Express behavior risk early warning method based on high-dimensional information

Abstract

The invention provides an express behavior risk early warning method based on high-dimensional information, and belongs to the field of artificial intelligence and express logistics, and the method comprises the following steps: 1, collecting multi-source data and constructing a knowledge graph; and step 2, GNN-based courier behavior feature extraction: comparing historical behavior features to calculate a courier behavior deviation degree so as to identify a potential risk. Step 3, time sequence risk dynamic modeling; step 4, reinforcement learning-driven risk early warning decision making; and step 5, risk assessment and visual output. According to the method, through multi-source data acquisition and knowledge graph construction, dynamic time embedding, reinforcement learning optimization and multi-scale risk assessment, efficient extraction and accurate risk early warning of courier behavior characteristics are realized, and the method has strong innovativeness, computability and data consistency and is suitable for complex risk detection requirements of express logistics scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence and express logistics, and particularly relates to an express behavior risk early warning method based on high-dimensional information. BACKGROUND

[0002] While the express industry is booming, it is facing risks such as package loss, false signing, delivery delay, and privacy leakage. These risks not only affect the normal operation of express services, but also bring many inconveniences and economic losses to users. Traditional risk early warning methods mainly rely on manual monitoring and rule-based matching mechanisms, which have limitations such as low efficiency, poor accuracy, and inability to cope with complex scenarios. With the development of artificial intelligence technology, using high-dimensional information for risk early warning has become a trend, including multi-source data fusion, knowledge graph and graph neural network behavior pattern analysis and risk mining, and reinforcement learning and time series analysis of risk dynamic evolution trend capture. The application of these technologies is expected to break through the limitations of traditional methods and significantly improve the accuracy, timeliness, and comprehensiveness of risk early warning, providing innovative solutions for risk management in the express industry. Therefore, it is of great practical significance and application value to invent an express behavior risk early warning method based on high-dimensional information. The present scheme realizes efficient extraction of express courier behavior characteristics and accurate risk early warning through heterogeneous graph modeling, dynamic time embedding, reinforcement learning optimization, and multi-scale risk assessment, and has strong innovation, computability, and data consistency, and is suitable for complex risk detection needs in the express logistics scene.

[0003] In summary, the present application provides an express behavior risk early warning method based on high-dimensional information, which has important practical significance and application value. SUMMARY

[0004] To achieve the above-mentioned purpose, the technical scheme provided by the present application is as follows: an express behavior risk early warning method based on high-dimensional information, specifically comprising the following steps:

[0005] Step S1: Multi-source data acquisition and knowledge graph construction Integrate express business data, express courier behavior data, external environment data, and social media data through the data acquisition module, and perform data preprocessing, including trajectory data cleaning, text data sentiment analysis, and time series data interpolation completion. Based on the preprocessed data, a dynamic knowledge graph with express couriers as the core is constructed, with nodes including express couriers, orders, customers, delivery areas, and external events, and edges representing interaction relationships. The knowledge graph is represented in the form of triples [subject, relationship, object], and the graph nodes and edges are dynamically updated according to real-time data flow.

[0006] Step S2: GNN-based courier behavior feature extraction Use graph neural network (GNN) to extract the behavior features of the courier from the knowledge graph, including graph embedding initialization, multi-layer message passing to update node embedding, behavior feature extraction, and abnormal behavior detection. By comparing the historical behavior features, the behavior deviation of the courier is calculated to identify potential risks.

[0007] Step S3: Time series risk dynamic modeling Based on the behavior characteristics of the courier and business data, a time series model is constructed to capture the dynamic evolution trend of the risk using a spatio-temporal Transformer (such as the Informer model) to predict future risk trends and calculate risk probabilities.

[0008] Step S4: Reinforcement learning-driven risk warning decision Reinforcement learning (RL) is used to optimize the risk warning strategy, define the state, action, and reward functions of the reinforcement learning environment, use deep Q network (DQN) to learn the optimal strategy, and dynamically adjust the warning threshold and intervention measures.

[0009] Step S5: Risk assessment and visual output Comprehensive assessment of risk probability and warning results, calculation of the comprehensive risk score of the courier and division of risk levels, generation of visual content, including courier behavior trajectory heat map, risk probability time series line chart, and subgraph of high-risk nodes and edges in the knowledge graph.

[0010] Firstly, the multi-source data collection and knowledge graph construction in step S1 include the following steps:

[0011] Step 1.1 Data collection

[0012] Collecting courier business data including order information, delivery trajectory, and receipt status, courier behavior data such as courier trajectory, working hours, order processing volume, and customer evaluation, external environment data such as weather, traffic conditions, holiday information, and regional security level, and public opinion information related to couriers collected from social media platforms.

[0013] Step 1.2 Data preprocessing

[0014] Clean the trajectory data to remove outliers; perform word segmentation and sentiment analysis on text data to extract positive / negative sentiment labels; interpolate and complete time series data to ensure data consistency.

[0015] Step 1.3 Knowledge graph construction and dynamic update

[0016] Based on the preprocessed data, construct a knowledge graph centered on the courier, represent entities and relationships in the form of triples, and dynamically update graph nodes and edges according to real-time data flow to ensure that the graph reflects the latest state.

[0017] Preferably, the courier behavior feature extraction based on GNN in step S2 includes the following steps:

[0018] Step 2.1 Graph Embedding Initialization

[0019] Assign an initial embedding vector to each node in the knowledge graph, and use a pre-trained embedding function to generate the initial embedding.

[0020] Step 2.2 GNN Message Passing

[0021] Use the GraphSAGE model for multi-layer message passing to update node embedding, and update the embedding vector of the current node by combining neighbor node information through an aggregation function (such as mean aggregation).

[0022] Step 2.3 Behavior Feature Extraction and Anomaly Detection

[0023] For the courier node, extract its final embedding vector as a behavior feature, and calculate the courier behavior deviation by comparing historical behavior features to identify potential abnormal behavior.

[0024] Preferably, the time series risk dynamic modeling in step S3 includes the following steps:

[0025] Step 3.1 Time Series Data Construction

[0026] Organize courier behavior features, order processing volume, customer complaint rate and other indicators into time series data according to timestamp.

[0027] Step 3.2 Spatio-temporal Transformer Modeling

[0028] Use the Informer model to model the time series, predict future risk trends, and capture the dynamic evolution of risk.

[0029] Step 3.3 Risk Probability Calculation

[0030] Based on the predicted state, calculate the risk probability of the courier at the future time step to provide a basis for risk warning.

[0031] Preferably, the reinforcement learning driven risk warning decision in step S4 includes the following steps:

[0032] Step 4.1 Reinforcement Learning Definition

[0033] The state of the reinforcement learning environment (current courier state and risk probability), action (warning threshold adjustment or intervention measures), and reward function (based on warning accuracy and intervention cost).

[0034] Step 4.2 DQN Training

[0035] Deep Q Network (DQN) is used to learn the optimal policy, which optimizes the risk warning decision by maximizing the expected return.

[0036] Step 4.3 Dynamic Warning

[0037] According to the action output by DQN, the warning threshold is adjusted in real time, triggering corresponding intervention measures such as warning or suspending delivery.

[0038] Preferably, the risk assessment and visualization output in step S5 include the following steps:

[0039] Step 5.1 Risk Quantification and Classification

[0040] The comprehensive risk score of the courier is calculated, and the risk level is divided according to the score to provide decision support for risk management.

[0041] Step 5.2 Visualization Output

[0042] Generate visualization content including courier behavior trajectory heat map, risk probability time series line chart, and subgraph of high-risk nodes and edges in knowledge graph to assist risk analysis and decision-making.

[0043] Compared with the prior art, the present application has the following advantages and effects:

[0044] 1. The present application integrates express business data, courier behavior data, external environment data and other multi-source information, uses advanced technologies such as knowledge graph and graph neural network, and realizes intelligent analysis and warning of express behavior risk. Compared with traditional methods, the present application can more comprehensively capture risk characteristics, improve the accuracy and timeliness of warning.

[0045] 2. The present application combines time series analysis and reinforcement learning technology to build a dynamic risk warning model, which can adjust the warning strategy in real time and optimize the intervention measures. Compared with the traditional method of fixed threshold, the present application can better adapt to the dynamically changing environment and improve the flexibility and effectiveness of risk management.

[0046] 3. The present application provides rich visualization output, including behavior trajectory heat map, risk probability line chart, etc., and supports interactive query and analysis. Compared with traditional methods, the present application can more intuitively display risk information to assist decision-makers in risk assessment and response. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The framework diagram of the system of the present application. DETAILED DESCRIPTION

[0048] The present application will be described in further detail below in conjunction with specific embodiments and drawings, but the embodiments of the present application are not limited thereto.

[0049] As Figure 1 shown, the embodiment discloses a high-dimensional information-based express behavior risk early warning method, comprising the following steps:

[0050] Step 1: Multi-source data acquisition and knowledge graph construction. Responsible for collecting high-dimensional data related to express business, and constructing a dynamic knowledge graph based on Wikidata knowledge triple structure for subsequent feature extraction and risk modeling.

[0051] (1) Data acquisition

[0052] Express business data, including order information (order ID, sender, recipient, package type), delivery track (timestamp, geographic coordinates), and signed state.

[0053] Express courier behavior data, courier track (GPS record), working hours, order processing volume, customer evaluation, etc.

[0054] External environment data, weather, traffic conditions, holiday information, regional security level, etc.

[0055] Social media data, collect public opinion information related to express couriers (such as complaints, abnormal behavior exposure) from public channels such as X platform.

[0056] (2) Data preprocessing

[0057] Clean the track data and remove abnormal points (such as GPS drift).

[0058] Segment and sentiment analysis of text data (such as customer reviews), extract positive / negative emotion labels.

[0059] Interpolate time series data to ensure data consistency.

[0060] (3) Knowledge graph construction

[0061] Based on the collected data, a knowledge graph with express couriers as the core is constructed, nodes include express couriers, orders, customers, delivery areas, external events, and edges represent interaction relationships (such as "delivery", "evaluation"). The triple form is [subject, relation, object], for example [courier A, delivery, order B].

[0062] The construction formula of the knowledge graph is:

[0063] Symbol explanation:

[0064] : Knowledge graph.

[0065] : Set of nodes, including courier , order , customer , area , event .

[0066] : Set of edges, representing relationships between nodes.

[0067] : Set of relationship types, such as "delivery", "evaluation", "located in", etc.

[0068] Triplet generation algorithm:

[0069] Symbol explanation:

[0070] : Set of triples.

[0071] : Subject node.

[0072] : Relationship type.

[0073] : Object node.

[0074] (4) Dynamic update

[0075] According to real-time data flow, dynamically update the graph nodes and edges to ensure that the graph reflects the latest state.

[0076] Step 2: Multi-GNN-based courier behavior feature extraction. Use graph neural networks to extract courier behavior features from the knowledge graph, and mine their behavior patterns and potential risk associations.

[0077] (1) Graph embedding initialization

[0078] Assign an initial embedding vector to each node in the knowledge graph.

[0079]

[0080] Symbol explanation:

[0081] : Initial embedding vector of node .

[0082] : Pre-trained embedding function (such as TransE).

[0083] (2) GNN message passing

[0084] GraphSAGE model is used for multi-layer message passing to update node embeddings:

[0085]

[0086] Notations:

[0087] : Node The embedding vector at the th layer.

[0088] : Activation function (e.g., ReLU).

[0089] : The learnable weight matrix at the th layer.

[0090] : Aggregation function (e.g., mean aggregation).

[0091] : The neighbor node set of node .

[0092] (3) Behavior feature extraction

[0093] For the courier node, the final embedding vector is extracted as the behavior feature:

[0094]

[0095] Notations:

[0096] : The behavior feature vector of courier .

[0097] : The number of layers of GNN.

[0098] (4) Abnormal behavior detection

[0099] By comparing the historical behavior features, the behavior deviation of the courier is calculated:

[0100]

[0101] Notations:

[0102] : The behavior deviation of courier .

[0103] : The historical average behavior feature of courier .

[0104] : L2 norm.

[0105] Step 3: Time series risk dynamic modeling. Based on the courier behavior characteristics and business data, a time series model is constructed to capture the dynamic evolution trend of risk.

[0106] (1) Time series data construction

[0107] The courier behavior characteristics , order processing volume , customer complaint rate , etc. are organized into time series according to the timestamp:

[0108]

[0109] Symbol explanation:

[0110] : Courier state vector at time .

[0111] : Time series length.

[0112] (2) Spatio-temporal Transformer modeling

[0113] Informer model is used to model the time series and predict the future risk trend:

[0114]

[0115] Symbol explanation:

[0116] : Predicted next time step state vector.

[0117] : Informer model.

[0118] (3) Risk probability calculation

[0119] Based on the predicted state, the risk probability is calculated:

[0120]

[0121] Symbol explanation:

[0122] : Courier risk probability at time step.

[0123] : Learnable parameters.

[0124] Step 4: Reinforcement Learning Driven Risk Warning Decision. Utilize reinforcement learning to optimize risk warning strategies and dynamically adjust warning thresholds and intervention measures.

[0125] (1) Reinforcement Learning Environment Definition

[0126] State: Current Courier State With Risk Probability .

[0127] Action: Warning Threshold Adjustment Or Intervention Measures (Such as Warning, Suspension of Delivery).

[0128] Reward: Based on Warning Accuracy and Intervention Cost:

[0129]

[0130] Symbol Explanation:

[0131] : Reward Value.

[0132] : Warning Accuracy.

[0133] : True Risk Label.

[0134] : Intervention Cost.

[0135] : Weight Coefficient.

[0136] (2) DQN Training

[0137] Use Deep Q Network (DQN) to learn optimal strategy:

[0138]

[0139] Symbol Explanation:

[0140] : State-Action Value Function.

[0141] : Discount Factor.

[0142] : Next State and Action.

[0143] (3) Dynamic Warning

[0144] Adjust the warning threshold according to the action output by DQN:

[0145]

[0146] Symbol explanation:

[0147] : Courier Early warning sign.

[0148] Step 5: Risk assessment and visual output. Comprehensive assessment of risk probability and early warning results, and generation of visual report.

[0149] (1) Risk quantification

[0150] Calculate the comprehensive risk score of the courier:

[0151]

[0152] Symbol explanation:

[0153] : Courier Comprehensive risk score.

[0154] : Weight coefficient, satisfying .

[0155] (2) Risk classification

[0156] According to the score, the risk level is divided:

[0157]

[0158] (3) Visual output, generate the following visual content:

[0159] Courier behavior trajectory heat map.

[0160] Risk probability time series line chart.

[0161] High-risk node and edge subgraph in knowledge graph.

[0162] The above embodiments are the preferred embodiments of the present application, but not limited to the scope of the present application, any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present application, all should be equivalent replacement way, all covered in the protection scope of the present application.

Claims

1. A high-dimensional information-based express behavior risk early warning method, characterized in that, Comprise the following steps: Step S1: Multi-source data collection and knowledge graph construction, integrate express business data, courier behavior data, external environment data and social media data through data collection module, and carry out data preprocessing, including trajectory data cleaning, text data sentiment analysis and time series data interpolation completion; Based on the preprocessed data, a dynamic knowledge graph with courier as the core is constructed, the nodes include courier, order, customer, distribution area and external event, the edge represents the interaction relationship, adopts the form of triple [subject, relation, object] to represent the entity and relationship in the knowledge graph, and dynamically updates the graph nodes and edges according to the real-time data flow; Step S2: Courier behavior feature extraction based on graph neural network GNN, use graph neural network GNN to extract the behavior characteristics of the courier from the knowledge graph, including graph embedding initialization, multi-layer message passing to update node embedding, behavior feature extraction and abnormal behavior detection; By comparing the historical behavior characteristics, the behavior deviation degree of the courier is calculated to identify potential risks; Step S3: Time series risk dynamic modeling, based on the behavior characteristics of the courier and the business data, a time series model is constructed, and a space-time Transformer is used to model to capture the dynamic evolution trend of the risk, predict the future risk trend and calculate the risk probability; Step S4: Risk warning decision driven by reinforcement learning, use reinforcement learning RL to optimize the risk warning strategy, define the state, action and reward function of the reinforcement learning environment, use deep Q network DQN to learn the optimal strategy, and dynamically adjust the warning threshold and intervention measures; Step S5: Risk assessment and visual output, comprehensive evaluation is carried out on the risk probability and warning result, the comprehensive risk score of the courier is calculated and the risk level is divided, the visual content is generated, including the courier behavior trajectory heat map, the risk probability time series line chart and the subgraph of high risk nodes and edges in the knowledge graph.

2. The method of claim 1, wherein, The multi-source data collection and knowledge graph construction in step S1 comprise the following steps: Step 1.1 Data collection Collecting express business data including order information, delivery track, signing status, courier behavior data such as courier track, working hours, order processing volume, customer evaluation, external environment data such as weather, traffic conditions, holiday information, regional security level, and public opinion information related to couriers collected from social media platforms; Step 1.2 Data preprocessing Clean the trajectory data to remove abnormal points; segment and sentiment analyze the text data to extract positive / negative emotion labels; interpolate the time series data to ensure data consistency; Step 1.3 Knowledge graph construction and dynamic update Based on the preprocessed data, a knowledge graph with courier as the core is constructed, the entities and relationships are represented in the form of triple, and the graph nodes and edges are dynamically updated according to the real-time data flow to ensure that the graph reflects the latest state.

3. The method of claim 1, wherein, The courier behavior feature extraction based on GNN in step S2 comprises the following steps: Step 2.1 Graph embedding initialization Assign an initial embedding vector to each node in the knowledge graph, and use a pre-trained embedding function to generate the initial embedding; Step 2.2 GNN message passing Using the GraphSAGE model for multi-layer message passing, updating node embeddings, and combining neighbor node information through an aggregation function to update the embedding vector of the current node; Step 2.3 Behavior feature extraction and anomaly behavior detection For the courier node, extract its final embedding vector as a behavior feature, compare it with historical behavior features, and calculate the courier behavior deviation to identify potential abnormal behavior.

4. The method of claim 1, wherein, The time series risk dynamic modeling in step S3 includes the following steps: Step 3.1 Time series data construction Organize courier behavior features, order processing volume, customer complaint rate, and other indicators by timestamp into time series data; Step 3.2 Spatio-temporal Transformer modeling Use the Informer model to model the time series, predict future risk trends, and capture the dynamic evolution of risk; Step 3.3 Risk probability calculation Based on the predicted state, calculate the risk probability of the courier at the future time step to provide a basis for risk warning.

5. The method of claim 1, wherein, The reinforcement learning driven risk warning decision in step S4 includes the following steps: Step 4.1 Reinforcement learning definition The state, action, and reward function of the reinforcement learning environment, the state includes the current courier state and risk probability, the action includes the warning threshold adjustment or intervention measures, and the reward function is based on the warning accuracy and intervention cost; Step 4.2 DQN training Use the deep Q network DQN to learn the optimal policy and optimize the risk warning decision by maximizing the expected return; Step 4.3 Dynamic warning According to the action output by DQN, adjust the warning threshold in real time and trigger the corresponding intervention measures such as warning or suspension of delivery.

6. The method of claim 1, wherein, The risk assessment and visualization output in step S5 includes the following steps: Step 5.1 Risk quantification and classification Calculate the comprehensive risk score of the courier and divide the risk level according to the score to provide decision support for risk management; Step 5.2 Visualization output Generate visual content including courier behavior trajectory heat map, risk probability time series line chart, and high-risk node and edge subgraph in knowledge graph to assist risk analysis and decision-making.

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