Full-link intelligent monitoring and arriving system for express logistics
By constructing an intelligent monitoring and outreach system for the entire express logistics chain, and utilizing distributed sensors and machine learning algorithms, abnormal nodes are monitored in real time and dynamically evaluated. This solves the problems of insufficient real-time performance and intelligence in traditional express logistics management, and improves operational efficiency and user experience.
Patent Information
- Application Number
- CN202511456285.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-13
- Publication Date
- 2026-02-27
AI Technical Summary
Traditional express logistics management models suffer from poor real-time monitoring and user outreach, lack of intelligent means, and low efficiency, resulting in the inability to detect and handle abnormal situations in a timely manner, which affects operational efficiency and user experience.
By employing real-time data acquisition and analysis based on distributed sensors, a dynamic topology network of express parcels, outlets, and links is constructed. Combining historical data and machine learning algorithms, abnormal nodes are automatically marked, risks are dynamically assessed, and the optimal delivery method is selected to achieve intelligent monitoring and delivery.
It enables real-time and precise management of the express delivery and logistics process, improves the efficiency of handling anomalies, shortens the transportation cycle, enhances user satisfaction and corporate economic benefits, and reduces resource waste.
Smart Images
Figure CN121581729A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of express logistics technology, specifically to an intelligent monitoring and outreach system for the entire express logistics chain. Background Technology
[0002] With the rapid development of e-commerce, the express delivery and logistics industry has expanded dramatically, experiencing explosive growth in business volume. Traditional express delivery and logistics management models have gradually exposed numerous problems in handling massive parcel volumes. In terms of monitoring, manual monitoring struggles to achieve real-time and accurate control over all aspects of the supply chain, resulting in untimely and inaccurate information transmission, hindering the timely detection and handling of anomalies such as delayed or lost parcels. Regarding user outreach, the lack of intelligent methods prevents the timely and proactive provision of effective information based on parcel status and user needs, leading to a poor user experience. In the anomaly handling phase, reliance on manual tracking and coordination is inefficient, making it difficult to quickly resolve user issues and impacting the operational efficiency and competitiveness of express delivery and logistics companies. Summary of the Invention
[0003] To address the aforementioned technical issues, this solution provides an intelligent monitoring and outreach system for the entire express logistics chain. This technical solution resolves the problems of manual monitoring, which struggles to achieve real-time monitoring of all aspects of the entire chain, lacks intelligent methods, and relies on manual order tracking and coordination, resulting in low efficiency.
[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Intelligent monitoring and outreach methods for the entire express delivery and logistics chain include: Based on distributed sensors, real-time status data of each logistics node in the entire express delivery chain is collected to construct a dynamic topology network of express delivery, outlet and link. Based on historical express logistics data, the dynamic topology network of express-outlets-links is associated and matched, the time window for each express to be operated at each logistics node is calculated, the actual operation time of each express is compared, the timeliness deviation of express logistics nodes is dynamically quantified, and abnormal nodes and responsible parties are automatically marked. Based on the automatic marking of abnormal nodes and responsible entities, and combined with the business rules of responsible outlets, an abnormal node risk assessment index system is constructed to calculate the risk entropy value of each abnormal node and realize the intelligent classification of abnormal nodes and responsible entities. Based on the intelligent classification of abnormal nodes and responsible entities, the system dynamically selects the optimal contact method and simultaneously activates the order processing flow of the responsible outlet. For unresolved abnormal nodes, the system automatically triggers a dual-path response mechanism of work order escalation and customer emotional reassurance, thereby achieving intelligent monitoring and secure delivery across the entire express logistics chain.
[0005] Preferably, the system uses distributed sensors to collect real-time status data of each logistics node throughout the entire express delivery chain. The status data of each logistics node in the entire express delivery chain includes: waybill number, geographical location, logistics outlet, operation type, and operator; Using logistics outlets as vertices, we traverse all logistics nodes in the entire express delivery chain and use continuous routing paths in each logistics node in the entire express delivery chain as edges to construct a static express delivery-outlet-link network graph. By utilizing the Apache Kafka distributed stream processing platform, a real-time data channel is established for each logistics node to update the vertex status of the logistics network and the edge status of continuous routing paths in each logistics node of the entire express delivery chain in real time. A fixed time window of ten minutes is set. Based on the status data of each logistics node in the entire express delivery chain, the continuous routing path edges between the logistics network vertex and each logistics node in the entire express delivery chain are calculated. Weights are assigned to each logistics node in the entire express delivery chain to construct a dynamic topology network of express delivery-network-link.
[0006] Preferably, the weighting formula for each logistics node in the entire express delivery chain includes:
[0007] in, The weights of continuous routing path edges in each logistics node of the entire express delivery chain. For time window identification, For the edges in the dynamic topology network of express delivery - outlet - link, This refers to the edge traffic in the dynamic topology network of express delivery, delivery point, and link within a fixed time window of ten minutes. The average operation time for edges in the dynamic topology network of express delivery-network point-process. The standard time consumption for edges in the dynamic topology network of express delivery-network point-process. This is a penalty factor.
[0008] Preferably, based on historical express logistics data, data preprocessing is performed to construct a historical express logistics database and extract successfully completed express logistics order data; For successfully completed express delivery order data, calculate the time between every two consecutive nodes in each express delivery order and generate a historical time table of express delivery order nodes; The historical time consumption table of the express logistics order node includes: route segment, product type, time period type and operation time consumption; Using the profile coefficient analysis method, the average distance between each data point in the historical time consumption table of express logistics order nodes is calculated to obtain the cluster density of the historical time consumption table of express logistics order nodes. Calculate the average distance between each data point in the historical time consumption table of express logistics order nodes and the nearest cluster data point to obtain the inter-cluster separation degree of the historical time consumption table of express logistics order nodes. Based on the intra-cluster tightness and inter-cluster separation of the historical time consumption table of express logistics order nodes, the profile coefficient of each data point in the historical time consumption table of express logistics order nodes is obtained, and the optimal number of clusters k in the historical time consumption table of express logistics order nodes is determined. Based on the historical time consumption table of express logistics order nodes, the optimal number of clusters k is selected as k points as the initial cluster centers; For each data point in the historical timeout table of express logistics order nodes, the Euclidean distance formula is used to calculate the distance from each data point to the initial cluster center of k points; Based on the distance results, the data points are assigned to the nearest cluster center, the mean of all data points in each cluster is recalculated, and the historical time cluster center of the express logistics order node is updated. Repeat the above process until the historical time clustering center of the express logistics order node converges, resulting in k groups of historical time consumption for express logistics order nodes.
[0009] Preferably, based on the historical time grouping of k express logistics order nodes, the mean, variance and standard deviation of the time for each group are calculated; Set excellent timeliness standards and tolerance limits for express logistics order nodes; The excellent timeliness line is at the 50th percentile. The upper limit of tolerance is taken as the mean plus 2 standard deviations; Obtain the operation time window for each express shipment at each logistics node; Based on distributed sensors, real-time status data of each logistics node in the entire express delivery chain is collected, and the dynamic topology network of express delivery-network point-link is associated and matched to obtain the actual operation time of each express delivery at each logistics node. Calculate the deviation between the actual handling time and the required handling time window for each express shipment at each logistics node; If the actual operation time of each package at each logistics node is less than or equal to the tolerance limit, the time consumption of the package logistics order node is considered normal. If the actual operation time of each package at each logistics node is greater than the tolerance limit, the time consumption of the package logistics order node is considered abnormal. A dynamic threshold is set. If the actual operation time of each express delivery at each logistics node deviates from the time window required for operation by more than the threshold, the express delivery order is judged to be abnormal in timeliness, and the network point to which the express delivery order belongs is marked as the responsible party. The dynamic threshold is determined by grouping the historical time consumption of express logistics order nodes.
[0010] Preferably, based on automatically marking abnormal nodes, multi-dimensional features of abnormal nodes are extracted, and multi-dimensional feature vectors of abnormal nodes are constructed. The multidimensional features include: the deviation between the actual operation time and the required operation time window for each express shipment at each logistics node, the impact range of abnormal nodes, the duration of abnormal nodes, the weight of each logistics node in the entire express shipment chain, customer value and weather factors. Based on the multidimensional feature vector of abnormal nodes, an abnormal node risk assessment index system is established using the analytic hierarchy process. The comprehensive risk assessment of abnormal nodes is used as the target layer, and the deviation between the actual operation time and the required operation time window of each express at each logistics node, the scope of impact of abnormal nodes, the duration of abnormal nodes, the weight of each logistics node in the entire express link, customer value and weather factors are used as the criteria layer, and quantitative parameters are used as the indicator layer. By using expert scoring, pairwise comparison criteria layers are constructed to form a judgment matrix. The eigenvectors of the judgment matrix are calculated, and consistency is verified to obtain the subjective weights of multidimensional features of abnormal nodes. By combining the business rules of the responsible outlets, multi-dimensional feature data of the criteria layer is collected to establish a criteria layer data matrix; Using information entropy theory, the multidimensional feature information entropy in the criterion layer data matrix is calculated to obtain the objective weights of the multidimensional features of abnormal nodes.
[0011] Preferably, the subjective and objective weights of the multidimensional features of the abnormal nodes are integrated to obtain the comprehensive weight of the multidimensional features of the abnormal nodes, and the risk entropy value of each abnormal node is calculated. By using the OrdinalRegression loss method, the risk entropy value of abnormal nodes is mapped to a preset risk level range, thereby realizing the intelligent classification of abnormal nodes and responsible entities. The intelligent risk classification of abnormal nodes and responsible entities includes: low risk, medium risk, high risk, and emergency risk. If the risk entropy value If so, it is judged as low risk; If the risk entropy value If so, it is determined to be of medium risk; If the risk entropy value If so, it is judged as high risk; If the risk entropy value If so, it is determined to be an emergency risk.
[0012] Preferably, an abnormal node is used as the root node, the risk level and the current time window are used as internal nodes, and the optimal reach method is used as the leaf node to construct a decision tree model; If it is a low-risk node, prioritize pushing in-site messages or SMS; if it is a medium-risk node, trigger voice call reminder; if it is a high-risk or emergency risk node, initiate manual customer service intervention and simultaneously push red alert to the terminal of the person in charge of the responsible branch. By combining the customer's preferred time period with the current logistics node status, the order processing flow of the responsible network point is activated simultaneously; For unresolved abnormal nodes, the work order will be automatically escalated to the next higher management unit. By using natural language processing methods, we analyze customers' historical communication records and emotional tendencies to generate personalized reassurance messages and push them to customer service terminals, simultaneously initiating the customer emotional reassurance process. Integrate the automatic triggering of work order escalation with customer emotional reassurance processes to build a dual-path response mechanism for work order escalation and customer emotional reassurance. By using a decision tree model and a dual-path response mechanism of work order escalation and customer emotional reassurance, intelligent monitoring and secure delivery across the entire express logistics chain can be achieved.
[0013] Furthermore, the express delivery and logistics end-to-end intelligent monitoring and outreach system is used to realize intelligent monitoring and outreach methods for the entire express delivery and logistics chain, including: Data acquisition module, data analysis module, data evaluation module, and data feedback module; The data acquisition module is used to collect real-time status data of each logistics node in the entire express delivery chain based on distributed sensors, and to construct a dynamic topology network of express delivery-network point-link. The data analysis module is used to associate and match the dynamic topology network of express delivery-network point-link based on historical express logistics data, calculate the operation time window of each express delivery at each logistics node, compare the actual operation time of each express delivery, dynamically quantify the timeliness deviation of express logistics nodes, and automatically mark abnormal nodes and responsible parties. The data evaluation module and the data analysis module are electrically connected and are used to construct an abnormal node risk assessment index system based on automatically marking abnormal nodes and responsible entities, combined with the business rules of the responsible outlets, to calculate the risk entropy value of each abnormal node and realize the intelligent classification of abnormal nodes and responsible entities. The data feedback module and the data evaluation module are electrically connected. They are used to dynamically select the optimal contact method based on the intelligent level classification of abnormal nodes and responsible entities, and simultaneously activate the order processing flow of the responsible outlet. For unresolved abnormal nodes, the system automatically triggers a dual-path response mechanism of work order escalation and customer emotional reassurance, thereby realizing intelligent monitoring and safe contact throughout the entire express logistics chain.
[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes an intelligent monitoring and outreach system for the entire express logistics chain. Through intelligent monitoring and automated tracking services, it achieves real-time and precise management of the express logistics process, reduces manual intervention, significantly improves the efficiency of handling anomalies, shortens the express delivery cycle, and enhances overall operational efficiency. It also reduces the risk of lost or delayed packages, enhancing the reliability of express logistics services. Personalized outreach strategies and efficient problem-solving mechanisms effectively soothe user emotions, improving user satisfaction and loyalty. Furthermore, it reduces unnecessary resource waste, further lowering operating costs and improving the economic benefits for enterprises. Attached Figure Description
[0015] Figure 1 Flowchart of intelligent monitoring and outreach methods for the entire express logistics chain; Figure 2 Framework diagram of intelligent monitoring and outreach system for the entire express logistics chain. Detailed Implementation
[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.
[0017] Reference Figure 1 As shown, the intelligent monitoring and outreach methods for the entire express logistics chain include: S1. Based on distributed sensors, real-time status data of each logistics node in the entire express delivery chain is collected to construct a dynamic topology network of express delivery-network point-link. Step S1 includes the following: Based on distributed sensors, real-time status data of each logistics node in the entire express delivery chain is collected. The status data of each logistics node in the entire express delivery chain includes: waybill number, geographical location, logistics outlet, operation type, and operator; Using logistics outlets as vertices, we traverse all logistics nodes in the entire express delivery chain and use continuous routing paths in each logistics node in the entire express delivery chain as edges to construct a static express delivery-outlet-link network graph. By utilizing the Apache Kafka distributed stream processing platform, a real-time data channel is established for each logistics node to update the vertex status of the logistics network and the edge status of continuous routing paths in each logistics node of the entire express delivery chain in real time. Using a fixed time window of ten minutes, based on the status data of all logistics nodes in the entire express delivery chain, calculate the continuous routing path edges between the logistics network vertex and each logistics node in the entire express delivery chain, assign weights to each logistics node in the entire express delivery chain, and construct a dynamic topology network of express delivery-network-linkage, as shown in the following formula:
[0018] in, The weights of continuous routing path edges in each logistics node of the entire express delivery chain. For time window identification, For the edges in the dynamic topology network of express delivery - outlet - link, This refers to the edge traffic in the dynamic topology network of express delivery, delivery point, and link within a fixed time window of ten minutes. The average operation time for edges in the dynamic topology network of express delivery-network point-process. The standard time consumption for edges in the dynamic topology network of express delivery-network point-process. This is a penalty factor.
[0019] When using it, refer to the content of step S1 above: Current technologies for end-to-end monitoring and logistics network optimization in express delivery mainly rely on static data collection and batch processing, typically allowing only offline monitoring and analysis of each logistics node. This approach suffers from poor real-time performance and slow response to dynamic changes during the logistics process. It primarily focuses on optimizing individual nodes or links, lacking dynamic monitoring across the entire chain and process, making it impossible to acquire and adjust the operational status of each link in real time, and failing to promptly identify bottlenecks or potential problems in the logistics chain. This step, through real-time data collection based on distributed sensors and the Apache Kafka processing platform, constructs a dynamically updated express delivery-network-link topology network, capturing real-time changes in the status of each logistics node. By setting time windows and assigning weights to each logistics node across the entire express delivery chain, the constructed dynamic topology network can reflect various changes in the logistics process in real time. Through multi-dimensional data analysis such as edge traffic and operation time, it helps identify bottlenecks or delays in the logistics process, achieving more efficient resource allocation and real-time response. This improves logistics efficiency, reduces delays and errors, and provides strong support for end-to-end monitoring and optimization of express delivery.
[0020] S2. Based on historical express logistics data, associate and match the dynamic topology network of express-network points-links, calculate the operation time window of each express at each logistics node, compare the actual operation time of each express, dynamically quantify the timeliness deviation of express logistics nodes, and automatically mark abnormal nodes and responsible parties. Step S2 includes the following: Based on historical express logistics data, data preprocessing is performed to build a historical express logistics database and extract data on successfully completed express logistics orders. For successfully completed express delivery order data, calculate the time between every two consecutive nodes in each express delivery order and generate a historical time table of express delivery order nodes; The historical time consumption table of the express logistics order node includes: route segment, product type, time period type and operation time consumption; Using the profile coefficient analysis method, the average distance between each data point in the historical time consumption table of express logistics order nodes is calculated to obtain the cluster density of the historical time consumption table of express logistics order nodes. Calculate the average distance between each data point in the historical time consumption table of express logistics order nodes and the nearest cluster data point to obtain the inter-cluster separation degree of the historical time consumption table of express logistics order nodes. Based on the intra-cluster tightness and inter-cluster separation of the historical time consumption table of express logistics order nodes, the profile coefficient of each data point in the historical time consumption table of express logistics order nodes is obtained, and the optimal number of clusters k in the historical time consumption table of express logistics order nodes is determined. Based on the historical time consumption table of express logistics order nodes, the optimal number of clusters k is selected as k points as the initial cluster centers; For each data point in the historical timeout table of express logistics order nodes, the Euclidean distance formula is used to calculate the distance from each data point to the initial cluster center of k points; Based on the distance results, the data points are assigned to the nearest cluster center, the mean of all data points in each cluster is recalculated, and the historical time cluster center of the express logistics order node is updated. Repeat the above process until the historical time clustering center of the express logistics order node converges, resulting in k groups of historical time consumption for express logistics order nodes.
[0021] Step S2 also includes the following: Based on the historical time grouping of k express logistics order nodes, calculate the mean, variance and standard deviation of the time for each group; Set excellent timeliness standards and tolerance limits for express logistics order nodes; The excellent timeliness line is at the 50th percentile. The upper limit of tolerance is taken as the mean plus 2 standard deviations; Obtain the operation time window for each express shipment at each logistics node; Based on distributed sensors, real-time status data of each logistics node in the entire express delivery chain is collected, and the dynamic topology network of express delivery-network point-link is associated and matched to obtain the actual operation time of each express delivery at each logistics node. Calculate the deviation between the actual handling time and the required handling time window for each express shipment at each logistics node; If the actual operation time of each package at each logistics node is less than or equal to the tolerance limit, the time consumption of the package logistics order node is considered normal. If the actual operation time of each package at each logistics node is greater than the tolerance limit, the time consumption of the package logistics order node is considered abnormal. A dynamic threshold is set. If the actual operation time of each express delivery at each logistics node deviates from the time window required for operation by more than the threshold, the express delivery order is judged to be abnormal in timeliness, and the network point to which the express delivery order belongs is marked as the responsible party. The dynamic threshold is determined by grouping the historical time consumption of express logistics order nodes.
[0022] When using it, refer to the content of step S2 above: Current domestic and international logistics timeliness monitoring technologies mostly rely on static thresholds and simple statistical analysis, which are difficult to accurately handle timeliness assessments of dynamic links under complex routes. They also suffer from shortcomings such as poor adaptability to network topology changes, high false alarm rates for anomaly marking, and lack of data-driven basis for responsibility identification. This step integrates historical data clustering analysis with dynamic topology network matching to construct a multi-dimensional adaptive timeliness benchmark model. It uses machine learning to optimize clustering and set dynamic thresholds to improve the accuracy and robustness of anomaly identification, achieve precise quantitative positioning of responsible network points, effectively reduce the false judgment rate, and improve the automation and intelligence level of logistics timeliness management.
[0023] S3. Based on the automatic marking of abnormal nodes and responsible entities, and combined with the business rules of responsible outlets, construct an abnormal node risk assessment index system, calculate the risk entropy value of each abnormal node, and realize the intelligent classification of abnormal nodes and responsible entities. Step S3 includes the following: Based on automatic labeling of abnormal nodes, multi-dimensional features of abnormal nodes are extracted and multi-dimensional feature vectors of abnormal nodes are constructed. The multidimensional features include: the deviation between the actual operation time and the required operation time window for each express shipment at each logistics node, the impact range of abnormal nodes, the duration of abnormal nodes, the weight of each logistics node in the entire express shipment chain, customer value and weather factors. Based on the multidimensional feature vector of abnormal nodes, an abnormal node risk assessment index system is established using the analytic hierarchy process. The comprehensive risk assessment of abnormal nodes is used as the target layer, and the deviation between the actual operation time and the required operation time window of each express at each logistics node, the scope of impact of abnormal nodes, the duration of abnormal nodes, the weight of each logistics node in the entire express link, customer value and weather factors are used as the criteria layer, and quantitative parameters are used as the indicator layer. By using expert scoring, pairwise comparison criteria layers are constructed to form a judgment matrix. The eigenvectors of the judgment matrix are calculated, and consistency is verified to obtain the subjective weights of multidimensional features of abnormal nodes. By combining the business rules of the responsible outlets, multi-dimensional feature data of the criteria layer is collected to establish a criteria layer data matrix; Using information entropy theory, the multidimensional feature information entropy in the criterion layer data matrix is calculated to obtain the objective weights of the multidimensional features of abnormal nodes.
[0024] Step S3 also includes the following: By integrating the subjective and objective weights of the multidimensional features of abnormal nodes, the comprehensive weight of the multidimensional features of abnormal nodes is obtained, and the risk entropy value of each abnormal node is calculated. By using the OrdinalRegression loss method, the risk entropy value of abnormal nodes is mapped to a preset risk level range, thereby realizing the intelligent classification of abnormal nodes and responsible entities. The intelligent risk classification of abnormal nodes and responsible entities includes: low risk, medium risk, high risk, and emergency risk. If the risk entropy value If so, it is judged as low risk; If the risk entropy value If so, it is determined to be of medium risk; If the risk entropy value If so, it is judged as high risk; If the risk entropy value If so, it is determined to be an emergency risk.
[0025] When using it, refer to the content of step S3 above: Current risk assessments of abnormal logistics nodes, both domestically and internationally, commonly suffer from biases due to reliance on single subjective or objective weighting methods. They also lack deep integration with the business rules of the responsible parties, leading to a disconnect between risk assessment results and actual management needs. This step integrates the Analytic Hierarchy Process (AHP) (subjective weighting) and information entropy theory (objective weighting) to construct a comprehensive weighting assessment system. Furthermore, it introduces ordered regression to achieve intelligent level classification, improving the accuracy and interpretability of abnormal node risk grading and providing precise risk control basis for the responsible parties.
[0026] S4. Based on the intelligent level classification of abnormal nodes and responsible entities, dynamically select the optimal contact method, simultaneously activate the order processing process of the responsible outlet, and automatically trigger the dual-path response mechanism of work order escalation and customer emotional reassurance for unresolved abnormal nodes, so as to realize intelligent monitoring and safe contact of the entire express logistics chain. Step S4 includes the following: A decision tree model is constructed using anomaly nodes as the root node, risk level and current time window as internal nodes, and optimal reach method as leaf nodes. If it is a low-risk node, prioritize pushing in-site messages or SMS; if it is a medium-risk node, trigger voice call reminder; if it is a high-risk or emergency risk node, initiate manual customer service intervention and simultaneously push red alert to the terminal of the person in charge of the responsible branch. By combining the customer's preferred time period with the current logistics node status, the order processing flow of the responsible network point is activated simultaneously; For unresolved abnormal nodes, the work order will be automatically escalated to the next higher management unit. By using natural language processing methods, we analyze customers' historical communication records and emotional tendencies to generate personalized reassurance messages and push them to customer service terminals, simultaneously initiating the customer emotional reassurance process. Integrate the automatic triggering of work order escalation with customer emotional reassurance processes to build a dual-path response mechanism for work order escalation and customer emotional reassurance. By using a decision tree model and a dual-path response mechanism of work order escalation and customer emotional reassurance, intelligent monitoring and secure delivery across the entire express logistics chain can be achieved.
[0027] When using it, refer to the content of step S4 above: Current domestic and international express logistics monitoring systems generally suffer from problems such as static and simplistic anomaly response methods, lack of dynamic risk adaptation capabilities, insufficient identification of customer emotional needs, and a disconnect between escalation mechanisms and reassurance processes, resulting in low processing efficiency and poor customer experience. This step improves the accuracy, timeliness, and customer satisfaction of anomaly handling by constructing a dynamic decision tree model to achieve risk-level outreach, combined with intelligent order tracking activation, a dual-path response mechanism, and sentiment analysis technology. At the same time, it achieves closed-loop management of full-link intelligent monitoring and secure outreach.
[0028] Reference Figure 2 As shown, the intelligent monitoring and outreach system for the entire express logistics chain includes... Data acquisition module, data analysis module, data evaluation module, and data feedback module; The data acquisition module is used to collect real-time status data of each logistics node in the entire express delivery chain based on distributed sensors, and to construct a dynamic topology network of express delivery-network point-link. The data analysis module is used to associate and match the dynamic topology network of express delivery-network point-link based on historical express logistics data, calculate the operation time window of each express delivery at each logistics node, compare the actual operation time of each express delivery, dynamically quantify the timeliness deviation of express logistics nodes, and automatically mark abnormal nodes and responsible parties. The data evaluation module and the data analysis module are electrically connected and are used to construct an abnormal node risk assessment index system based on automatically marking abnormal nodes and responsible entities, combined with the business rules of the responsible outlets, to calculate the risk entropy value of each abnormal node and realize the intelligent classification of abnormal nodes and responsible entities. The data feedback module and the data evaluation module are electrically connected. They are used to dynamically select the optimal contact method based on the intelligent level classification of abnormal nodes and responsible entities, and simultaneously activate the order processing flow of the responsible outlet. For unresolved abnormal nodes, the system automatically triggers a dual-path response mechanism of work order escalation and customer emotional reassurance, thereby realizing intelligent monitoring and safe contact throughout the entire express logistics chain.
[0029] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.
Claims
1. A method for intelligent monitoring and outreach across the entire express logistics chain, characterized in that: include: S1. Based on distributed sensors, real-time status data of each logistics node in the entire express delivery chain is collected to construct a dynamic topology network of express delivery-network point-link. S2. Based on historical express logistics data, associate and match the dynamic topology network of express-network points-links, calculate the operation time window of each express at each logistics node, compare the actual operation time of each express, dynamically quantify the timeliness deviation of express logistics nodes, and automatically mark abnormal nodes and responsible parties. S3. Based on the automatic marking of abnormal nodes and responsible entities, and combined with the business rules of responsible outlets, construct an abnormal node risk assessment index system, calculate the risk entropy value of each abnormal node, and realize the intelligent classification of abnormal nodes and responsible entities. S4. Based on the intelligent classification of abnormal nodes and responsible entities, dynamically select the optimal contact method, simultaneously activate the order processing flow of the responsible outlet, and automatically trigger a dual-path response mechanism of work order escalation and customer emotional reassurance for unresolved abnormal nodes, so as to realize intelligent monitoring and safe contact of the entire express logistics chain.
2. The intelligent monitoring and outreach method for the entire express logistics chain according to claim 1, characterized in that, S1 includes: Based on distributed sensors, real-time status data of each logistics node in the entire express delivery chain is collected. The status data of each logistics node in the entire express delivery chain includes: waybill number, geographical location, logistics outlet, operation type, and operator; Using logistics outlets as vertices, we traverse all logistics nodes in the entire express delivery chain and use continuous routing paths in each logistics node in the entire express delivery chain as edges to construct a static express delivery-outlet-link network graph. By utilizing the Apache Kafka distributed stream processing platform, a real-time data channel is established for each logistics node to update the vertex status of the logistics network and the edge status of continuous routing paths in each logistics node of the entire express delivery chain in real time. A fixed time window of ten minutes is set. Based on the status data of each logistics node in the entire express delivery chain, the continuous routing path edges between the logistics network vertex and each logistics node in the entire express delivery chain are calculated. Weights are assigned to each logistics node in the entire express delivery chain to construct a dynamic topology network of express delivery-network-link.
3. The intelligent monitoring and outreach method for the entire express logistics chain according to claim 2, characterized in that, The formulas for the weights of each logistics node in the entire express delivery chain include: ； in, The weights of continuous routing path edges in each logistics node of the entire express delivery chain. For time window identification, For the edges in the dynamic topology network of express delivery - outlet - link, This refers to the edge traffic in the dynamic topology network of express delivery, delivery point, and link within a fixed time window of ten minutes. The average operation time for edges in the dynamic topology network of express delivery-network point-process. The standard time consumption for edges in the dynamic topology network of express delivery-network point-process. This is a penalty factor.
4. The intelligent monitoring and outreach method for the entire express logistics chain according to claim 1, characterized in that, S2 includes: Based on historical express logistics data, data preprocessing is performed to build a historical express logistics database and extract data on successfully completed express logistics orders. For successfully completed express delivery order data, calculate the time between every two consecutive nodes in each express delivery order and generate a historical time table of express delivery order nodes; The historical time consumption table of the express logistics order node includes: route segment, product type, time period type and operation time consumption; Using the profile coefficient analysis method, the average distance between each data point in the historical time consumption table of express logistics order nodes is calculated to obtain the cluster density of the historical time consumption table of express logistics order nodes. Calculate the average distance between each data point in the historical time consumption table of express logistics order nodes and the nearest cluster data point to obtain the inter-cluster separation degree of the historical time consumption table of express logistics order nodes. Based on the intra-cluster tightness and inter-cluster separation of the historical time consumption table of express logistics order nodes, the profile coefficient of each data point in the historical time consumption table of express logistics order nodes is obtained, and the optimal number of clusters k in the historical time consumption table of express logistics order nodes is determined. Based on the historical time consumption table of express logistics order nodes, the optimal number of clusters k is selected as k points as the initial cluster centers; For each data point in the historical timeout table of express logistics order nodes, the Euclidean distance formula is used to calculate the distance from each data point to the initial cluster center of k points; Based on the distance results, the data points are assigned to the nearest cluster center, the mean of all data points in each cluster is recalculated, and the historical time cluster center of the express logistics order node is updated. Repeat the above process until the historical time clustering center of the express logistics order node converges, resulting in k groups of historical time consumption for express logistics order nodes.
5. The intelligent monitoring and outreach method for the entire express logistics chain according to claim 4, characterized in that, S2 further includes: Based on the historical time grouping of k express logistics order nodes, calculate the mean, variance and standard deviation of the time for each group; Set excellent timeliness standards and tolerance limits for express logistics order nodes; The excellent timeliness line is at the 50th percentile. The upper limit of tolerance is taken as the mean plus 2 standard deviations; Obtain the operation time window for each express shipment at each logistics node; Based on distributed sensors, real-time status data of each logistics node in the entire express delivery chain is collected, and the dynamic topology network of express delivery-network point-link is associated and matched to obtain the actual operation time of each express delivery at each logistics node. Calculate the deviation between the actual handling time and the required handling time window for each express shipment at each logistics node; If the actual operation time of each package at each logistics node is less than or equal to the tolerance limit, the time consumption of the package logistics order node is considered normal. If the actual operation time of each package at each logistics node is greater than the tolerance limit, the time consumption of the package logistics order node is considered abnormal. A dynamic threshold is set. If the actual operation time of each express delivery at each logistics node deviates from the time window required for operation by more than the threshold, the express delivery order is judged to be abnormal in timeliness, and the network point to which the express delivery order belongs is marked as the responsible party. The dynamic threshold is determined by grouping the historical time consumption of express logistics order nodes.
6. The intelligent monitoring and outreach method for the entire express logistics chain according to claim 5, characterized in that, S3 includes: Based on automatic labeling of abnormal nodes, multi-dimensional features of abnormal nodes are extracted and multi-dimensional feature vectors of abnormal nodes are constructed. The multidimensional features include: the deviation between the actual operation time and the required operation time window for each express shipment at each logistics node, the impact range of abnormal nodes, the duration of abnormal nodes, the weight of each logistics node in the entire express shipment chain, customer value and weather factors. Based on the multidimensional feature vector of abnormal nodes, an abnormal node risk assessment index system is established using the analytic hierarchy process. The comprehensive risk assessment of abnormal nodes is used as the target layer, and the deviation between the actual operation time and the required operation time window of each express at each logistics node, the scope of impact of abnormal nodes, the duration of abnormal nodes, the weight of each logistics node in the entire express link, customer value and weather factors are used as the criteria layer, and quantitative parameters are used as the indicator layer. By using expert scoring, pairwise comparison criteria layers are constructed to form a judgment matrix. The eigenvectors of the judgment matrix are calculated, and consistency is verified to obtain the subjective weights of multidimensional features of abnormal nodes. By combining the business rules of the responsible outlets, multi-dimensional feature data of the criteria layer is collected to establish a criteria layer data matrix; Using information entropy theory, the multidimensional feature information entropy in the criterion layer data matrix is calculated to obtain the objective weights of the multidimensional features of abnormal nodes.
7. The intelligent monitoring and outreach method for the entire express logistics chain according to claim 6, characterized in that, S3 further includes: By integrating the subjective and objective weights of the multidimensional features of abnormal nodes, the comprehensive weight of the multidimensional features of abnormal nodes is obtained, and the risk entropy value of each abnormal node is calculated. By using the OrdinalRegression loss method, the risk entropy value of abnormal nodes is mapped to a preset risk level range, thereby realizing the intelligent classification of abnormal nodes and responsible entities. The intelligent risk classification of abnormal nodes and responsible entities includes: low risk, medium risk, high risk, and emergency risk. If the risk entropy value If so, it is judged as low risk; If the risk entropy value If so, it is determined to be of medium risk; If the risk entropy value If so, it is judged as high risk; If the risk entropy value If so, it is determined to be an emergency risk.
8. The intelligent monitoring and outreach method for the entire express logistics chain according to claim 7, characterized in that, S4 includes: A decision tree model is constructed using anomaly nodes as the root node, risk level and current time window as internal nodes, and optimal reach method as leaf nodes. If it is a low-risk node, prioritize pushing in-site messages or SMS; if it is a medium-risk node, trigger voice call reminder; if it is a high-risk or emergency risk node, initiate manual customer service intervention and simultaneously push red alert to the terminal of the person in charge of the responsible branch. By combining the customer's preferred time period with the current logistics node status, the order processing flow of the responsible network point is activated simultaneously; For unresolved abnormal nodes, the work order will be automatically escalated to the next higher management unit. By using natural language processing methods, we analyze customers' historical communication records and emotional tendencies to generate personalized reassurance messages and push them to customer service terminals, simultaneously initiating the customer emotional reassurance process. Integrate the automatic triggering of work order escalation with customer emotional reassurance processes to build a dual-path response mechanism for work order escalation and customer emotional reassurance. By using a decision tree model and a dual-path response mechanism of work order escalation and customer emotional reassurance, intelligent monitoring and secure delivery across the entire express logistics chain can be achieved.
9. A system for implementing the intelligent monitoring and outreach method for the entire express logistics chain according to any one of claims 1-8, characterized in that, include: Data acquisition module, data analysis module, data evaluation module, and data feedback module; The data acquisition module is used to collect real-time status data of each logistics node in the entire express delivery chain based on distributed sensors, and to construct a dynamic topology network of express delivery-network point-link. The data analysis module is used to associate and match the dynamic topology network of express delivery-network point-link based on historical express logistics data, calculate the operation time window of each express delivery at each logistics node, compare the actual operation time of each express delivery, dynamically quantify the timeliness deviation of express logistics nodes, and automatically mark abnormal nodes and responsible parties. The data evaluation module and the data analysis module are electrically connected and are used to construct an abnormal node risk assessment index system based on automatically marking abnormal nodes and responsible entities, combined with the business rules of the responsible outlets, to calculate the risk entropy value of each abnormal node and realize the intelligent classification of abnormal nodes and responsible entities. The data feedback module and the data evaluation module are electrically connected. They are used to dynamically select the optimal contact method based on the intelligent level classification of abnormal nodes and responsible entities, and simultaneously activate the order processing flow of the responsible outlet. For unresolved abnormal nodes, the system automatically triggers a dual-path response mechanism of work order escalation and customer emotional reassurance, thereby realizing intelligent monitoring and safe contact throughout the entire express logistics chain.