Intelligent supply chain tracking system for process monitoring

Abstract

The invention discloses a supply chain intelligent tracking system oriented to process monitoring, relates to the technical field of supply chain intelligent management, and is used for solving the problems of flexibility and anti-risk capability of supply chain breakpoint management. Comprising a supply chain data processing module, an energy transmission analysis module, a breakpoint prediction module and a supply chain adjustment module. According to the method, original data are collected from supply chain nodes, effective features are generated in combination with a time sequence, classification features and a high-dimensional data processing technology, a breakpoint prediction model is constructed through a gradient lifting decision tree, breakpoint risks are identified and evaluated, a supply chain network topology structure is constructed, and propagation paths and influence ranges of the breakpoint risks are calculated recursively. Determining an affected node set, formulating an adjustment and recovery strategy in combination with a prediction result, optimizing resource allocation and logistics routes, quickly recovering key nodes, ensuring continuity of a supply chain, and upgrading an early warning mechanism to dynamically monitor risks, thereby improving management flexibility and anti-risk capability of the supply chain.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent supply chain management, and more specifically, to an intelligent supply chain tracking system oriented to process monitoring. Background Art

[0002] Supply chain management refers to the planning, coordination and control of each link in the supply chain to achieve efficient flow and delivery of products or services from raw material suppliers to end consumers. Supply chain management covers multiple links such as procurement, production, warehousing, transportation, and distribution, involving multiple parties such as suppliers, manufacturers, logistics service providers, retailers, and customers. Its core goal is to optimize resource allocation, shorten delivery time, reduce costs, and improve the overall efficiency and flexibility of the supply chain. Effective supply chain management can respond to changes in market demand, enhance corporate competitiveness, ensure on-time delivery of products, reduce inventory and logistics costs, and maintain a high level of customer service quality.

[0003] Deficiencies in existing technologies: Supply chain management systems often have problems such as inaccurate predictions and delayed responses when dealing with breakpoint risks, which are mainly reflected in the following aspects. First, traditional risk assessment methods mostly rely on simple statistical analysis of historical data, lack of deep modeling of the interactive relationships between multiple nodes and multiple links in complex supply chain networks, resulting in difficulty in accurately identifying high-risk nodes and potential breakpoints. Secondly, such systems are usually unable to monitor the dynamic changes of various links in the supply chain in real time, lack a keen sense of changes in key factors such as logistics, supplier fulfillment rates, and warehousing fluctuations, and are unable to issue early warnings in a timely manner. Especially when sudden breakpoints occur in the supply chain, the response of traditional systems is relatively delayed, and measures are often taken only when the problem has spread to multiple nodes and the scope of influence has expanded. Such lag makes it difficult for supply chain managers to effectively take measures in advance to prevent risks, prolonging the recovery time of supply chain disruptions, and ultimately leading to reduced supply chain efficiency, increased costs, and reduced customer satisfaction. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, there is a solution as follows to solve the problems of flexibility and risk resistance of supply chain breakpoint management in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] Supply chain data processing module, which is used to collect raw data from various nodes and links in the supply chain, perform time series processing, classification feature processing and high-dimensional data processing on the raw data, and generate effective features for model training;

[0007] The breakpoint prediction module is used to build a supply chain breakpoint prediction model using a gradient boosting decision tree, conduct risk assessment on breakpoints in the supply chain, identify the probability of breakpoints occurring, and provide prediction results of supply chain disruption risks;

[0008] Risk propagation recursive module: Based on the supply chain network topology and combined with the results of the supply chain breakpoint prediction model, recursive calculation is performed on the propagation path and impact range of breakpoint risks in the supply chain to determine high-risk nodes and affected node sets;

[0009] The supply chain adjustment module is used to formulate supply chain adjustment and recovery strategies according to the breakpoint impact range results, and implement key node recovery, resource allocation optimization, logistics route adjustment and early warning upgrade mechanism for supply chain management.

[0010] In a preferred embodiment, the method is used to collect raw data from various nodes and links in the supply chain, and perform time series analysis, classification feature conversion, and data dimension reduction preprocessing on the raw data to generate effective features for model training. The specific steps are as follows:

[0011] Collect raw data from each node and link of the supply chain. The types of raw data collected include logistics data, supplier data, warehousing data, market demand data and external factors;

[0012] Perform seasonal decomposition on the time series data in the original data, use difference operation to eliminate the trend effect in the time series data, and use moving average to smooth the original data;

[0013] One-hot encoding and label encoding of categorical data are converted into binary features;

[0014] Use principal component analysis to reduce the dimensionality of the original data;

[0015] Feature selection is performed based on the feature importance of the tree, the importance value of each feature is calculated and output, and feature screening is performed based on the importance value.

[0016] In a preferred embodiment, a supply chain breakpoint prediction model is constructed using a gradient boosting decision tree to perform risk assessment on breakpoints in the supply chain, identify the probability of breakpoints occurring, and provide a prediction result of supply chain disruption risk. The specific steps include:

[0017] The input data of the supply chain breakpoint prediction model includes transportation time, supplier historical delivery time, inventory turnover rate, logistics cost, product shipment frequency, external environment data, time series trend of each feature, seasonal fluctuation, and category characteristics;

[0018] Define and construct model labels, and divide labels in supply chain breakpoint prediction into binary classification labels;

[0019] The gradient boosting decision tree was selected as the supply chain breakpoint prediction model, and grid search was performed on different hyperparameter combinations to optimize the hyperparameter settings;

[0020] Use 70% of the original data as the training set to train the supply chain breakpoint prediction model, and the remaining 30% of the original data as the validation set;

[0021] After the supply chain breakpoint prediction model is trained, it is evaluated using the validation set to determine the accuracy, F1 score, and area under the ROC curve;

[0022] If the supply chain breakpoint prediction model is evaluated and achieves the expected results, it will be put into use to calculate the probability of supply chain breakpoints and predict the risk of supply chain disruptions.

[0023] In a preferred embodiment, based on the supply chain network topology and combined with the results of the supply chain breakpoint prediction model, the propagation path and impact range of the breakpoint risk in the supply chain are recursively calculated to determine the high-risk nodes and the affected node set. The specific steps are as follows:

[0024] According to the output data of the supply chain breakpoint prediction model, the supply chain breakpoints are determined and the network topology of the supply chain is constructed;

[0025] The supply chain network topology is constructed as a graph, which consists of a set of nodes in the supply chain and a set of connecting edges between the nodes;

[0026] The weight of two node edges is determined by the nonlinear combination of material flow, transaction volume and transportation frequency of the two node edges;

[0027] Based on the supply chain network topology, a breakpoint risk propagation matrix is ​​established. The risk propagation matrix represents the risk propagation in the network when a breakpoint occurs at a node.

[0028] After the supply chain breakpoint prediction model detects a breakpoint in a supply chain node, the breakpoint propagation matrix is ​​used to recursively calculate the impact range of the breakpoint, evaluate the breadth and depth of the breakpoint propagation from the initial node to each node, and determine the set of affected nodes under the high-risk node.

[0029] In a preferred embodiment, evaluating the breadth and depth of the breakpoint propagation from the initial node to each node and determining the set of nodes affected by the high-risk node includes the following steps:

[0030] The breakpoint nodes detected by the supply chain breakpoint prediction model are used as the high-risk node set for recursive calculation. Each node in the high-risk node set represents a node where a breakpoint occurs in the supply chain. The high-risk node set is used as the starting point of recursive propagation.

[0031] For each initial breakpoint node, the risk propagation intensity of all adjacent nodes is calculated. The propagation intensity is based on the elements in the breakpoint risk propagation matrix. When the propagation intensity exceeds the threshold, the risk spreads to the next node.

[0032] When the recursive calculation is terminated, the impact range is output, and the output results include the affected node set, the propagation path, and the cumulative propagation intensity of the impact.

[0033] In a preferred embodiment, the system is used to formulate supply chain adjustment and recovery strategies based on the breakpoint impact range results, and implement key node recovery, resource allocation optimization, logistics route adjustment and early warning upgrade mechanism for supply chain management, including the following steps:

[0034] Based on the output set of affected nodes, nodes in the supply chain are identified and restored first according to the importance and vulnerability calculation results of the nodes;

[0035] Prioritize recovery of node P i The ranking is comprehensively evaluated by node dependency and influence strength: Among them, d ij is node v i , v j The dependence between ij is the risk transmission rate, C i Is the recovery node v i the cost of resources required;

[0036] Assign the recovery task of high-priority nodes to the supply chain management team and immediately deploy resources (to restore node functions, monitor the status of each node during the recovery process, and ensure that these nodes are given priority;

[0037] Carry out resource reallocation and dynamic logistics route adjustment. According to the propagation path analysis, identify the logistics paths between the affected nodes and the bottleneck nodes in the supply chain. The logistics route optimization is carried out through resource flow F ij Solve the maximization formula of: Among them, F ij is node v i To node v j The material flow, r ij is the risk transmission rate, L ij It is the physical distance or transportation delay of the logistics route;

[0038] Supply chain managers dynamically select the optimal transportation routes based on resource flows;

[0039] While implementing measures to prioritize node restoration, resource reallocation, and dynamic logistics route adjustment, a dynamic risk monitoring and early warning upgrade mechanism is initiated. The supply chain intelligent tracking system continuously monitors changes in node status and propagation paths based on real-time data streams, identifies new potential breakpoints, and upgrades the early warning level.

[0040] The technical effects and advantages of the supply chain intelligent tracking system for process monitoring of the present invention are as follows:

[0041] The present invention collects multi-dimensional raw data from each node and link of the supply chain, combines time series processing, classification feature processing and high-dimensional data processing technology to generate effective features that can be used for model training. The data features can comprehensively reflect the operating status of the supply chain, especially the dynamic changes in logistics, suppliers, warehousing, market demand and other links. By using the gradient boosting decision tree to build a supply chain breakpoint prediction model, the system can accurately identify potential breakpoint risks in the supply chain and evaluate the probability of breakpoint occurrence. The prediction results generated by the model help supply chain managers take preventive measures before the breakpoint occurs, avoid supply chain interruptions, and improve the stability and reliability of the overall supply chain.

[0042] Through the combination of the supply chain network topology structure and the breakpoint prediction model, the recursive propagation calculation of breakpoint risks is realized, which can accurately determine the high-risk nodes and the affected node sets, identify the breakpoint propagation path and impact range, and provide early warning for breakpoints. Targeted adjustment and recovery strategies can also be formulated based on the results of the recursive calculation. Combined with the resource allocation optimization and logistics route adjustment functions, the system can quickly respond and restore key nodes when a breakpoint occurs, ensuring the continuity of supply chain operations. In addition, the early warning upgrade mechanism can dynamically monitor emerging risks, effectively improving the flexibility and risk resistance of supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the structure of the supply chain intelligent tracking system for process monitoring of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In order to achieve the above objectives, Figure 1A schematic diagram of the structure of the supply chain intelligent tracking system for process monitoring of the present invention is given, which specifically includes a supply chain data processing module, an energy transmission analysis module, a breakpoint prediction module and a supply chain adjustment module, and each module is connected by a signal;

[0046] Supply chain data processing module, which is used to collect raw data from various nodes and links in the supply chain, perform time series processing, classification feature processing and high-dimensional data processing on the raw data, and generate effective features for model training;

[0047] The breakpoint prediction module is used to build a supply chain breakpoint prediction model using a gradient boosting decision tree, conduct risk assessment on breakpoints in the supply chain, identify the probability of breakpoints occurring, and provide prediction results of supply chain disruption risks;

[0048] Risk propagation recursive module: Based on the supply chain network topology and combined with the results of the supply chain breakpoint prediction model, recursive calculation is performed on the propagation path and impact range of breakpoint risks in the supply chain to determine high-risk nodes and affected node sets;

[0049] The supply chain adjustment module is used to formulate supply chain adjustment and recovery strategies according to the breakpoint impact range results, and implement key node recovery, resource allocation optimization, logistics route adjustment and early warning upgrade mechanism for supply chain management.

[0050] Collect historical and real-time data from various nodes and links in the supply chain. The types of raw data include logistics data (such as transportation time and transportation routes), supplier data (such as delivery time and historical fulfillment rate), warehousing data (such as inventory changes and inbound / outbound cycles), market demand data (such as sales volume and customer order status), and external factors (such as weather and economic conditions). The collected data will serve as the basic input for building subsequent forecasting models;

[0051] Data collection is the basis for supply chain breakpoint prediction. By obtaining raw data from multiple sources, the system can establish a comprehensive view of the status of supply chain nodes and the impact of the external environment. Data in different formats need to be standardized to ensure that the data can be effectively used by subsequent machine learning algorithms. For example, logistics data may need to be obtained through a GPS tracking system, supplier fulfillment rate data may be extracted from historical order records, and weather and economic factors may be obtained through third-party service interfaces. When sorting data, it is necessary to deal with problems such as missing values ​​and noise data to ensure the quality of input data;

[0052] After collecting and organizing the data, the system needs to convert the raw data into features that are helpful for model training through feature extraction and feature engineering, perform time series analysis, classification feature conversion, and data dimensionality reduction on the raw data. The specific steps are as follows:

[0053] Seasonal decomposition is performed on the time series data X(t) collected from the original data, and it is decomposed into three parts: trend T(t) represents the long-term change direction, seasonality S(t) represents the cyclical fluctuation of the data (such as the difference in order volume of the supply chain in different quarters), and random noise R(t) is an unexplained random fluctuation. For example, the transportation time X(t) in the logistics data can be decomposed in this way to extract the trend and seasonal fluctuation characteristics that affect transportation delays. The extracted features will be used as the input of the model to predict whether there will be transportation delays in the future.

[0054] Furthermore, the difference operation is used to eliminate the trend effect in time series data and generate a stable sequence. For the sequence X(t), the first-order difference calculation formula is: X'(t) = X(t)-X(t-1), where t represents the time point. After the difference operation, short-term fluctuations can be captured and the influence of long-term trends can be eliminated. It is particularly suitable for capturing sharp fluctuations in the supply chain in the short term (such as sudden transportation delays);

[0055] Moving average is used to smooth the original data, eliminate short-term fluctuations, and obtain long-term trends. For the moving average with a window size of k, the formula is as follows: Moving average can ignore short-term abnormal fluctuations and focus on long-term trends; for example, the moving average of logistics time can capture the average transportation time over a period of time and help assess whether there will be abnormal delays in current logistics.

[0056] In supply chain data, many features are categorical data (such as supplier category, transportation method, warehouse location, etc.). Such data cannot be directly used in machine learning models and need to be converted into numerical features through categorical feature conversion. Common methods include One-Hot encoding and label encoding. One-Hot encoding converts categorical variables into multiple binary features. For example, assuming that there are three modes of transportation in the supply chain: "air transport", "sea transport", and "land transport", they can be represented by three binary variables: (1, 0, 0) for air transport, (0, 1, 0) for sea transport, and (0, 0, 1) for land transport.

[0057] In the case of high-dimensional features, the data may contain many redundant or irrelevant features. In order to reduce the complexity of the subsequent model and improve the computational efficiency, data dimensionality reduction techniques such as principal component analysis (PCA) are used in feature engineering to reduce the dimensionality of high-dimensional data, project the high-dimensional data into a low-dimensional space, and retain the main change trend of the data;

[0058] Assume that there are 100 different features in the supply chain, such as shipping time, inventory level, productivity, etc. PCA can compress these high-dimensional features into fewer principal components (such as 10), ensuring that the model can still capture the main trends while improving computational efficiency;

[0059] After preprocessing the original data, that is, after feature extraction and conversion, the feature selection method is used to screen out the most predictive features. The tree-based feature importance is used for feature selection. Tree models such as random forests or gradient boosted trees can directly output the importance value of each feature. The importance value is evaluated by calculating the contribution of each feature to the model split point. The formula is expressed as: Where ΔG(t) represents the change in the Gini index or information gain of node t in the decision tree, T is the set of all nodes in the decision tree, and I j is the importance of feature j.

[0060] In summary, through the combination of time series analysis, categorical feature transformation and data dimensionality reduction, feature extraction and feature engineering steps can effectively transform raw data into structured features that can be used by the model. These features can help the model capture the complex patterns of supply chain breakpoints and improve prediction accuracy.

[0061] Build a supply chain breakpoint prediction model that can accurately capture the relationship between input data (features) and output data (labels), and has good generalization capabilities, can adapt to changes in different supply chain nodes, and effectively predict possible future breakpoints;

[0062] The processed features are used as input data (features), including transportation time, supplier historical delivery time, inventory turnover rate, logistics cost, product shipment frequency, external environment data (such as weather, market demand fluctuations), etc. The time series trend, seasonal fluctuations, and category characteristics (such as different suppliers and logistics companies) of each feature can be used as input;

[0063] Define and build model labels, and divide the labels in the supply chain breakpoint prediction into two categories (0 means no breakpoint, 1 means a breakpoint occurs). The definition of labels can be based on the records of historical breakpoint events and combined with business needs. Specifically:

[0064] Output data (labels): Breakpoint prediction is usually a binary classification problem. The label 1 represents a breakpoint (such as delivery delay, logistics interruption), and 0 represents no breakpoint. The generation of labels can be based on actual breakpoint events in the historical supply chain. For example, a delivery time exceeding the scheduled time by X days can be defined as a breakpoint. The breakpoint label needs to be synchronized with the feature. For example, if the system wants to predict whether there will be a supply chain interruption in the next week, the breakpoint state corresponding to the label needs to be aligned with the time point of the input feature. For example, to predict the delivery status of a supplier in the next month, the label 1 means that the supplier may delay delivery (more than 3 days), and the label 0 means that the supplier delivers on time.

[0065] Select the gradient boosting decision tree (XGBoost) as the model. XGBoost has strong performance in dealing with high-dimensional data, feature complexity and nonlinear problems. By integrating multiple decision trees, it gradually corrects errors, improves prediction accuracy, and minimizes the objective function with regularization terms: Among them, the loss function Used to measure the error between the model prediction value and the actual value, regularization term Used to control the complexity of the model and avoid overfitting; i Represents the true label of the i-th sample; represents the predicted value of the i-th sample predicted by the model; f k represents the kth tree; K represents the number of trees;

[0066] Represents the loss function, which is used to measure the error between the model prediction value and the true label. The form of the loss function is different in different tasks;

[0067] The key hyperparameters in the gradient boosting decision tree include: the number of trees indicates how many trees are built. A larger number of trees can capture more details, but may lead to overfitting. The default value is generally 100; the maximum depth of the tree is used to control the depth of each tree. A larger depth will make the model more complex and suitable for scenarios with more complex data; the learning rate is used to control the learning rate of each tree. A smaller learning rate can improve the generalization ability of the model, but more trees are required; the proportion of tree construction samples;

[0068] In the supply chain breakpoint prediction model, the hyperparameters are set as follows: 200 trees are used to improve the accuracy of the model, the maximum depth of each tree is set to 6, which is suitable for capturing complex feature relationships, and a small learning rate of 0.05 is set to enhance generalization ability. 80% of the samples are used for tree construction to prevent model overfitting;

[0069] Conduct model training and hyperparameter optimization. Train the model using historical data and use the gradient boosting algorithm to iteratively optimize the prediction error. During the training process, the model's hyperparameters also need to be tuned. Common methods include grid search or random search.

[0070] The logarithmic loss function is usually used for binary classification problems: Used to measure the deviation between the model prediction value and the actual label;

[0071] Use 70% of the original data as the training set for model training, and the remaining 30% as the validation set;

[0072] Use K-fold cross-validation to prevent overfitting and ensure generalization.

[0073] Perform hyperparameter optimization, that is, perform grid search for different hyperparameter combinations (such as tree depth, learning rate, subsample ratio) to find the optimal hyperparameter settings, and use an early stopping mechanism, that is, if the performance of the model on the validation set does not improve after a few rounds, stop training early;

[0074] When optimizing hyperparameters using grid search, test different combinations of learning rates and tree depths.

[0075] After the model training is completed, an independent validation set or test set is used to evaluate the model. The main indicators used include accuracy, F1 score, area under the ROC curve (AUC), etc. In addition, the generalization ability depends on the performance of the model on unseen data, especially the ability to predict supply chain disruption scenarios;

[0076] The generalization ability of the model depends on feature selection, hyperparameter setting, and the control of model complexity by regularization terms. The generalization ability of the model can be improved by controlling the depth of the tree and the regularization term to prevent overfitting. For the extensive requirements of input features, the processing of time series data (such as difference and moving average) can capture the long-term changes of features and improve the model's prediction accuracy for future supply chain breakpoints.

[0077] After the supply chain breakpoint prediction model has been evaluated and achieved the expected effect, it can be deployed to the production environment to predict the supply chain breakpoint risk in real time. The system regularly inputs real-time data through the supply chain management platform, and the model derives risk scores through reasoning and generates early warning signals;

[0078] For example, a global electronic equipment company (XYZ Company) relies on multiple suppliers and logistics partners to transport electronic components from supplier warehouses to various production bases. Since the supply chain involves multiple countries and different modes of transportation (air, sea, and land), logistics delays have become a key issue for the company's production efficiency. In order to avoid production line interruptions due to logistics delays, XYZ Company decided to build a supply chain breakpoint prediction model to predict logistics delay risks in advance and issue early warnings;

[0079] Input data (features) include: Logistics history data: logistics delay days, transportation mode (air, sea, land), transportation distance, transportation time, route congestion of all transportation batches in the past year; Supplier data: supplier's historical delivery on-time rate, number of delivery batches; Inventory data: inventory level of each production base, inventory turnover days; Market demand data: changes in customer orders in the past 6 months, fluctuations in market demand; External factors: weather conditions on transportation routes, port congestion, political risks (such as customs delays, tariff adjustments, etc.);

[0080] Output data (labels), the logistics delay label is whether logistics delay occurs (the label is 1 for delay and 0 for on-time arrival), and delay is defined as the transportation time exceeding the scheduled arrival time by more than 3 days;

[0081] One-hot coding is performed on the modes of transport (air, sea, and land), moving average processing is used on historical transport time data, trend features are extracted, and numerical features such as inventory levels and supplier delivery on-time rates are normalized to ensure the consistency of model inputs;

[0082] Select XGBoost as the core model setting, set the hyperparameters to 200 trees, the maximum depth of each tree to 6, the learning rate to 0.05, use 80% of the samples to build the tree, and the minimum sample weight on the leaf node is 1;

[0083] We use the logistics data from the past year, 70% of which is used for training and 30% for verifying the generalization ability of the model. We use the logarithmic loss function to optimize the prediction effect of the binary classification problem. The goal is to minimize the cost of misclassification. We test different hyperparameter combinations through grid search and select the optimal combination.

[0084] The model was evaluated and the accuracy of the model on the validation set was 0.88, indicating that the model can well distinguish delayed and non-delayed logistics batches; the F1 score was 0.85, indicating that the model has a strong ability to capture delayed events and has a good balance between the prediction of positive and negative classes; the area under the ROC curve was 0.92, indicating that the overall performance of the model is excellent and can well distinguish batches with logistics delays from those without delays;

[0085] The generalization ability of the model was tested by K-fold cross validation (K=5). The results showed that the AUC in each fold exceeded 0.90, indicating that the model performed well on different data sets and could be generalized to different transportation routes, suppliers, and modes of transportation.

[0086] XYZ Company deploys the optimized XGBoost model in its supply chain management platform. The platform receives data from suppliers, logistics partners, and market demand every day, and performs real-time reasoning through the model, as follows:

[0087] The platform collects real-time transportation time, route status, weather forecast and port conditions of logistics batches every day. The model calculates the risk score of delay for each transportation batch in real time. When the delay risk score of a batch exceeds 70% (for example, an important transportation route has a 75% probability of delay), the system will issue an early warning and notify logistics managers to reschedule routes or increase capacity.

[0088] According to the output data of the supply chain breakpoint prediction model, the supply chain breakpoints are determined and the network topology of the supply chain is constructed. This structure represents each node in the supply chain (such as suppliers, manufacturers, logistics nodes, warehouses, customers, etc.) in the form of a graph. Each node is connected to its upstream and downstream nodes by edges. The weight of the edge represents the dependency between the nodes. It is calculated based on data such as material flow, transaction volume, and transportation frequency in the supply chain, as follows:

[0089] Suppose the supply chain network topology is a graph G = (V, E), where V represents the set of nodes in the supply chain (such as suppliers, manufacturers, logistics nodes, etc.), and E represents the set of connecting edges between nodes;

[0090] For any two nodes v i , v j ∈V, edge weight w ij The material flow between the two ij , transaction volume ij and the transport frequency q ij The nonlinear combination of is determined by: Among them, α, β1, β2, γ are parameters to be adjusted, and ∈ is a minimum value used to prevent the denominator from being zero;

[0091] The construction of the supply chain network topology is based on the dependency between nodes. The dependency is comprehensively reflected through the original data. Material flow and transportation frequency are important indicators of the interaction intensity between nodes, and transaction volume reflects the dependency at the economic level. Through the design of nonlinear formulas, the weights in the network can flexibly capture the dependency changes between different supply chain nodes.

[0092] The breakpoint risk propagation matrix R is constructed based on the supply chain network topology structure to describe the risk propagation in the network when one or more nodes have breakpoints. Each element r of the risk propagation matrix is ij Defined as: Where N(i) represents the node v i The set of neighbor nodes; η i Represents node v i The vulnerability of the node is determined by many factors, such as the node's resource redundancy, response time, and historical interruption rate.

[0093] Node vulnerability η i Reflects the node v i In the supply chain, the node with higher vulnerability is more susceptible to external risks when a breakpoint occurs, and spreads the risk to downstream nodes. Vulnerability calculation is usually based on the following factors:

[0094] Resource redundancy means that the node with less redundancy is more vulnerable; the slower the response speed of the response time node, the worse its ability to handle external shocks; the higher the vulnerability, the historical breakpoint rate means that the node with more interruptions is usually more vulnerable;

[0095] It should be noted that the construction of the risk propagation matrix is ​​mainly used to quantify the diffusion pattern of breakpoint risks in the supply chain network. Through the normalized edge weights, it can be ensured that the risk propagation of each node is relative and depends on the connection strength of its adjacent nodes. At the same time, the node vulnerability is used to reflect the self-protection ability of the node. The more vulnerable the node is, the more likely it is to be affected in risk transmission.

[0096] After the supply chain breakpoint prediction model detects a breakpoint in one or more supply chain nodes, the breakpoint propagation matrix is ​​used to recursively calculate the impact range of the breakpoint. Specifically, the breakpoint risk propagates from the initial breakpoint node, propagates to its adjacent nodes, and then continues to propagate outward until the propagation intensity is lower than the set threshold.

[0097] Based on the construction of the supply chain network topology and the breakpoint risk propagation matrix to quantify the dependencies and risk propagation relationships between nodes, this step recursively calculates the impact range of the supply chain breakpoints to evaluate the breadth and depth of the breakpoints from the initial node to other nodes. Starting from the initial breakpoint node, the risk propagation between adjacent nodes is calculated in turn, and the scope of the propagation is determined. The specific steps are as follows:

[0098] After the supply chain breakpoint prediction model detects that one or more nodes have breakpoints, these breakpoint nodes are used as the high-risk node set S0 for recursive calculation. The high-risk nodes are the source of the supply chain breakpoint propagation; that is, let S0 be the initial breakpoint node set, and each node v i ∈S0 represents the node where the breakpoint occurs in the supply chain. The high-risk node set S0 is used as the starting point of recursive propagation. The set can include suppliers, logistics nodes, or warehousing nodes, etc.;

[0099] For each initial breakpoint node v i , calculate its risk propagation intensity to all adjacent nodes, the propagation intensity is based on the element r in the breakpoint risk propagation matrix ij , which represents the node v i For adjacent nodes v i The impact of risk transmission intensity I j From Node v i Propagate to neighboring nodes v j Defined as: Among them, r ij is an element in the risk propagation matrix, r ij Represents node v iFor node v j The risk transmission rate; τ is the transmission threshold, 1(r ij >τ) is the indicator function. When the transmission intensity exceeds the threshold τ, the risk will spread to the next node;

[0100] This step describes the local propagation of the breakpoint impact. The risk will only propagate from one node to another when the risk propagation rate between nodes exceeds the preset propagation threshold. In this way, the propagation range can be controlled to avoid excessive alarms.

[0101] Recursively update the impact range. After calculating the risk propagation intensity of the initial breakpoint node, the risk begins to spread to its adjacent nodes. Each affected adjacent node will become a new breakpoint node and continue to propagate the risk to its adjacent nodes until the propagation intensity of all nodes is lower than the propagation threshold.

[0102] Assume I(t) represents the influence range at time t, and I(t+1) represents the influence range at the next time. Then the recursive update formula is: I(t+1)=I(t)+∑ i∈I(t) ∑ j∈N(i) r ij ·1(r ij >τ), where N(i) is the node v i The neighbor node set of the new influence range is recursively added to the node with strong propagation intensity until no new node meets the propagation condition.

[0103] After the initial breakpoint node propagates the risk to its adjacent nodes, if the adjacent nodes meet the propagation conditions (propagation intensity is greater than the threshold), they will continue to propagate to their neighboring nodes, recursively updating the gradual diffusion process of the capture breakpoint until all possible propagation paths are evaluated;

[0104] When the propagation intensity of all nodes is lower than the propagation threshold, or the recursive calculation reaches the preset maximum number of iterations, the risk propagation terminates, that is, when the propagation path no longer expands or reaches the preset number of iterations, the recursive process ends, and the impact range of the breakpoint can be determined at this time;

[0105] When the recursive calculation is terminated, the system outputs the breakpoint impact range I(t) as the analysis result of the breakpoint's impact on the entire supply chain. The output results include the affected node set, propagation path, and the cumulative propagation intensity of the impact. This result can be used by supply chain managers to formulate response strategies. The specific steps are as follows:

[0106] Based on the output of the affected node set, the system identifies the most important nodes in the supply chain. These nodes are highly dependent on the entire supply chain, usually including suppliers, logistics hubs, storage centers, etc. Based on the importance and vulnerability calculation of the nodes, the nodes that are critical to the entire supply chain are restored first to prevent further spread. i The ranking can be evaluated comprehensively by node dependency and influence strength: Among them, d ij is node v i , v j The dependence between ij is the risk transmission rate, C i Is the recovery node v i The required resource cost, nodes with higher priority should be restored first;

[0107] Key nodes on the transmission path (such as logistics centers or major suppliers) have a greater impact on the overall supply chain, so more resources should be allocated to restore the normal operation of these nodes as soon as possible. The focus of the response strategy is to prevent the breakpoint risk from spreading to a wider range and affecting more nodes;

[0108] Specifically, the recovery task of high-priority nodes is assigned to the supply chain management team, and resources are immediately deployed (such as rescheduling logistics and accelerating supplier delivery) to restore node functions. The real-time monitoring module of the system is called to monitor the status of each node during the recovery process to ensure that these nodes are given priority.

[0109] Reallocate resources and dynamically adjust logistics routes. Based on the propagation path analysis, the system identifies the logistics paths between the affected nodes and the bottleneck nodes in the supply chain. Through the intelligent tracking system to monitor logistics data in real time, the system can dynamically adjust the logistics routes, optimize resource allocation, and ensure that the flow of materials is not affected. Logistics route optimization can be achieved through resource flow F ij Solve the maximization formula of: Among them, F ij is node v i To node v j The material flow, r ij is the risk transmission rate, L ij It is the physical distance or transportation delay of the logistics route;

[0110] In this strategy, the system optimizes the logistics paths in the supply chain in real time to ensure that the supply of materials to the affected nodes is not interrupted. Through the system's path tracking, supply chain managers can dynamically select the optimal transportation route to avoid logistics delays on high-risk transmission paths, while balancing material distribution to ensure that the resource supply of key nodes is prioritized.

[0111] Specifically, the supply chain management platform can be used to adjust logistics routes and transfer materials from affected areas to alternative routes or hub nodes. The dynamic route planning function embedded in the system can update routes in real time and optimize material flows through automatic scheduling algorithms. With the help of technologies such as drones and autonomous vehicles, key materials can be quickly transported to high-priority nodes to ensure the continuity of the supply chain.

[0112] While implementing countermeasures, the system can also start dynamic risk monitoring and early warning upgrade mechanisms. The supply chain intelligent tracking system continuously monitors changes in node status and propagation paths based on real-time data streams, identifies new potential breakpoints and promptly upgrades the early warning level so that more in-depth protective measures can be taken;

[0113] Through the above-mentioned specific response strategies, supply chain managers can take quick action after a breakpoint occurs to ensure the stability and continuity of the supply chain. The supply chain intelligent tracking system not only helps identify the impact scope of the breakpoint, but also ensures that the supply chain can recover quickly and resist future risks through real-time monitoring, dynamic scheduling and long-term optimization.

[0114] It should be noted that the relevant threshold information in this embodiment is pre-set by professionals and will not be explained in detail here. For example, the threshold for comparing the propagation intensity and the intensity, etc., some parameters in the embodiments have the same English letters, but different meanings are explained when used, which will not be explained one by one here.

[0115] The present invention collects multi-dimensional raw data from each node and link of the supply chain, combines time series processing, classification feature processing and high-dimensional data processing technology to generate effective features that can be used for model training. The data features can comprehensively reflect the operating status of the supply chain, especially the dynamic changes in logistics, suppliers, warehousing, market demand and other links. By using the gradient boosting decision tree to build a supply chain breakpoint prediction model, the system can accurately identify potential breakpoint risks in the supply chain and evaluate the probability of breakpoint occurrence. The prediction results generated by the model help supply chain managers take preventive measures before the breakpoint occurs, avoid supply chain interruptions, and improve the stability and reliability of the overall supply chain.

[0116] Through the combination of the supply chain network topology structure and the breakpoint prediction model, the recursive propagation calculation of breakpoint risks is realized, which can accurately determine the high-risk nodes and the affected node sets, identify the breakpoint propagation path and impact range, and provide early warning for breakpoints. Targeted adjustment and recovery strategies can also be formulated based on the results of the recursive calculation. Combined with the resource allocation optimization and logistics route adjustment functions, the system can quickly respond and restore key nodes when a breakpoint occurs, ensuring the continuity of supply chain operations. In addition, the early warning upgrade mechanism can dynamically monitor emerging risks, effectively improving the flexibility and risk resistance of supply chain management.

[0117] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0118] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0119] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0120] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0121] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0122] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Supply chain intelligent tracking system for process monitoring, characterized by: It includes a supply chain data processing module, an energy transmission analysis module, a breakpoint prediction module, and a supply chain adjustment module, and each module is connected through signals; Supply chain data processing module, which is used to collect raw data from various nodes and links in the supply chain, perform time series processing, classification feature processing and high-dimensional data processing on the raw data, and generate effective features for model training; The breakpoint prediction module is used to build a supply chain breakpoint prediction model using a gradient boosting decision tree, conduct risk assessment on breakpoints in the supply chain, identify the probability of breakpoints occurring, and provide prediction results of supply chain disruption risks; Risk propagation recursive module: Based on the supply chain network topology and combined with the results of the supply chain breakpoint prediction model, recursive calculation is performed on the propagation path and impact range of breakpoint risks in the supply chain to determine high-risk nodes and affected node sets; The supply chain adjustment module is used to formulate supply chain adjustment and recovery strategies according to the breakpoint impact range results, and implement key node recovery, resource allocation optimization, logistics route adjustment and early warning upgrade mechanism for supply chain management.

2. The supply chain intelligent tracking system for process monitoring according to claim 1 is characterized by: It is used to collect raw data from various nodes and links in the supply chain, and perform time series analysis, classification feature conversion, and data dimension reduction preprocessing on the raw data to generate effective features for model training. The specific steps are as follows: Collect raw data from each node and link of the supply chain. The types of raw data collected include logistics data, supplier data, warehousing data, market demand data and external factors; Perform seasonal decomposition on the time series data in the original data, use difference operation to eliminate the trend effect in the time series data, and use moving average to smooth the original data; One-Hot encoding and label encoding of categorical data are converted into binary features; Use principal component analysis to reduce the dimensionality of the original data; Feature selection is performed based on the feature importance of the tree, the importance value of each feature is calculated and output, and feature screening is performed based on the importance value.

3. The supply chain intelligent tracking system for process monitoring according to claim 2 is characterized by: It is used to build a supply chain breakpoint prediction model using a gradient boosting decision tree, conduct risk assessment on breakpoints in the supply chain, identify the probability of breakpoints occurring, and provide prediction results for supply chain disruption risks. The specific steps include: The input data of the supply chain breakpoint prediction model includes transportation time, supplier historical delivery time, inventory turnover rate, logistics cost, product shipment frequency, external environment data, time series trend of each feature, seasonal fluctuation, and category characteristics; Define and construct model labels, and divide labels in supply chain breakpoint prediction into binary classification labels; The gradient boosting decision tree was selected as the supply chain breakpoint prediction model, and grid search was performed on different hyperparameter combinations to optimize the hyperparameter settings; Use 70% of the original data as the training set to train the supply chain breakpoint prediction model, and the remaining 30% of the original data as the validation set; After the supply chain breakpoint prediction model is trained, it is evaluated using the validation set to determine the accuracy, F1 score, and area under the ROC curve; If the supply chain breakpoint prediction model is evaluated and achieves the expected results, it will be put into use to calculate the probability of supply chain breakpoints and predict the risk of supply chain disruptions.

4. The supply chain intelligent tracking system for process monitoring according to claim 3 is characterized by: Based on the supply chain network topology and combined with the results of the supply chain breakpoint prediction model, the propagation path and impact range of the breakpoint risk in the supply chain are recursively calculated to determine the high-risk nodes and the affected node sets. The specific steps are as follows: According to the output data of the supply chain breakpoint prediction model, the supply chain breakpoints are determined and the network topology of the supply chain is constructed; The supply chain network topology is constructed as a graph, which consists of a set of nodes in the supply chain and a set of connecting edges between the nodes; The weight of two node edges is determined by the nonlinear combination of material flow, transaction volume and transportation frequency of the two node edges; Based on the supply chain network topology, a breakpoint risk propagation matrix is ​​established. The risk propagation matrix represents the risk propagation in the network when a breakpoint occurs at a node. After the supply chain breakpoint prediction model detects a breakpoint in a supply chain node, the breakpoint propagation matrix is ​​used to recursively calculate the impact range of the breakpoint, evaluate the breadth and depth of the breakpoint propagation from the initial node to each node, and determine the set of affected nodes under the high-risk node.

5. The supply chain intelligent tracking system for process monitoring according to claim 4 is characterized by: Evaluate the breadth and depth of the breakpoint propagation from the initial node to each node, and determine the set of nodes affected by the high-risk node, including the following steps: The breakpoint nodes detected by the supply chain breakpoint prediction model are used as the high-risk node set for recursive calculation. Each node in the high-risk node set represents a node where a breakpoint occurs in the supply chain. The high-risk node set is used as the starting point of recursive propagation. For each initial breakpoint node, the risk propagation intensity of all adjacent nodes is calculated. The propagation intensity is based on the elements in the breakpoint risk propagation matrix. When the propagation intensity exceeds the threshold, the risk spreads to the next node. When the recursive calculation is terminated, the impact range is output, and the output results include the affected node set, the propagation path, and the cumulative propagation intensity of the impact.

6. The supply chain intelligent tracking system for process monitoring according to claim 5 is characterized by: It is used to formulate supply chain adjustment and recovery strategies based on the breakpoint impact range results, and implement key node recovery, resource allocation optimization, logistics route adjustment and early warning upgrade mechanism for supply chain management, including the following steps: Based on the output set of affected nodes, nodes in the supply chain are identified and restored first according to the importance and vulnerability calculation results of the nodes; Prioritize recovery of node P i The ranking is comprehensively evaluated by node dependency and influence strength: Among them, d ij is node v i , v j The dependence between ij is the risk transmission rate, C i Is the recovery node v i the cost of resources required; Assign the recovery task of high-priority nodes to the supply chain management team and immediately deploy resources (to restore node functions, monitor the status of each node during the recovery process, and ensure that these nodes are given priority; Carry out resource reallocation and dynamic logistics route adjustment. According to the propagation path analysis, identify the logistics paths between the affected nodes and the bottleneck nodes in the supply chain. The logistics route optimization is carried out through resource flow F ij Solve the maximization formula of: Among them, F ij is node v i To node v j The material flow, r ij is the risk transmission rate, L ij It is the physical distance or transportation delay of the logistics route; Supply chain managers dynamically select the optimal transportation routes based on resource flows; While implementing measures to prioritize node restoration, resource reallocation, and dynamic logistics route adjustment, a dynamic risk monitoring and early warning upgrade mechanism is initiated. The supply chain intelligent tracking system continuously monitors changes in node status and propagation paths based on real-time data streams, identifies new potential breakpoints, and upgrades the early warning level.

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