system into standard formats to obtain standardized business data. Calculate the supplier's comprehensive score based on the standardized business data and generate a dynamic ranking list; Receive purchase order information and create a logistics scheduling plan based on the dynamic sorting list, and pre-allocate the optimal storage location for the incoming goods to obtain a warehouse pre-allocation plan; Monitor the execution progress of the warehouse pre-allocation plan in real time and update business status information synchronously; The review status of the purchase order information is monitored based on the business status information, and an intelligent collaborative decision-making solution is generated according to the review status.

2. The data real-time monitoring method based on the smart factory management platform system according to claim 1 is characterized in that: The supplier Excel data, carrier GPS track data stream, and warehouse RFID location data in the smart factory ERP system are converted into a standard format to obtain standardized business data, including: Extract supplier Excel data, carrier GPS track data streams, and warehouse RFID location data from 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 track data stream, and the cargo location code field in the warehouse RFID cargo location data to obtain format difference information; Creating supplier coding uniformity rules, GPS coordinate standardization rules, and RFID tag conversion rules based on the format difference information; Based on the supplier coding uniform rules, the GPS coordinate standardization rules and the RFID tag conversion rules, the supplier Excel data, the carrier GPS track data stream and the warehouse RFID cargo location data are converted into standardized business data.

3. The data real-time monitoring method based on the smart factory management platform system according to claim 1 is characterized in that: The step of calculating the supplier comprehensive score based on the standardized business data and generating a dynamic ranking list includes: Parsing the basic file information and purchase receipt documents of suppliers in the standardized business data, and extracting historical transaction records of each supplier based on the basic file information and the purchase receipt documents; Counting the number of on-time deliveries, quality pass rates, and timely payment rates in the historical transaction records, and combining the supplier's qualification certification level and price competitiveness to obtain the supplier's qualification information; The supplier comprehensive score is calculated based on the qualification information, and suppliers whose comprehensive scores exceed a preset score threshold are screened 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 is characterized in that: The receiving of purchase order information and creating a logistics scheduling plan in combination with the dynamic sorting list, and pre-allocating the optimal cargo space for incoming goods to obtain a warehousing pre-allocation plan, include: Receive and parse the supplier address coordinates, product volume and weight parameters, and delivery time requirements in the purchase order information, and generate order transportation requirement information based on the transportation requirements of the ordered products in the purchase order information; Matching the order transportation demand information with the geographical locations of suppliers in the dynamic sorting list, screening out suppliers with reasonable transportation distances and supply capabilities that meet the order requirements, and obtaining a set of candidate suppliers; Traversing the carrier resource list corresponding to the candidate supplier set, and selecting the optimal carrier and the optimal transportation route from the carrier resource list; Integrate the vehicle dispatch information of the optimal carrier and the time node arrangement of the optimal transportation route to obtain a logistics dispatch plan; The warehouse inventory status is analyzed according to the logistics scheduling plan, and the optimal storage location is pre-allocated for the incoming goods to obtain a warehouse pre-allocation plan.

5. The data real-time monitoring method based on the smart factory management platform system according to claim 4 is characterized in that: The traversing the carrier resource list corresponding to the candidate supplier set and selecting the optimal carrier and the optimal transportation route from the carrier resource list includes: Extracting the carriers associated with each supplier from the candidate supplier set to obtain a carrier resource list; Calculating each carrier's vehicle load capacity, historical on-time performance, and transportation cost based on the carrier resource list; Inputting the vehicle load capacity, the historical on-time rate, and the transportation cost into a multi-objective optimization algorithm for weight allocation and comprehensive scoring to obtain a plurality of qualified carriers; An optimal carrier with the highest comprehensive score is selected from the multiple qualified carriers, and an optimal transportation route from the supplier address to the enterprise warehouse of the optimal carrier is calculated.

6. The data real-time monitoring method based on the smart factory management platform system according to claim 5 is characterized in that: The analyzing the warehouse inventory status according to the logistics scheduling plan, pre-allocating the optimal storage location for incoming goods, and obtaining a warehouse pre-allocation plan includes: Extracting the estimated arrival time, incoming commodity name, commodity quantity, and volume weight parameters from the logistics scheduling plan, parsing the storage temperature requirements and shelf life constraints of the ordered commodities from the purchase order information, and obtaining commodity storage demand information; Query the current inventory, safety stock threshold, and reserved inventory data corresponding to the commodity storage demand information in the warehouse management module to obtain the warehouse inventory status; Scan the occupancy and storage capacity of each cargo location in the warehouse inventory status, calculate the cargo location capacity matching degree based on the volume requirements of the incoming goods, select cargo location options that meet the storage conditions and are conveniently located for storage and access, and obtain a list of candidate cargo locations; The distance from each storage location in the candidate storage location list to the outbound port and the access convenience are analyzed, and classified storage location allocation is performed according to the turnover frequency of the incoming goods to obtain a warehouse pre-allocation plan.

7. The data real-time monitoring method based on the smart factory management platform system according to claim 1 is characterized in that: The real-time monitoring of the execution progress of the warehousing pre-allocation plan and the simultaneous updating of business status information include: Monitor the execution of the storage space allocation and the progress of the goods entering the warehouse in the warehouse pre-allocation plan, and capture the execution progress of the storage space occupancy status in the warehouse management module; Receive the execution progress and simultaneously capture status change events of supplier delivery status in the supplier management module and vehicle arrival status in the logistics scheduling module; Processing the timestamp conflict in the state change event and performing state consistency verification to obtain a synchronization state verification result; The synchronization status verification result is applied to update the business status information of the supplier management module, the logistics scheduling module and the warehouse management module.

8. The data real-time monitoring method based on the smart factory management platform system according to claim 7 is characterized in that: The processing of the timestamp conflict in the state change event and performing state consistency verification to obtain a synchronization state verification result includes: Parsing timestamp identifiers of 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; Apply for the corresponding distributed lock control authority from the smart factory ERP system according to the shared resource number involved in the timestamp conflict; Read the current business status data of the supplier management module, logistics scheduling module and warehouse management module during the period of holding the distributed lock control authority, and generate a state consistency verification result based on the current business status data; Based on the state consistency verification result, data rollback or resynchronization operation is performed on the inconsistent state information. After completion, all distributed lock resources are released in sequence to obtain the synchronization state verification result.

9. The data real-time monitoring method based on the smart factory management platform system according to claim 1 is characterized in that: The monitoring of the review status of the purchase order information based on the business status information and generating an intelligent collaborative decision-making solution according to the review status includes: Continuously monitoring the review status of the purchase order information from the business status information, and generating a status change notification based on the review status; After receiving the status change notification, the supply chain resource evaluation program is started to recalculate the supply chain resource allocation analysis results; Combining the supply chain resource allocation analysis results with historical sales data to perform demand forecasting and generate a supply chain demand forecast report; Based on the resource gaps and optimization suggestions in the supply chain demand forecast report, an intelligent collaborative decision-making plan is formulated.

10. The data real-time monitoring method based on the smart factory management platform system according to claim 9 is characterized in that: The combining of the supply chain resource allocation analysis results with historical sales data to perform demand forecasting and generate a supply chain demand forecast report includes: Integrate the supplier capacity change trend in the supply chain resource allocation analysis results with the historical sales data of the sales and delivery module in the smart factory ERP system to obtain a comprehensive forecast data source; Performing time series analysis based on the comprehensive forecast data source to obtain multi-dimensional demand forecast results; Comparing the multi-dimensional demand forecast results with the actual capacity limits of current suppliers, the capacity allocation of carriers, and the storage capacity status of warehouses to identify resource constraints; Based on the resource constraints, a supply chain demand forecast report is formulated, including adding high-priority suppliers, adjusting carrier transportation plans, and replanning warehouse space configurations.

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