Material supply management data flow full-process automatic processing system and method

Abstract

The invention discloses a material supply management data flow full-process automatic processing system and method, and relates to the technical field of electric power material supply chains. According to the scheme, order full-life-cycle data are collected in real time through an API interface, a classification model is constructed based on time dimensions and state transition rules, and a classification result is obtained; orders are divided into four classes of new generation, normal circulation, abnormal retention and state jump, and technologies of natural language processing, a decision tree algorithm, OCR and the like are combined, so that full-process automation of order information automatic verification, effective triggering, abnormal processing and settlement process closed loop is realized. According to the method, the manual intervention proportion can be remarkably reduced, the processing efficiency is improved, a closed-loop management mechanism of data acquisition-processing-analysis-optimization is formed, and a standardized solution is provided for intelligent transformation of supply chains of electric power and related industries.

Description

Technical Field

[0001] This invention relates to the field of power supply chain technology, and more specifically to a fully automated data flow processing system and method for supply management. Background Technology

[0002] Within the traditional operational framework of the power supply chain, the State Grid's new-generation e-commerce platform ECP2.0 serves as the core management vehicle, constructing a status management system for the entire lifecycle of procurement orders. This platform clearly categorizes procurement orders into four statuses: confirmed, effective, awaiting delivery, and accepted, forming an interconnected chain of business processes, as detailed below:

[0003] 1) The confirmed status indicates that the supplier has completed the order acceptance. At this time, the material professional fulfillment personnel need to manually review the basic information in the order, such as material description, quantity, and technical specifications. After confirming that there are no errors, they manually trigger the "effective" operation of the order.

[0004] 2) Once an order has been fulfilled, the fulfillment personnel need to communicate and confirm details such as delivery time and transportation method with the supplier offline, and promote the order to be transferred to the pending delivery status by creating a delivery notice;

[0005] 3) When the materials arrive at the designated warehouse, the warehouse management personnel need to carry out manual acceptance operations such as on-site counting and quality inspection based on the order information. Orders that pass the acceptance will be updated to the accepted status, thus completing the closed-loop management of material arrival.

[0006] In this process, each status node requires manual intervention to complete operations such as information verification, process approval, and status changes. This "fully manual intervention" model has many practical problems: the speed of manual processing of each order is difficult to match the high-frequency order flow needs, often resulting in delayed delivery notifications and untimely acceptance, leading to extended material delivery cycles; when manually entering and comparing information such as material names, quantities, and invoice amounts, errors and omissions are easily caused due to fatigue or negligence, requiring additional manpower for review and correction; different positions rely on offline communication to transmit order status changes, and information barriers lead to poor coordination in fulfillment, warehousing, and settlement. The above problems reflect the inadequacy of the traditional model in the era of intelligentization, and there is an urgent need to restructure processes through data flow-driven automation.

[0007] Therefore, how to solve the problems of low efficiency and poor coordination in the traditional manual management model of the power material supply chain, and build an automated and efficient material management system to help the power material supply chain transform towards intelligence is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of this, the present invention provides a fully automated data flow processing system and method for material supply management, which solves the problems existing in the background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for automating the entire data flow of material supply management includes the following steps:

[0011] Synchronize order lifecycle data from business systems, build an order classification model based on time dimension and status transition rules, and divide orders into four categories: newly generated orders, normally flowing orders, abnormally delayed orders, and status jump orders, forming a basic data pool for automated processing;

[0012] The system verifies the execution information of newly generated orders and automatically triggers the effective process according to the delivery cycle. It analyzes the reasons for the delay of abnormal and delayed orders and matches solutions. The intelligent work order engine realizes the automated advancement of the process.

[0013] Intelligent analysis models are built based on order lifecycle data. Through performance trend prediction and data correlation analysis, automated decision-making suggestions are generated to drive the optimization of supplier performance indicators and the formulation of resource allocation strategies.

[0014] By linking order, acceptance, and invoice data through an intelligent data flow engine, the system automatically verifies the execution information of accepted orders, initiates differentiated payment processes based on preset rules, and monitors the settlement status in real time to trigger an exception handling mechanism, forming a closed-loop management system driven by data throughout the entire process.

[0015] Optionally, synchronize order lifecycle data from the business system, specifically:

[0016] By using API interfaces to connect with business systems, incremental data synchronization technology is used to periodically capture order numbers, status fields, update timestamps, and material description texts; among them, incremental data synchronization technology only synchronizes order data that has changed status or has been newly generated.

[0017] Optionally, an order classification model can be constructed based on the time dimension and state transition rules, specifically as follows:

[0018] New orders are identified based on a preset time threshold, and the time of order creation is determined by comparing the order creation time with the local time stamp.

[0019] Establish a status transition rule base, define a valid transition path of "confirmed - effective - awaiting delivery - accepted", and mark orders that conform to the valid transition path as normal transition orders;

[0020] Set a dwell time threshold for each status, and determine the orders that exceed the dwell time threshold as abnormal dwell orders;

[0021] Detect unexpected status changes and identify them as orders with status jumps.

[0022] Optionally, the execution information of newly generated orders can be verified and the effective process can be automatically triggered according to the delivery cycle, specifically as follows:

[0023] The system performs completeness checks on the material descriptions, quantities, and technical specifications of newly generated orders. It uses natural language processing technology to match the standard format of "equipment type-technical parameters-quantity". If key fields are missing, it automatically generates a supplementary work order with a blockchain timestamp and pushes it to the supplier. If the order information is complete and the delivery cycle is within the preset cycle, the effective process is automatically triggered based on preset rules.

[0024] Optionally, analyze the reasons for abnormally delayed orders and match solutions, specifically:

[0025] The decision tree algorithm is used to analyze the order data of abnormally delayed orders, match several preset delay cause feature rules, and automatically generate a delay cause analysis report. Based on the analysis results, the corresponding strategy is matched from the solution library, and the solution is pushed to the corresponding responsible position for execution through the intelligent work order engine.

[0026] Optionally, automated decision-making recommendations can be generated through performance trend prediction and data correlation analysis, specifically:

[0027] A dynamic data platform was built, and time series training was performed on order data from the previous 12 months based on LSTM neural network to predict the fulfillment trend for the next 30 days.

[0028] The association rule algorithm is used to mine the correlation between order status dwell time and supplier region and material type, and generate status evolution curves and bottleneck node analysis reports;

[0029] Based on preset assessment rules and forecast results, the system automatically generates optimization suggestions for supplier on-time delivery rate and abnormal order rate, as well as warehousing resource allocation plans for quarterly order peaks.

[0030] Optionally, the order execution information can be automatically verified, specifically as follows:

[0031] By utilizing the unique identifier association mechanism of the intelligent data flow engine, the supply list, acceptance form photos, and electronic invoice files of the accepted order are linked together;

[0032] The material name, specifications, quantity, and amount in the invoice are extracted using an OCR recognition engine and compared in three dimensions with the actual received quantity in the technical specifications and acceptance form in the order data.

[0033] Orders that pass the comparison are marked as "pending payment". Abnormal orders are automatically marked and a two-factor authentication mechanism is triggered. The results of abnormal handling are synchronously recorded in the blockchain evidence storage system.

[0034] Optionally, a closed-loop management system driven by data throughout the entire process can be established, specifically as follows:

[0035] Establish a payment strategy library and initiate differentiated payment processes based on order amount, supplier credit rating, and abnormal risk level;

[0036] The system scans the settlement platform's status data daily, identifies abnormal orders through a status comparison algorithm, generates follow-up work orders with blockchain timestamps, and pushes them to the finance manager and suppliers.

[0037] By synchronizing payment anomaly data to the order classification model and intelligent analysis model, optimizing the status retention time threshold and supplier assessment indicators, a closed-loop process of "data collection-processing-analysis-optimization" is formed.

[0038] A system for performing the automated processing method for the entire data flow of material supply management as described in any one of the above embodiments, comprising:

[0039] The data acquisition and classification module is used to synchronize order lifecycle data from the business system. Based on the time dimension and status transition rules, it builds an order classification model to divide orders into four categories: newly generated orders, normally flowing orders, abnormally delayed orders, and status jump orders, forming a basic data pool for automated processing.

[0040] The intelligent processing module is used to verify the execution information of newly generated orders and automatically trigger the effective process according to the delivery cycle. It analyzes the reasons for the delay of abnormal and delayed orders and matches solutions. The intelligent work order engine realizes the automated advancement of the process.

[0041] The data analysis module is used to build intelligent analysis models based on order lifecycle data, generate automated decision suggestions through performance trend prediction and data correlation analysis, and drive the optimization of supplier performance indicators and the formulation of resource allocation strategies.

[0042] The settlement closed-loop execution module is used to associate order, acceptance, and invoice data through an intelligent data flow engine, automatically verify the execution information of accepted orders, start differentiated payment processes based on preset rules, and monitor the settlement status in real time to trigger an exception handling mechanism, forming a closed-loop management driven by data throughout the entire process.

[0043] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a fully automated data flow processing system and method for material supply management, which has the following beneficial effects:

[0044] (1) This invention achieves automated processing of the entire lifecycle of orders through intelligent data flow, reducing the degree of manual intervention; from the core links of order status classification, information verification to settlement and payment, a closed-loop mechanism of "data perception - rule execution - anomaly handling" is formed, replacing the traditional manual order-by-order operation mode, which greatly improves the efficiency and continuity of process processing;

[0045] (2) This invention achieves accurate verification and abnormal warning of order data based on intelligent identification and analysis technology, and combines blockchain evidence storage to ensure that the data is tamper-proof, reduce the risk of errors caused by human operation, and build a quality control system with full-process traceability.

[0046] (3) Based on dynamic data modeling and trend analysis, this invention provides a scientific basis for supply chain management. By mining the correlation characteristics of order data and predicting the performance trend, it can optimize supplier assessment indicators and dynamically allocate resources, and promote the transformation of management model from "experience-driven" to "data-predictive". Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0048] Figure 1 The flowchart illustrates the fully automated processing method for the data flow of material supply management provided by this invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] To address the problem that "existing technologies lack automated closed-loop management of the entire order lifecycle, failing to achieve intelligent driving of the entire process from status monitoring to settlement and payment, and thus failing to meet the needs of intelligent transformation of the power material supply chain," this invention discloses a method for fully automated processing of the entire data flow of material supply management, such as... Figure 1 As shown, it includes the following steps:

[0051] Synchronize order lifecycle data from business systems, build an order classification model based on time dimension and status transition rules, and divide orders into four categories: newly generated orders, normally flowing orders, abnormally delayed orders, and status jump orders, forming a basic data pool for automated processing;

[0052] The system verifies the execution information of newly generated orders and automatically triggers the effective process according to the delivery cycle. It analyzes the reasons for the delay of abnormal and delayed orders and matches solutions. The intelligent work order engine realizes the automated advancement of the process.

[0053] Intelligent analysis models are built based on order lifecycle data. Through performance trend prediction and data correlation analysis, automated decision-making suggestions are generated to drive the optimization of supplier performance indicators and the formulation of resource allocation strategies.

[0054] By linking order, acceptance, and invoice data through an intelligent data flow engine, the system automatically verifies the execution information of accepted orders, initiates differentiated payment processes based on preset rules, and monitors the settlement status in real time to trigger an exception handling mechanism, forming a closed-loop management system driven by data throughout the entire process.

[0055] based on Figure 1 The process shown in this embodiment aims to reduce the proportion of manual operations in the entire material supply process by building an intelligent management system of "data-driven decision-making and automatic system execution," thereby solving the problems of low efficiency, high risk, and poor coordination in the traditional model and realizing the automation, efficiency, and intelligence of material management.

[0056] Furthermore, synchronize order lifecycle data from the business system, specifically as follows:

[0057] By connecting with business systems via API interfaces, order data from the ECP2.0 platform can be synchronized. Incremental data synchronization technology is used to periodically capture data such as order numbers, status fields, update timestamps, and material description texts. Incremental data synchronization technology only synchronizes order data that has changed status or has been newly generated.

[0058] Furthermore, an order classification model is constructed based on the time dimension and state transition rules, specifically as follows:

[0059] New orders are identified based on a preset time threshold, which is determined by comparing the order creation time with a local time stamp; in this embodiment, the preset time threshold can be set to 24 hours.

[0060] Establish a status transition rule base, define a valid transition path of "confirmed - effective - awaiting delivery - accepted", and mark orders that conform to the valid transition path as normal transition orders;

[0061] Set a dwell time threshold for each status, and determine orders that exceed the dwell time threshold as abnormal dwell orders (the dwell time of the status exceeds 1.5 times the industry standard); in this embodiment, the dwell time threshold for the pending delivery status can be set to 72 hours.

[0062] Detect unexpected status changes (e.g., a direct jump from "Confirmed" to "Pending Delivery") and identify them as status-jumping orders.

[0063] Based on the above solution, this embodiment replaces the traditional manual order-by-order monitoring with incremental data synchronization and automatic classification technology, thereby reducing the manpower required for order status management.

[0064] Furthermore, the execution information of newly generated orders is verified, and the effective process is automatically triggered according to the delivery cycle, specifically as follows:

[0065] The system performs integrity checks on the material descriptions, quantities, and technical specifications of newly generated orders. It uses natural language processing technology to match the standard format of "equipment type-technical parameters-quantity". If key fields are missing, it automatically generates a supplementary work order with a blockchain timestamp and pushes it to the supplier. If the order information is complete and the delivery cycle is within the preset cycle, the effective process is automatically triggered based on preset rules without manual intervention.

[0066] Furthermore, the reasons for the abnormally delayed orders are analyzed and solutions are matched, specifically as follows:

[0067] The decision tree algorithm analyzes the order data of abnormally delayed orders, matches it with several preset delay cause feature rules, and automatically generates a delay cause analysis report. Based on the analysis results, the corresponding strategy is matched from the solution library, and the solution is pushed to the corresponding responsible position for execution through the intelligent work order engine. In this embodiment, the order data of abnormally delayed orders includes supplier historical performance records, logistics tracking information, material types, etc.; the preset delay cause feature rules include insufficient supplier capacity, abnormal logistics routes, and material quality inspection delays, etc.; the strategies in the solution library include triggering the alternative supplier allocation process, initiating logistics route optimization suggestions, and pushing expedited quality inspection work orders.

[0068] Based on the above process, this embodiment uses a state transition rule base and a dwell time threshold setting to achieve automatic identification of abnormal orders. The decision tree algorithm combined with the preset dwell rules improves the accuracy of abnormal cause analysis and provides early warning of performance risks.

[0069] Furthermore, automated decision-making recommendations are generated through performance trend prediction and data correlation analysis, specifically:

[0070] A dynamic data platform is constructed, which uses an LSTM neural network to train time series data from the previous 12 months to predict the fulfillment trend for the next 30 days. The dynamic data platform includes several core indicators such as order generation, completion, status dwell time, and anomaly rate.

[0071] The association rule algorithm is used to mine the correlation between order status dwell time and dimensions such as supplier region and material type, and generate status evolution curves and bottleneck node analysis reports.

[0072] Based on preset assessment rules and forecast results, the system automatically generates optimization suggestions for assessment indicators such as supplier on-time delivery rate and abnormal order rate, as well as warehousing resource allocation plans for quarterly order peaks.

[0073] Based on the above process, this embodiment trains the LSTM network on order data from the past 12 months to achieve the prediction of fulfillment trends for the next 30 days, improves the accuracy of early warning of order peaks, and improves the utilization rate of acceptance manpower by allocating warehousing resources in advance. Based on the discovery of hidden associations by association rules, it can help optimize supplier assessment indicators and increase the proportion of high-quality suppliers.

[0074] Furthermore, the acceptance order execution information is automatically verified, specifically as follows:

[0075] By utilizing the unique identifier association mechanism of the intelligent data flow engine, the supply list, acceptance sheet photo, and electronic invoice of the accepted order are linked together; in this embodiment, the order number can be regarded as the unique identifier of the intelligent data flow engine.

[0076] The OCR recognition engine extracts fields such as material name, specifications, quantity, and amount from the invoice and performs a three-dimensional comparison with the actual received quantity in the technical specifications and acceptance form in the order data. Specifically, the material name is verified by a semantic matching algorithm, the quantity deviation is controlled within ±5%, and the amount deviation is controlled within ±3%.

[0077] Orders that pass the verification are marked as "pending payment," while abnormal orders are automatically marked and a two-factor authentication mechanism is triggered. The results of the abnormality handling are synchronously recorded in the blockchain evidence storage system. The two-factor authentication mechanism includes manual review and AI secondary verification.

[0078] Based on the above process, this embodiment uses OCR recognition and 3D verification technology to achieve automatic verification of acceptance orders.

[0079] Furthermore, a closed-loop management system driven by data across the entire process is formed, specifically as follows:

[0080] Establish a payment strategy library and initiate differentiated payment processes based on order amount, supplier credit rating, and abnormal risk level. Specifically, for orders with A+ credit rating and amount ≤100,000, a fast track of "pay upon acceptance" can be enabled to automatically skip manual review, while high-risk orders are forced to enter a two-person review process.

[0081] The system scans the settlement platform's status data daily, identifies abnormal orders through a status comparison algorithm, generates follow-up work orders with blockchain timestamps, and pushes them to the finance manager and suppliers.

[0082] Payment anomaly data (such as duplicate payments and payment delays) is synchronized to the order classification model and intelligent analysis model to optimize the status retention time threshold and supplier performance indicators, forming a closed-loop process of "data collection-processing-analysis-optimization". In this embodiment, the settlement anomaly data is fed back to the order classification and analysis model, which can dynamically adjust the status retention threshold and promote continuous improvement in the efficiency of the entire process.

[0083] and Figure 1 Corresponding to the method described above, this embodiment of the invention also provides a fully automated data flow processing system for material supply management, used for... Figure 1 The specific implementation of the method, the fully automated data flow processing system for material supply management provided in this embodiment of the invention, can be applied to computer terminals or various mobile devices, specifically including:

[0084] The data acquisition and classification module is used to synchronize order lifecycle data from the business system. Based on the time dimension and status transition rules, it builds an order classification model to divide orders into four categories: newly generated orders, normally flowing orders, abnormally delayed orders, and status jump orders, forming a basic data pool for automated processing.

[0085] The intelligent processing module is used to verify the execution information of newly generated orders and automatically trigger the effective process according to the delivery cycle. It analyzes the reasons for the delay of abnormal and delayed orders and matches solutions. The intelligent work order engine realizes the automated advancement of the process.

[0086] The data analysis module is used to build intelligent analysis models based on order lifecycle data, generate automated decision suggestions through performance trend prediction and data correlation analysis, and drive the optimization of supplier performance indicators and the formulation of resource allocation strategies.

[0087] The settlement closed-loop execution module is used to associate order, acceptance, and invoice data through an intelligent data flow engine, automatically verify the execution information of accepted orders, start differentiated payment processes based on preset rules, and monitor the settlement status in real time to trigger an exception handling mechanism, forming a closed-loop management driven by data throughout the entire process.

[0088] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0089] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for automating the entire process of data flow processing in material supply management, characterized in that, Includes the following steps: Synchronize order lifecycle data from business systems, build an order classification model based on time dimension and status transition rules, and divide orders into four categories: newly generated orders, normally flowing orders, abnormally delayed orders, and status jump orders, forming a basic data pool for automated processing; The system verifies the execution information of newly generated orders and automatically triggers the effective process according to the delivery cycle. It analyzes the reasons for the delay of abnormal and delayed orders and matches solutions. The intelligent work order engine realizes the automated advancement of the process. Intelligent analysis models are built based on order lifecycle data. Through performance trend prediction and data correlation analysis, automated decision-making suggestions are generated to drive the optimization of supplier performance indicators and the formulation of resource allocation strategies. By linking order, acceptance, and invoice data through an intelligent data flow engine, the system automatically verifies the execution information of accepted orders, initiates differentiated payment processes based on preset rules, and monitors the settlement status in real time to trigger an exception handling mechanism, forming a closed-loop management system driven by data throughout the entire process.

2. The method for fully automated processing of data flow in material supply management according to claim 1, characterized in that, Synchronizing order lifecycle data from the business system specifically involves: By using API interfaces to connect with business systems, incremental data synchronization technology is used to periodically capture order numbers, status fields, update timestamps, and material description texts; among them, incremental data synchronization technology only synchronizes order data that has changed status or has been newly generated.

3. The method for fully automated processing of data flow in material supply management according to claim 1, characterized in that, An order classification model is constructed based on the time dimension and state transition rules, specifically as follows: New orders are identified based on a preset time threshold, and the time of order creation is determined by comparing the order creation time with the local time stamp. Establish a status transition rule base, define a valid transition path of "confirmed - effective - awaiting delivery - accepted", and mark orders that conform to the valid transition path as normal transition orders; Set a dwell time threshold for each status, and determine the orders that exceed the dwell time threshold as abnormal dwell orders; Detect unexpected status changes and identify them as orders with status jumps.

4. The method for fully automated processing of data flow in material supply management according to claim 1, characterized in that, The system verifies the execution information of newly generated orders and automatically triggers the effective process based on the delivery cycle. Specifically: Completeness checks are performed on the material descriptions, quantities, and technical specifications of newly generated orders. Natural language processing technology is used to match the standard format of "equipment type-technical parameters-quantity". When key fields are missing, a supplementary work order with a blockchain timestamp is automatically generated and pushed to the supplier. If the order information is complete and the delivery period is within the preset period, the effective process will be automatically triggered based on the preset rules.

5. The method for fully automated processing of data flow in material supply management according to claim 1, characterized in that, Analyze the reasons for abnormally delayed orders and match solutions, specifically as follows: The decision tree algorithm is used to analyze the order data of abnormally delayed orders, match several preset delay cause feature rules, and automatically generate a delay cause analysis report. Based on the analysis results, the corresponding strategy is matched from the solution library, and the solution is pushed to the corresponding responsible position for execution through the intelligent work order engine.

6. The method for fully automated processing of data flow in material supply management according to claim 1, characterized in that, Automated decision-making recommendations are generated through performance trend prediction and data correlation analysis, specifically: A dynamic data platform was built, and time series training was performed on order data from the previous 12 months based on LSTM neural network to predict the fulfillment trend for the next 30 days. The association rule algorithm is used to mine the correlation between order status dwell time and supplier region and material type, and generate status evolution curves and bottleneck node analysis reports; Based on preset assessment rules and forecast results, the system automatically generates optimization suggestions for supplier on-time delivery rate and abnormal order rate, as well as warehousing resource allocation plans for quarterly order peaks.

7. The method for fully automated processing of data flow in material supply management according to claim 1, characterized in that, The system automatically verifies the execution information of the received orders, specifically as follows: By utilizing the unique identifier association mechanism of the intelligent data flow engine, the supply list, acceptance form photos, and electronic invoice files of the accepted order are linked together; The material name, specifications, quantity, and amount in the invoice are extracted using an OCR recognition engine and compared in three dimensions with the actual received quantity in the technical specifications and acceptance form in the order data. Orders that pass the comparison are marked as "pending payment". Abnormal orders are automatically marked and a two-factor authentication mechanism is triggered. The results of abnormal handling are synchronously recorded in the blockchain evidence storage system.

8. The method for fully automated processing of data flow in material supply management according to claim 1, characterized in that, To form a closed-loop management system driven by data throughout the entire process, specifically: Establish a payment strategy library and initiate differentiated payment processes based on order amount, supplier credit rating, and abnormal risk level; The system scans the settlement platform's status data daily, identifies abnormal orders through a status comparison algorithm, generates follow-up work orders with blockchain timestamps, and pushes them to the finance manager and suppliers. By synchronizing payment anomaly data to the order classification model and intelligent analysis model, optimizing the status retention time threshold and supplier assessment indicators, a closed-loop process of "data collection-processing-analysis-optimization" is formed.

9. A system for implementing the fully automated data flow processing method for material supply management as described in any one of claims 1-8, characterized in that, include: The data acquisition and classification module is used to synchronize order lifecycle data from the business system. Based on the time dimension and status transition rules, it builds an order classification model to divide orders into four categories: newly generated orders, normally flowing orders, abnormally delayed orders, and status jump orders, forming a basic data pool for automated processing. The intelligent processing module is used to verify the execution information of newly generated orders and automatically trigger the effective process according to the delivery cycle. It analyzes the reasons for the delay of abnormal and delayed orders and matches solutions. The intelligent work order engine realizes the automated advancement of the process. The data analysis module is used to build intelligent analysis models based on order lifecycle data, generate automated decision suggestions through performance trend prediction and data correlation analysis, and drive the optimization of supplier performance indicators and the formulation of resource allocation strategies. The settlement closed-loop execution module is used to associate order, acceptance, and invoice data through an intelligent data flow engine, automatically verify the execution information of accepted orders, start differentiated payment processes based on preset rules, and monitor the settlement status in real time to trigger an exception handling mechanism, forming a closed-loop management driven by data throughout the entire process.

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