Key material supply risk dynamic early warning and coordination system and method

Abstract

The invention relates to the technical field of supply chain risk management, in particular to a key material supply risk dynamic early warning and coordination system and method. The invention discloses a key material supply risk dynamic early warning and coordination system, and the system comprises a data collection layer which is used for collecting material supply data and equipment operation associated data, and carrying out the preprocessing of the data; the risk identification and double-view clustering layer is used for dynamically identifying the material supply risk by adopting a double-view fusion clustering algorithm; the early warning and online forcing layer is used for generating early warning information according to the identified risk level and triggering an online forcing process; the coordination solution intelligent recommendation layer is connected with the early warning and online forcing layer, and obtains the solution from the scheme database according to the risk type; and the closed-loop optimization layer is used for tracking the treatment effect and dynamically optimizing system parameters. Through a double-view data fusion clustering mechanism and a full-process automatic closed-loop system, more accurate and prospective identification and early warning of the supply chain risk are realized, and the risk disposal efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of supply chain risk management technology, and in particular to a dynamic early warning and coordination system and method for supply risks of key materials. Background Technology

[0002] In current enterprise supply chain management, especially in industries such as energy, power, energy storage, and chemicals where the stability of key material supply is extremely important, there is a close correlation between material supply risks and equipment operating status. Efficient and accurate risk identification and early warning are key to ensuring production continuity and equipment safety.

[0003] Existing supply chain risk management systems or materials management platforms mostly focus on single-dimensional data analysis. For example, one type of system relies primarily on data from the supply side, such as supplier delivery cycles, inventory levels, order execution progress, and price fluctuations, aiming to provide early warnings of potential supply disruptions, delivery delays, or cost risks. Another type of system focuses on equipment-side data monitoring, predicting the operating status, failure risks, or performance degradation of equipment by collecting sensor data (such as temperature, pressure, and corrosion rates) and equipment maintenance records. However, these systems generally suffer from the following technical limitations: information silos and a single dimension of risk identification, a disconnect between risk warning and coordinated response, insufficient unstructured data processing capabilities, and a lack of dynamic optimization and closed-loop management capabilities.

[0004] Therefore, how to build a system that can deeply integrate data from both the material supply and equipment operation perspectives, and achieve fully automated closed-loop management from dynamic risk identification, intelligent early warning, online delivery reminders to coordinated solutions and recommendations, has become an urgent technical problem to be solved in order to improve the resilience of the supply chain and the level of intelligent equipment management in key industries. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problems of information silos and single risk identification dimension, disconnect between risk warning and coordinated handling, insufficient unstructured data processing capability, and lack of dynamic optimization and closed-loop management capability in the prior art.

[0006] To address the aforementioned technical problems, this invention provides a dynamic early warning and coordination system for supply risks of key materials, comprising: The data acquisition layer is used to collect material supply data and equipment operation-related data, and to preprocess the collected data. The risk identification and dual-view clustering layer, connected to the data acquisition layer, is used to dynamically identify material supply risks based on preprocessed data using a dual-view fusion clustering algorithm. The early warning and online collection layer is connected to the risk identification and dual-view clustering layer, and is used to generate early warning information and trigger the online collection process based on the identified risk level. The intelligent recommendation layer is coordinated and connected to the early warning and online collection layer, and is used to obtain solutions from the solution database according to the risk type. The closed-loop optimization layer is used to track the treatment effect and dynamically optimize system parameters.

[0007] Preferably, the data acquisition layer includes: The multi-source data integration module is used to connect to supplier systems, equipment monitoring systems and industry databases via API interfaces, and also supports manual data entry. The data preprocessing module is used to clean and normalize the collected data, and convert text data into numerical vectors through a word embedding model.

[0008] Preferably, the risk identification and dual-view clustering layer is connected to the data acquisition layer and is used to dynamically identify material supply risks based on preprocessed data using a dual-view fusion clustering algorithm, including: Construct feature vectors for the material supply view and feature vectors for the equipment association view; Based on the feature vectors of the material supply view and the feature vectors of the equipment association view, a comprehensive feature vector is calculated using a weighted fusion formula. By inputting the comprehensive feature vector into the improved K-means clustering algorithm, risk types can be dynamically identified.

[0009] Preferably, the types of risks include: delivery delay risk, quality risk, supply disruption risk, and cost inversion risk.

[0010] Preferably, the early warning and online payment reminder layer includes: The graded early warning module is used to calculate the risk level value based on the risk level assessment formula and issue four levels of early warning: blue, yellow, orange, and red. The intelligent collection reminder module is used to automatically generate standardized collection reminder notices and send them to suppliers through multiple channels when an alert is triggered, and to track feedback status.

[0011] Preferably, the intelligent recommendation layer for coordination solutions includes a database storing multiple coordination solutions, including: diversified procurement strategy solutions, strategic cooperation solutions, and emergency alternative solutions.

[0012] Preferably, the closed-loop optimization layer, used to track the treatment effect and dynamically optimize system parameters, includes: Evaluate system performance based on early warning and response records, follow-up feedback results, and plan execution success rate; Based on the evaluation results, the weights of dual-view fusion, risk level, and clustering algorithm thresholds are automatically adjusted. Successful coordination cases are added to the database as new samples to optimize the recommendation logic.

[0013] This invention also provides a method for dynamic early warning and coordination of supply risks for key materials, including: Real-time acquisition of material supply view data and equipment association view data, and preprocessing of them; Normalize the feature vectors of the material supply view and the equipment association view; The comprehensive feature vector is obtained through a weighted fusion formula; The comprehensive feature vector is input into the improved K-means clustering model, and the risk clustering result is output. The risk level is assessed based on the clustering results, and corresponding early warning and coordination actions are triggered.

[0014] Preferably, the step of inputting the comprehensive feature vector into the improved K-means clustering model and outputting the risk clustering result includes: Initialize cluster centers; The similarity between the sample and the cluster center is calculated using the cosine similarity formula; Based on the similarity, the cluster centers are dynamically adjusted, and the iteration termination condition is set to obtain the risk clustering results.

[0015] Preferably, the step of assessing the risk level based on the clustering results and triggering corresponding early warning and coordination actions includes: Receive risk clustering results and risk levels; Based on the risk level, the system automatically generates and sends tiered early warning information to the corresponding internal personnel and suppliers. For orders or materials that have been flagged for alerts, standardized online reminder notices are automatically generated and sent to suppliers, establishing a reminder feedback tracking process; Based on the risk type of the risk clustering results, intelligent matching and coordination solutions are obtained from a preset solution database; Track and record the early warning response, follow-up feedback results, and the effectiveness of coordination plan implementation, and feed the results data back to the risk identification model and database to optimize system parameters and recommendation logic.

[0016] The technical solution of the present invention has the following advantages over the prior art: This invention discloses a dynamic early warning and coordination system and method for key material supply risks. Through a dual-view data fusion and clustering mechanism, it overcomes the limitations of traditional systems that rely on single-dimensional data. It considers not only the supplier's performance but also real-time equipment operating status, enabling earlier and more accurate identification of hidden equipment risks caused by material issues, thus improving the accuracy and foresight of risk identification and early warning. It integrates risk identification, tiered early warning, online delivery reminders, intelligent coordination recommendations, and effect evaluation optimization into a fully automated closed loop. The system can automatically trigger and execute standardized delivery reminder and coordination actions based on the risk level, automatically matching the best solution, significantly reducing the delay and uncertainty of manual intervention, greatly shortening the risk response cycle, and improving supply chain resilience. Through a built-in closed-loop optimization layer, the system can dynamically learn based on the actual effect of each early warning response, automatically adjusting data fusion weights, clustering algorithm parameters, and solution recommendation priorities. This gives the system self-evolution capabilities, allowing it to continuously adapt to changes in the supply chain and equipment operating environment, with the accuracy and efficiency of risk management continuously improving over time. Attached Figure Description

[0017] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is a structural diagram of a dynamic early warning and coordination system for supply risks of key materials provided by the present invention; Figure 2 This is a flowchart of a method for dynamic early warning and coordination of supply risks for key materials provided by the present invention. Detailed Implementation

[0018] The core of this invention is to provide a dynamic early warning and coordination system and method for supply risks of key materials. Through a dual-view data fusion clustering mechanism and a fully automated closed-loop system, it achieves more accurate and forward-looking identification and early warning of supply chain risks, thereby improving the efficiency of risk handling.

[0019] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely 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.

[0020] Please refer to Figure 1. Figure 1 The logical relationship diagram of a dynamic early warning and coordination system for supply risks of key materials provided by the present invention is as follows; the specific operation steps are as follows: The data acquisition layer is used to collect material supply data and equipment operation-related data, and to preprocess the collected data. The data acquisition layer includes a multi-source data integration module, which connects to supplier systems, equipment monitoring systems, and industry databases via API interfaces and supports manual data entry. The data preprocessing module is used to clean and normalize the collected data, and convert text data into numerical vectors through a word embedding model.

[0021] Specifically, the data acquisition layer collects data across the entire supply chain of materials and related equipment, forming a dual-view data source. The entire supply chain of materials includes: supplier capacity, delivery cycle, prepayment ratio, price fluctuations, quality inspection data, order execution progress, industry capacity utilization rate, and policy change information; Equipment-related data includes: equipment sensor data (such as temperature, corrosion, and operating load of thermal power plant water-cooled walls), maintenance records, fault history data, and equipment life cycle data; Data collection method: Connect to supplier systems, power plant SCADA systems, and industry databases via API interface, and supplement non-standard data with manual data entry terminals to achieve real-time synchronization (synchronization frequency ≤ 15 minutes). Data preprocessing: Automatically completes data cleaning (removing outliers and filling in missing values) and normalization, and converts text data (such as maintenance records and supplier feedback) into numerical vectors through word embedding to avoid reliance on complex corpora.

[0022] A risk identification and dual-view clustering layer, connected to the data acquisition layer, is used to dynamically identify material supply risks based on preprocessed data using a dual-view fusion clustering algorithm; including: Construct feature vectors for the material supply view and feature vectors for the equipment association view; Based on the feature vectors of the material supply view and the feature vectors of the equipment association view, a comprehensive feature vector is calculated using a weighted fusion formula. The comprehensive feature vector is input into the improved K-means clustering algorithm to dynamically identify risk types, including: delivery delay risk, quality risk, supply disruption risk, and cost inversion risk. In one embodiment, the comprehensive feature vector Z is input into the improved K-means algorithm, and the system completes a full clustering every 24 hours, monitoring data fluctuations in real time. When the supply characteristics of a certain type of material (such as a sudden increase in delivery cycle from 30 days to 60 days) or equipment characteristics (such as a corrosion rate exceeding 0.5 mm / year) trigger a threshold, the system immediately initiates clustering updates to identify risk types (such as the risk of delayed delivery of energy storage cells and the quality risk of materials related to corrosion of thermal power equipment). Specifically, it dynamically identifies key material supply risks (such as delivery delays, substandard quality, supply interruptions, and price inversions) and links them with equipment-related risks for analysis; it adopts an improved dual-view K-means clustering algorithm to integrate material supply characteristics and equipment operation characteristics, solving the problem of inaccurate identification based on a single data dimension.

[0023] The early warning and online collection reminder layer, connected to the risk identification and dual-view clustering layer, is used to generate early warning information and trigger the online collection reminder process based on the identified risk level; including: The graded early warning module is used to calculate the risk level value based on the risk level assessment formula and issue four levels of early warning: blue, yellow, orange, and red. The intelligent collection reminder module is used to automatically generate standardized collection reminder notices and send them to suppliers through multiple channels when an early warning is triggered, and to track feedback status. Specifically, the warning is triggered as follows: warning information is automatically pushed according to the risk level R. Blue is only recorded by the system, yellow is pushed to the purchasing specialist, orange is pushed to the department head, and red is pushed to the enterprise decision-making level, and is also sent to the supplier. Online delivery reminders: After an alert is triggered, the system automatically generates a standardized delivery reminder letter (including order information, agreed delivery date, and current progress deviation), which is pushed to the supplier's contact person through multiple channels such as platform messages, SMS, and email; Delivery tracking: Suppliers provide online feedback on delivery plan adjustments, the system automatically records the feedback and updates the order progress, and automatically resends the delivery reminder every 12 hours if no feedback is received, and red risk alerts are issued every 6 hours.

[0024] A coordinated intelligent recommendation layer, connected to the early warning and online collection layer, is used to obtain solutions from a solution database based on risk type; it includes a database storing multiple coordinated solutions, including: diversified procurement strategy solutions, strategic cooperation solutions, and emergency alternative solutions; Specifically, risk matching: the system intelligently recommends suitable solutions from the solution database based on the risk types derived from clustering (such as delivery delays); Solution Execution: After the user selects a solution, the system automatically generates an execution list (such as the material allocation ratio for splitting orders, contact information for alternative suppliers), and tracks the execution progress; Emergency Coordination: When a red risk is triggered, the system automatically initiates an emergency response, simultaneously pushing out a list of alternative suppliers, suggestions for adjusting prepayments, and strategic cooperation negotiation templates to shorten the coordination cycle.

[0025] A closed-loop optimization layer is used to track the treatment effect and dynamically optimize system parameters; In one embodiment, the system performance is evaluated based on early warning and handling records, follow-up feedback results, and the success rate of plan execution; the dual-view fusion weight, risk level weight, and clustering algorithm threshold are automatically adjusted according to the evaluation results; successful coordination cases are added to the database as new samples to optimize the recommendation logic; Specifically, the effectiveness evaluation includes: the system automatically calculating the accuracy rate of early warnings, the response rate to delivery reminders, and the success rate of solution execution (such as the on-time delivery rate of alternative suppliers). Parameter optimization: Based on the evaluation results, the dual-view fusion weight α, risk level weight β, and clustering threshold are automatically adjusted to improve the accuracy of risk identification; Solution Update: Successful coordination cases (such as the application of new alternative materials) will be automatically entered into the solution database, feature tags will be added, and recommendation logic will be optimized.

[0026] This embodiment provides a dynamic early warning and coordination system for key material supply risks. Through a dual-view data fusion and clustering mechanism, it overcomes the limitations of traditional systems that rely on single-dimensional data. It considers not only the supplier's performance but also real-time equipment operating status, enabling earlier and more accurate identification of hidden equipment risks caused by material issues, thus improving the accuracy and foresight of risk identification and early warning. The system integrates risk identification, tiered early warning, online delivery reminders, intelligent coordination recommendations, and effect evaluation optimization into a fully automated closed loop. Based on the risk level, the system automatically triggers and executes standardized delivery reminders and coordination actions, automatically matching the best solution, significantly reducing the delay and uncertainty of manual intervention, greatly shortening the risk response cycle, and improving supply chain resilience. Through a built-in closed-loop optimization layer, the system can dynamically learn based on the actual effect of each early warning response, automatically adjusting data fusion weights, clustering algorithm parameters, and solution recommendation priorities. This gives the system self-evolution capabilities, allowing it to continuously adapt to changes in the supply chain and equipment operating environment, with the accuracy and efficiency of risk management continuously improving over time.

[0027] like Figure 2 As shown, Figure 2 The present invention provides a method for dynamic early warning and coordination of supply risks for key materials, as detailed below: Step S201: Collect material supply view data and equipment association view data in real time, and perform preprocessing; Step S202: Normalize the feature vectors of the material supply view and the equipment association view; Step S203: Obtain the comprehensive feature vector through the weighted fusion formula; Step S204: Input the comprehensive feature vector into the improved K-means clustering model and output the risk clustering result; Step S205: Assess the risk level based on the clustering results and trigger corresponding early warning and coordination actions.

[0028] Based on the above embodiments, this embodiment will provide a detailed description of step S201: In one embodiment, the system automatically connects to the supplier's ERP system, power plant sensor terminals, and industry data platforms to capture data such as delivery cycle, capacity utilization rate, and corrosion rate in real time. Text data (such as maintenance records "water-cooled wall coking and decoking 3 times") is converted into numerical vectors using the Word2Vec model. Abnormal data (such as price fluctuations exceeding the normal range by 3 times) is automatically marked and triggered for manual verification.

[0029] Based on the above embodiments, this embodiment will provide a detailed description of step S203: In one embodiment, the material supply feature vector is defined as (X = [x1, x2, ..., xm]) (where m is the supply feature dimension, such as delivery cycle fluctuation, price increase, etc.), and the equipment association feature vector is defined as (Y = [y1, y2, ..., yn]) (where n is the equipment feature dimension, such as corrosion rate, failure frequency, etc.).

[0030] Feature fusion formula: A comprehensive feature vector Z is obtained through weighted fusion, with weights α generated by training on historical risk data (when supply risk is dominant). When equipment-related risks dominate , ,in It is a normalization function to ensure that the characteristic dimensions are consistent, and its value range is [0,1].

[0031] Based on the above embodiments, this embodiment will provide a detailed description of step S204: In one embodiment, cluster centers are initialized; the similarity between samples and cluster centers is calculated using the cosine similarity formula; cluster centers are dynamically adjusted based on the similarity, and an iteration termination condition is set to obtain risk clustering results.

[0032] Specifically, dynamic risk clustering algorithms (improved K-means) include: Initialize cluster centers , Number of risk types (preset to 4: delivery delay risk, quality risk, supply interruption risk, cost inversion risk); The similarity between a sample and its cluster center is calculated using the cosine similarity formula:

[0033] in, For the first The comprehensive feature vector of each sample For the first Cluster centers for risk-like clusters; The cluster centers are updated dynamically based on the sample distribution, and the calculation formula is as follows:

[0034] in, For the number of iterations, For the first The number of samples for each risk class For the first A sample set of risk categories; Set the iteration termination condition (cluster center change ≤ 0.001 or iteration count ≥ 50), output the final risk clustering result, and realize dynamic updates (automatic re-clustering every 24 hours, and triggering immediate updates when data fluctuation exceeds 20%).

[0035] Based on the above embodiments, this embodiment will provide a detailed description of step S205: In one embodiment, the risk level is calculated based on the clustering results, combined with the degree of risk impact (such as the amount of loss and the duration of downtime) and the probability of occurrence. : ,in This represents the probability of risk occurring (calculated from the cluster density). Risk impact coefficient (levels 1-5, corresponding to losses from less than 100,000 yuan to more than 10 million yuan). These are the weighting coefficients; Risk level classification: Blue (low risk) Yellow (general risk) Orange (higher risk) Red (Extremely High Risk) After assessing the risk level based on the clustering results and triggering corresponding early warning and coordination actions, the following steps are taken: Receive risk clustering results and risk levels; Based on the risk level, the system automatically generates and sends tiered early warning information to the corresponding internal personnel and suppliers. For orders or materials that have been flagged for alerts, standardized online reminder notices are automatically generated and sent to suppliers, establishing a reminder feedback tracking process; Based on the risk type of the risk clustering results, intelligent matching and coordination solutions are obtained from a preset solution database; Track and record the early warning response, follow-up feedback results, and the effectiveness of coordination plan implementation, and feed the results data back to the risk identification model and database to optimize system parameters and recommendation logic.

[0036] This embodiment provides a dynamic early warning and coordination method for key material supply risks. Through a dual-view data fusion and clustering mechanism, it overcomes the limitations of traditional systems that rely on single-dimensional data. It considers not only the supplier's performance but also real-time equipment operating status, enabling earlier and more accurate identification of hidden equipment risks caused by material issues, thus improving the accuracy and foresight of risk identification and early warning. It integrates risk identification, tiered early warning, online delivery reminders, intelligent coordination recommendations, and effect evaluation optimization into a fully automated closed loop. The system can automatically trigger and execute standardized delivery reminder and coordination actions based on the risk level, automatically matching the best solution, significantly reducing the delay and uncertainty of manual intervention, greatly shortening the risk response cycle, and improving supply chain resilience. Through a built-in closed-loop optimization layer, the system can dynamically learn based on the actual effect of each early warning response, automatically adjusting data fusion weights, clustering algorithm parameters, and solution recommendation priorities. This gives the system self-evolution capabilities, allowing it to continuously adapt to changes in the supply chain and equipment operating environment, with the accuracy and efficiency of risk management continuously improving over time.

[0037] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A dynamic early warning and coordination system for supply risks of key materials, characterized in that, include: The data acquisition layer is used to collect material supply data and equipment operation-related data, and to preprocess the collected data. The risk identification and dual-view clustering layer, connected to the data acquisition layer, is used to dynamically identify material supply risks based on preprocessed data using a dual-view fusion clustering algorithm. The early warning and online collection layer is connected to the risk identification and dual-view clustering layer, and is used to generate early warning information and trigger the online collection process based on the identified risk level. The intelligent recommendation layer is coordinated and connected to the early warning and online collection layer, and is used to obtain solutions from the solution database according to the risk type. The closed-loop optimization layer is used to track the treatment effect and dynamically optimize system parameters.

2. The dynamic early warning and coordination system for key material supply risks according to claim 1, characterized in that, The data acquisition layer includes: The multi-source data integration module is used to connect to supplier systems, equipment monitoring systems and industry databases via API interfaces, and also supports manual data entry. The data preprocessing module is used to clean and normalize the collected data, and convert text data into numerical vectors through a word embedding model.

3. The dynamic early warning and coordination system for key material supply risks according to claim 1, characterized in that, The risk identification and dual-view clustering layer, connected to the data acquisition layer, is used to dynamically identify material supply risks based on preprocessed data using a dual-view fusion clustering algorithm, including: Construct feature vectors for the material supply view and feature vectors for the equipment association view; Based on the feature vectors of the material supply view and the feature vectors of the equipment association view, a comprehensive feature vector is calculated using a weighted fusion formula. By inputting the comprehensive feature vector into the improved K-means clustering algorithm, risk types can be dynamically identified.

4. The dynamic early warning and coordination system for key material supply risks according to claim 3, characterized in that, The types of risks include: delivery delay risk, quality risk, supply disruption risk, and cost inversion risk.

5. The dynamic early warning and coordination system for key material supply risks according to claim 1, characterized in that, The early warning and online payment reminder layer includes: The graded early warning module is used to calculate the risk level value based on the risk level assessment formula and issue four levels of early warning: blue, yellow, orange, and red. The intelligent collection reminder module is used to automatically generate standardized collection reminder notices and send them to suppliers through multiple channels when an alert is triggered, and to track feedback status.

6. The dynamic early warning and coordination system for key material supply risks according to claim 1, characterized in that, The intelligent recommendation layer for coordination solutions includes a database storing various coordination solutions, including diversified procurement strategy solutions, strategic cooperation solutions, and emergency alternative solutions.

7. The dynamic early warning and coordination system for key material supply risks according to claim 6, characterized in that, The closed-loop optimization layer, used to track the treatment effect and dynamically optimize system parameters, includes: Evaluate system performance based on early warning and response records, follow-up feedback results, and plan execution success rate; Based on the evaluation results, the weights of dual-view fusion, risk level, and clustering algorithm thresholds are automatically adjusted. Successful coordination cases are added to the database as new samples to optimize the recommendation logic.

8. A method for dynamic early warning and coordination of supply risks for key materials, characterized in that, include: Real-time acquisition of material supply view data and equipment association view data, and preprocessing of them; Normalize the feature vectors of the material supply view and the equipment association view; The comprehensive feature vector is obtained through a weighted fusion formula; The comprehensive feature vector is input into the improved K-means clustering model, and the risk clustering result is output. The risk level is assessed based on the clustering results, and corresponding early warning and coordination actions are triggered.

9. The dynamic early warning and coordination system for key material supply risks according to claim 8, characterized in that, The step of inputting the comprehensive feature vector into the improved K-means clustering model and outputting risk clustering results includes: Initialize cluster centers; The similarity between the sample and the cluster center is calculated using the cosine similarity formula; Based on the similarity, the cluster centers are dynamically adjusted, and the iteration termination condition is set to obtain the risk clustering results.

10. The method for dynamic early warning and coordination of supply risks for key materials according to claim 8, characterized in that, The step of assessing the risk level based on the clustering results and triggering corresponding early warning and coordination actions includes: Receive risk clustering results and risk levels; Based on the risk level, the system automatically generates and sends tiered early warning information to the corresponding internal personnel and suppliers. For orders or materials that have been flagged for alerts, standardized online reminder notices are automatically generated and sent to suppliers, establishing a reminder feedback tracking process; Based on the risk type of the risk clustering results, intelligent matching and coordination solutions are obtained from a preset solution database; Track and record the early warning response, follow-up feedback results, and the effectiveness of coordination plan implementation, and feed the results data back to the risk identification model and database to optimize system parameters and recommendation logic.

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