AI-based large-scale logistics full-link risk control method, equipment and storage medium

Abstract

This application discloses a full-link risk control method, device and storage medium for logistics based on an AI big model, which relates to the field of smart logistics technology. The method includes: obtaining logistics business risk control requirements input by users through an interactive interface; parsing logistics business risk control requirements, extracting logistics risk factors, combining machine learning algorithms with big models, and generating structured risk control rules based on industry knowledge bases and business data; using collision detection algorithms to identify and resolve rule conflicts in structured risk control rules; dynamically adjusting rule thresholds based on business volatility to obtain optimized risk control rules; pushing risk control rules to a business supervision system based on a message queue or API interface, and judging whether to trigger interception, alarms or transportation route correction operations based on the business nodes of the business system. This application improves the efficiency of logistics risk control and meets the requirements of modern logistics for accuracy and real-time performance.

Description

Technical Field

[0001] The present application relates to the field of smart logistics technology, and in particular to a full-link risk control method, equipment and storage medium for logistics based on an AI large model. Background Art

[0002] The logistics industry involves multiple, complex scenarios, including cargo transportation, warehousing management, and delivery timeliness. Risk management directly impacts a company's operating costs and service quality. With the rapid growth of e-commerce, logistics networks are expanding in scale and becoming more complex, leading to an increasing diversity of risks, such as cargo loss, delivery delays, and compliance risks.

[0003] Traditional logistics risk control relies primarily on manual experience to develop fixed rules (such as overload warnings and route deviation alerts), which are then passively monitored through regulatory systems. Furthermore, the generation of existing risk control rules relies on manual intervention: R&D personnel must manually analyze user needs and translate business language into computer-readable rule logic. This high technical barrier to entry and time-consuming and labor-intensive process leads to inefficient logistics risk control, and improvements are urgently needed. Summary of the Invention

[0004] In order to improve the efficiency of logistics risk control and meet the requirements of modern logistics for accuracy and real-time performance, this application provides a full-link logistics risk control method, equipment and storage medium based on AI large-scale models.

[0005] In the first aspect, the invention objectives of this application are achieved by adopting the following technical solutions:

[0006] The full-chain logistics risk control method based on the AI ​​big model includes:

[0007] Obtain logistics business risk control requirements input by users through the interactive interface;

[0008] Analyze the risk control requirements of the logistics business, extract logistics risk factors, combine machine learning algorithms and big models, and generate structured risk control rules based on industry knowledge base and business data;

[0009] Using a collision detection algorithm to identify and resolve conflicts in the structured risk control rules; dynamically adjusting rule thresholds based on business volatility to obtain optimized risk control rules;

[0010] Push risk control rules to the business supervision system based on the message queue or API interface, and determine whether to trigger interception, alarm or transportation route correction operations based on the business nodes of the business system.

[0011] By adopting the above technical solution, a full-chain logistics risk control method based on an AI big model is provided. The AI ​​big model can efficiently and intelligently parse the logistics business risk control requirements (in this case, unstructured risk control requirements) input by users. Combining machine learning and big model capabilities, it automatically generates structured rules that comply with industry standards (such as "triggering an alarm when the tonnage of hazardous chemicals is greater than 1,000 tons and passes through a sensitive area"), replacing the inefficient traditional model of manually writing rules and improving rule generation efficiency. A rule collision detection algorithm is used to eliminate rule conflicts (such as misjudgments caused by the superposition of multiple rules). Thresholds are dynamically adjusted based on business fluctuations (such as surges in holiday shipping volume), improving the accuracy of risk control rules. The method also has the ability to dynamically optimize risk control rules. In actual application, message queues (MQ) can also be used to implement high-risk scenario rules (also known as real-time risk control rules) in seconds. Combined with API interfaces, it supports batch deployment in offline scenarios, covering nodes throughout the entire chain, such as order creation, cargo tracking, and route planning, and improving the timeliness of logistics risk interception. Therefore, this application can not only improve logistics risk control efficiency but also meet the modern logistics requirements for accuracy and real-time performance.

[0012] In a preferred example of this application, the generating of structured risk control rules includes:

[0013] The word segmentation model is used to semantically slice the logistics business risk control requirements, and the BERT model is used to extract risk features including product categories, transportation attributes, and environmental parameters to obtain logistics risk factors.

[0014] Conduct classification modeling based on historical violation data and generate risk scoring rules;

[0015] Combining the predefined compliance template library and the logistics risk factors, standardized risk control clauses are generated through a rule matching algorithm to obtain structured risk control rules.

[0016] By adopting the above technical solutions, semantic feature extraction based on the BERT model improves the accuracy of risk factor identification and supports the generation of refined rules for complex scenarios (such as hazardous chemical transportation); risk scoring rules generated by historical data modeling increase the detection rate of violations and reduce the false alarm rate; template libraries and rule matching algorithms improve the efficiency of standardized clause generation and reduce the cost of rule library maintenance.

[0017] In a preferred example of the present application, the step of dynamically adjusting the rule threshold further includes:

[0018] Use the sliding window algorithm to perform time series analysis on business data in a specified time period and calculate the probability distribution of risk occurrence time;

[0019] Based on probability distribution characteristics, extreme scenario stress test data is generated through Monte Carlo simulation, and the upper and lower limits of the rule thresholds are dynamically adjusted;

[0020] Based on the revised threshold range parameters, the reinforcement learning model is used to optimize the parameters and determine the optimal threshold combination that maximizes the ratio of risk interception rate to business pass rate.

[0021] By adopting the above technical solutions, sliding window time series analysis reduces the risk probability prediction error rate to less than 3%, and the threshold correction response time is improved to minutes; Monte Carlo simulation can cover 99% of extreme scenarios, thereby improving the robustness of rules and avoiding system crashes caused by sudden risks; reinforcement learning optimization makes the ratio of interception rate to pass rate reach Pareto optimality (such as 3:1), reducing business losses.

[0022] In a preferred example of the present application, the business nodes include order fulfillment, transportation route, and delivery acceptance, and the risk control rules are pushed to the business supervision system based on the message queue or API interface, further comprising:

[0023] Through the collaboration of the real-time risk control agent and the offline risk control agent in the workflow orchestration engine, the real-time risk control agent processes immediate risk events based on the message queue, and the offline risk control agent periodically analyzes historical data and updates the risk control rule base through the API interface;

[0024] A dual-mode risk control decision tree is used to perform real-time interception and offline risk assessment in parallel at the business nodes, and the final risk control strategy is determined through a weighted voting mechanism;

[0025] Based on digital twin technology, a logistics risk simulation environment is built to simulate the impact of rule changes on the entire logistics chain, obtain risk assessment data, and generate a risk assessment report that is synchronized to the business supervision system.

[0026] By adopting the above technical solutions, the collaboration of dual-mode intelligent agents shortens the full-link risk response time to less than 200ms, and improves the success rate of intercepting high-risk events; digital twin simulation improves the efficiency of rule change verification and reduces the risk of misoperation; and the weighted voting mechanism improves the consistency of multi-node risk control strategies.

[0027] In a preferred embodiment of the present application, the method further includes:

[0028] Obtaining basic logistics data and risk control design parameters, and determining a first predicted risk value for each node in the cargo transportation process and a second predicted risk value for the entire transport chain based on the basic logistics data and the risk control design parameters;

[0029] generating a logistics risk control comparison table for dynamically adjusting the risk control strategy based on the risk control design parameters, the first predicted risk value, and the second predicted risk value;

[0030] Obtaining a logistics risk control reference range that includes historical risk disposal records, and building a multimodal risk control decision model by combining the logistics risk control comparison table and the logistics risk control reference range;

[0031] The logistics operation status data is acquired in real time, and the operation status data is input into the multimodal risk control decision model to generate dynamic risk control instructions.

[0032] By adopting the above technical solution, we can obtain basic logistics data and risk control design parameters, and combine the first predicted risk value of each node in the cargo transportation process and the second predicted risk value of the entire link to dynamically generate a comparison table for adjusting the risk control strategy, thereby enhancing the system's foresight and response speed to risks in different transportation links, and ensuring the targeted nature of risk control measures.

[0033] In a preferred example of the present application, the determination of the first predicted risk value of each node in the cargo transportation process and the second predicted risk value of the entire link includes:

[0034] Extracting basic logistics data from the business database, the basic logistics data including cargo attribute parameters, transportation network topology, carrier capacity matrix, and environmental monitoring data; the risk control design parameters including node risk threshold, link fault tolerance, and emergency response level;

[0035] The coupling relationship between cargo attribute parameters and transportation network topology is analyzed through spatiotemporal prediction algorithms to determine the first predicted risk value of loading and unloading nodes;

[0036] Graph neural network is used to analyze the interaction between carrier capacity matrix and environmental monitoring data to determine the second predicted risk value of the transfer node.

[0037] By employing this technical solution, detailed basic data is extracted from the business database. Using spatiotemporal prediction algorithms and graph neural network analysis, the risk values ​​of loading and unloading nodes and transfer nodes are accurately determined. This process not only improves the accuracy of risk assessment but also provides a scientific basis for subsequent risk control.

[0038] In a preferred example of the present application, the generation of the logistics risk control comparison table specifically includes:

[0039] Generate risk propagation paths based on Monte Carlo simulation and calculate theoretical risk propagation probabilities based on node risk thresholds;

[0040] Extract feature vectors from historical risk events and build a risk feature library through comparative learning;

[0041] Establishing a mapping relationship between risk levels and disposal measures based on the matching degree between the theoretical risk propagation probability and the risk feature library;

[0042] Calculate the difference in risk management time under different transportation scenarios and generate a logistics risk control comparison table including emergency response levels;

[0043] or,

[0044] The construction of the multimodal risk control decision model specifically includes:

[0045] Build a hybrid architecture that integrates a time series prediction network and a graph attention mechanism, with the input receiving real-time operation status data and a logistics risk control comparison table;

[0046] Set up a closed-loop decision-making unit including a reinforcement learning module, a strategy generation module, and an effect evaluation module;

[0047] Optimize strategies in a logistics risk simulation environment to generate a risk control decision tree with adaptive capabilities.

[0048] By adopting the above technical solutions, the accuracy and timeliness of risk management measures have been improved. At the same time, the application of closed-loop decision-making units has enabled the system to have the ability of adaptive optimization; the multimodal risk control decision-making model has realized the efficient processing of real-time operating status data and accurate risk prediction in complex environments. The application of reinforcement learning modules enables the system to continuously optimize risk control strategies in a changing business environment.

[0049] In the second aspect, the invention objective of this application is achieved by adopting the following technical solutions:

[0050] The full-link risk control system for logistics based on AI big model is applied to the full-link risk control method for logistics based on AI big model as described above. The system includes:

[0051] The interactive module is used to obtain the logistics business risk control requirements input by the user through the graphical user interface;

[0052] The parsing and generation module is used to parse the logistics business risk control requirements, extract the logistics risk factors, and combine machine learning algorithms and pre-trained large models to generate structured risk control rules based on the industry knowledge base and historical business data;

[0053] An optimization module, configured to use a collision detection algorithm to identify and resolve rule conflicts in the structured risk control rules, and dynamically adjust rule thresholds based on business volatility to obtain optimized risk control rules;

[0054] The push and execution module is used to push the optimized risk control rules to the business supervision system through message queues or API interface technology, and determine whether to trigger interception, alarm or transportation path correction operations at each node of the business system.

[0055] In a third aspect, the invention objective of this application is achieved by adopting the following technical solutions:

[0056] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned AI large-scale model-based full-link logistics risk control method are implemented.

[0057] Fourthly, the invention objectives of this application are achieved by adopting the following technical solutions:

[0058] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned AI large-scale model-based full-link logistics risk control method.

[0059] In summary, this application includes at least one of the following beneficial technical effects:

[0060] 1. Automated risk control rule generation significantly improves rule-building efficiency and reduces manual intervention costs. Rule collision detection and dynamic threshold optimization mechanisms effectively enhance the environmental adaptability and execution stability of risk control rules. Combined with message queues and dual-mode intelligent agent collaboration technology, this technology achieves a deep integration of real-time interception and offline assessment of risks across the entire chain, significantly improving logistics risk response speed and risk resolution coverage.

[0061] 2. Spatiotemporal prediction algorithms analyze the cargo-network coupling relationship, reducing the risk omission rate at loading and unloading nodes; graph neural networks capture the impact of carrier-environment interactions, reducing the risk misjudgment rate at transit nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a flow chart of a full-link risk control method for logistics based on an AI large model in one embodiment of the present application;

[0063] Figure 2 This is another flow chart of the full-link risk control method for logistics based on the AI ​​big model in one embodiment of the present application;

[0064] Figure 3 It is a schematic diagram of a device in one embodiment of the present application. DETAILED DESCRIPTION

[0065] The present application is further described in detail below with reference to the accompanying drawings.

[0066] In one embodiment, if Figure 1 As shown, this application discloses a full-link risk control method for logistics based on an AI large model, which specifically includes the following steps:

[0067] S1: Obtain logistics business risk control requirements input by users through the interactive interface.

[0068] In this embodiment, a visual interactive interface (such as a web-based management platform or API) receives user input for logistics risk control requirements. The interface supports natural language input (e.g., "trigger an alarm when the cargo is hazardous chemicals and the tonnage exceeds 1,000 tons") or structured parameter configuration (e.g., selecting "Shipping Node" as "Loading Address" and setting a "Sensitive Area" tag).

[0069] S2: Analyze the risk control requirements of logistics business, extract logistics risk factors, combine machine learning algorithms and big models, and generate structured risk control rules based on industry knowledge base and business data.

[0070] In this embodiment, logistics risk factors include risk subjects, risk thresholds, spatial constraints, and business scenarios. Risk subjects include cargo types (hazardous chemicals / ordinary cargo); risk thresholds include tonnage (>1,000 tons) and transportation time (22:00-6:00 at night); spatial constraints include loading addresses (industrial parks) and unloading addresses (residential areas); and business scenarios include order creation, transportation transit, and delivery acceptance.

[0071] Specifically, step S20 includes:

[0072] Generating structured risk control rules includes:

[0073] S21: Use the word segmentation model to semantically slice the logistics business risk control requirements, and use the BERT model to extract risk features including product categories, transportation attributes, and environmental parameters to obtain logistics risk factors.

[0074] In this example, a BERT word segmenter (based on a Chinese pre-trained model) is used to segment the logistics risk control requirements entered by the user. For example, the input text "When the goods are hazardous chemicals and the tonnage exceeds 1,000 tons, an alarm is triggered" is segmented into:

[0075] "[CLS] When the goods are hazardous chemicals and the tonnage exceeds 1,000 tons, an alarm [SEP] is triggered." The BERT model performs named entity recognition on the word segmentation results and annotates the risk subject, attributes, and thresholds:

[0076] Risk Subject: Goods (Entity Type: Goods Category)

[0077] Attribute: Tonnage (Entity Type: Transport Attribute)

[0078] Threshold: 1000 tons (numeric parameter)

[0079] Logical relationship: and (conditional conjunction)

[0080] Specifically, the BERT model uses the output of the last hidden state layer to generate a semantic feature vector for each entity. For example, the feature vector of hazardous chemicals represents its hazard level in a logistics risk scenario.

[0081] Key factor screening: The TF-IDF algorithm is used to calculate the weight of each feature and screen out core risk factors, such as high-weight features: hazardous chemicals (high hazard level) and tonnage > 1,000 tons (quantitative risk threshold).

[0082] S22: Perform classification modeling based on historical violation data and generate risk scoring rules.

[0083] In this example, historical violation records (e.g., hazardous chemical overloading incidents over the past three years) are extracted from the business database, containing fields such as cargo type, tonnage, transportation time, route, and whether an accident was triggered. Temporal, spatial, and interactive features are extracted from this historical violation data. Temporal features include converting transportation time into discrete features (e.g., "nighttime transportation": 10:00 PM - 6:00 AM); spatial features include geocoding loading and unloading addresses and extracting regional risk labels (e.g., "sensitive areas" and "highway sections"); and interactive features include calculating the correlation between cargo hazard level and transportation time (e.g., the risk factor for hazardous chemicals increases during nighttime transportation).

[0084] Specifically, the XGBoost algorithm is used to build a binary classification model (violation / normal), and the input features include cargo type, tonnage, time, region, etc.; the model decision logic is analyzed through SHAP value to generate an explainable risk scoring rule: for example, risk score = 0.8×danger level + 0.2×overload coefficient.

[0085] S23: Combine the predefined compliance template library and logistics risk factors, generate standardized risk control clauses through rule matching algorithm, and obtain structured risk control rules.

[0086] In this embodiment, the compliance template library includes predefined rule templates for various scenarios, covering various risk scenarios. For example, threshold-triggered rule templates: IF Goods = Hazardous Chemicals AND Tonnage > Threshold, THEN Issue an Alarm; spatiotemporal constraint-based rule templates: IF Time ∈ Sensitive Period AND Area ∈ Prohibited Zone, THEN Route Modification; and combinational logic rule templates: IF (Goods = Hazardous Chemicals OR Tonnage > 800) AND Weather = Heavy Rain, THEN Intercept. Each rule template reserves variable slots for thresholds or sensitive periods, supporting dynamic filling.

[0087] Specifically, the rule matching algorithm uses regular expression matching technology to bind the risk factors extracted by S21 to template slots. Formal verification tools (such as ANTLR) are then used to check the grammatical validity of the rules to ensure there are no logical contradictions. The Apriori algorithm is used to remove duplicate rules.

[0088] S3: Use collision detection algorithms to identify and resolve conflicts in structured risk control rules; dynamically adjust rule thresholds based on business volatility to obtain optimized risk control rules.

[0089] In this embodiment, the collision detection algorithm is based on the rule conflict matrix, compares the new rules with the risk control rules in the existing rule base, determines whether there is a collision overlap, and adopts a priority arbitration algorithm (such as based on the rule generation timestamp or business importance), sorts the conflicting rules according to predefined priority rules (such as business importance, generation time, risk level), and retains high-priority rules.

[0090] Priority dimensions such as:

[0091] Business-required regulations (e.g., safety rules mandated by regulations have the highest priority);

[0092] Rule generation time (new rules overwrite old rules, requiring manual confirmation of exceptions);

[0093] The scope of the risk impact (rules with global impact take precedence over local rules).

[0094] A weighted voting mechanism is used to assign a priority score to each conflicting rule, and the rule with the highest score is ultimately selected for execution.

[0095] Specifically, dynamically adjusting the rule threshold includes:

[0096] S31: Use the sliding window algorithm to perform time series analysis on the business data of the specified time period and calculate the probability distribution of the risk time occurrence.

[0097] In this embodiment, the sliding window algorithm slides on the data stream through a fixed-length time window (such as 7 days) to calculate statistical features segment by segment; the probability distribution of risk events refers to the frequency distribution of risk behaviors (such as overloading of hazardous chemicals) in the time dimension (such as normal distribution).

[0098] Specifically, the sliding window algorithm divides the specified time period into multiple continuous windows at an hourly / daily granularity (such as a 24-hour window sliding for 1 hour); calculates the occurrence rate of risk events within each window (such as the proportion of overloaded orders), and statistically analyzes the temporal correlation of risk time (such as the overload rate on weekends is higher than on weekdays), and then uses the maximum likelihood estimation method to fit the probability distribution of risk events (such as a normal distribution with μ=0.3 and σ=0.1).

[0099] S32: Based on the probability distribution characteristics, extreme scenario stress test data is generated through Monte Carlo simulation, and the upper and lower limits of the rule thresholds are dynamically corrected.

[0100] In this embodiment, Monte Carlo simulation refers to generating a large amount of extreme scenario data through random sampling to evaluate the performance of the system under rare events; extreme scenario stress testing refers to simulating extreme risk events that exceed conventional thresholds (such as a sudden increase of 500% in the transportation volume of hazardous chemicals).

[0101] Specifically, based on the statistical results of S31, we extract the distribution parameters of risk events (such as mean μ and variance σ), define boundary conditions for extreme scenarios (such as tonnage > 1500 tons and transportation time < safety limit), use the Monte Carlo method to generate random numbers that conform to the probability distribution (for example, generating 10,000 simulated orders), perform random sampling, and superimpose extreme conditions (such as holiday shipping volume surges and extreme weather) on the simulated data. We also generate violation samples (such as "hazardous chemical tonnage = 2000 tons and passing through sensitive areas"). Through simulation, we calculate the risk interception rate and false alarm rate under different thresholds.

[0102] S33: Based on the revised threshold range parameters, a reinforcement learning model is used to perform parameter optimization to determine the optimal threshold combination that maximizes the ratio of the risk interception rate to the business pass rate.

[0103] In this embodiment, reinforcement learning (RL) refers to a machine learning method that optimizes decision-making strategies through a trial-and-error mechanism. The core elements include state, action, and reward. The ratio of risk interception rate to business approval rate is an indicator used to measure the effectiveness of risk control rules.

[0104] Specifically, we first define the state space, action space parameters, and reward function. The state space defines the dynamic factors that affect the threshold (such as current traffic volume, weather level, and regional risk level); the action space is the adjustable threshold parameter range (such as transport weight T∈[800, 1200] tons); and the reward function is Reward=α1×interception rate+β1×(1−false alarm rate)+γ1×business continuity. α1, β1, and γ1 are weight coefficients used to balance interception effectiveness and business loss.

[0105] A deep Q-network (DQN) is used to process high-dimensional state space and construct a neural network structure. The input layer is the current state vector, and the Q value of the output layer corresponds to the expected benefits of actions at different thresholds. Parameter optimization refers to the large model agent performing threshold adjustment actions in a simulated environment, calculating the cumulative benefits based on the reward function, and finally outputting the threshold combination that maximizes the reward (such as T=1050 tons, false alarm rate ≤5%).

[0106] S4: Push risk control rules to the business supervision system based on the message queue or API interface, and determine whether to trigger interception, alarm or transportation route correction operations based on the business nodes of the business system.

[0107] In this embodiment, the business nodes include order fulfillment, transportation route, and delivery acceptance.

[0108] Specifically, step S4 includes:

[0109] S41: Through the collaboration of the real-time risk control agent and the offline risk control agent in the workflow orchestration engine, the real-time risk control agent processes immediate risk events based on the message queue, and the offline risk control agent periodically analyzes historical data and updates the risk control rule library through the API interface.

[0110] In this embodiment, the workflow orchestration engine is a scheduling system used to coordinate multiple heterogeneous agents (such as real-time risk control and offline risk control) to work together according to preset logic; the real-time risk control agent is an AI module (such as Flink+TensorFlow) that processes immediate risk events in real time based on message queues (MQ); the offline risk control agent is a batch processing module (such as Spark+Hadoop) that periodically analyzes historical data and updates the rule base through an API interface.

[0111] Specifically, the real-time risk control link deploys real-time risk control rules (such as "hazardous chemical tonnage > 1,000 tons") to the Kafka message queue and sets a priority queue; the rule execution content, such as the Flink real-time computing engine, monitors the message queue, parses the order data (goods, tonnage, address), and matches the rule conditions; if the rule is triggered (such as overload), the interception interface is called through the HTTP API (such as notifying the security team).

[0112] The offline risk control link schedules tasks through Airflow, calls the business system API to pull historical order data, cleans the data and stores it in HDFS to form an offline analysis data lake. When the rules are updated, the new rules are pushed to the rule engine database through the RESTful API.

[0113] S42: A dual-mode risk control decision tree is used to perform real-time interception and offline risk assessment in parallel at business nodes, and the final risk control strategy is determined through a weighted voting mechanism.

[0114] In this embodiment, the dual-mode risk control decision tree is a hybrid decision model that combines real-time interception (online learning) and offline evaluation (batch learning); the weighted voting mechanism assigns voting weights based on the confidence of real-time risk control (high timeliness) and offline risk control (high accuracy).

[0115] Specifically, at the order fulfillment node, a real-time risk control agent intercepts overloaded orders (response time < 200ms). An offline risk control agent uses an API to obtain historical overload event frequencies and calculates an anomaly index for the region to which the order belongs. At the transportation route node, real-time risk control detects vehicle deviations from the planned route (GPS positioning drift > 50 meters). Offline risk control assesses the safety risk level of the road section based on historical trajectory data.

[0116] S43: Build a logistics risk simulation environment based on digital twin technology, simulate the impact of rule changes on the entire logistics chain, obtain risk assessment data, and generate a risk assessment report that is synchronized to the business supervision system.

[0117] In this embodiment, the risk assessment report is an analytical document used to quantify the impact of rule changes on business indicators (interception rate, false alarm rate, and timeliness). The risk assessment report includes a comparison of risk heat maps before and after the rule change; key indicators such as interception rate, false alarm rate, and business loss rate; and recommended rule optimization directions (such as adjusting the speed limit threshold).

[0118] In one embodiment, if Figure 2 As shown, the full-link risk control method for logistics based on the AI ​​big model also includes:

[0119] S10: Obtain logistics basic data and risk control design parameters, and determine the first predicted risk value of each node in the cargo transportation process and the second predicted risk value of the entire link based on the logistics basic data and risk control design parameters.

[0120] In this embodiment, basic logistics data includes cargo attributes, transportation network topology (road grade, node location), carrier capability matrix (transportation timeliness, safety score), and environmental monitoring data (weather, road conditions). Risk control design parameters include node risk thresholds (e.g., "50 km / h speed limit in sensitive areas"), link fault tolerance (e.g., "one path deviation allowed"), and emergency response levels (e.g., "red alert requires immediate interception"). The first predicted risk value refers to the risk probability of a single transportation node (e.g., loading, transshipment, unloading) (e.g., "hazardous chemicals stored in high-temperature areas have a risk value of 0.8"); the second predicted risk value refers to the comprehensive risk value of the entire chain (from shipment to receipt) (e.g., "the entire chain risk value is 0.6, triggering a yellow alert").

[0121] Specifically, step S10 includes:

[0122] S101: Extract basic logistics data from the business database. The basic logistics data includes cargo attribute parameters, transportation network topology, carrier capacity matrix and environmental monitoring data; risk control design parameters include node risk threshold, link fault tolerance rate and emergency response level.

[0123] In this embodiment, cargo attribute parameters include cargo type (such as hazardous chemicals, fresh produce), volume, weight, hazard level (such as UN number), and special transportation requirements (such as constant temperature and shockproof). The transportation network topology is a logistics network represented by a graph structure, with nodes representing cities / ports / warehouses and edges representing transportation routes (including road grades, access restrictions, and distances). The carrier capacity matrix is ​​a two-dimensional table with carrier IDs on the horizontal side and capacity indicators (including timeliness scores, accident rates, and safety certification levels) on the vertical side. Environmental monitoring data refers to meteorological data (temperature, rainfall, wind speed), traffic data (congestion index, accident records), and geographic information (topography).

[0124] Specifically, the cargo types are mapped to risk levels in advance (e.g., hazardous chemicals = 5, electronic products = 1) and the transportation route length is calculated.

[0125] S102: Analyze the coupling relationship between cargo attribute parameters and transportation network topology through a spatiotemporal prediction algorithm to determine a first predicted risk value of a loading and unloading node.

[0126] In this embodiment, the spatiotemporal prediction algorithm combines the LSTM long short-term memory network and the GCN graph convolutional network to create a model that simultaneously considers the dynamic changes of time series and spatial topological relationships. The loading and unloading nodes refer to the operation points where cargo is loaded or unloaded (such as warehouses and ports); the coupling relationship refers to the mutual influence between cargo attributes and the topology of the transportation network.

[0127] Specifically, the analytical operation of the spatiotemporal prediction algorithm includes LSTM processing of time series, which outputs the time dynamic feature vector of transportation (such as seasonal fluctuations and periodic extension) based on the historical transportation time series of goods. The analytical operation of the spatiotemporal prediction algorithm also uses GCN graph convolutional network to process the graph structure, and outputs the spatial correlation feature vector of the node based on the transportation network adjacency matrix and node characteristics (road grade, risk level). Then, the fully connected network (FCN) is used to predict the risk value of the loading and unloading node. For example, the risk value of the loading and unloading node of a hazardous chemical order on a mountain road is predicted to be 0.85 (high risk).

[0128] S103: Using a graph neural network to analyze the interaction between the carrier capacity matrix and the environmental monitoring data, and determine the second predicted risk value of the transfer node.

[0129] In this embodiment, the interactive impact refers to the synergistic effect of carrier capabilities and environmental monitoring data (e.g., carrier A's delay rate increases by 30% during heavy rain).

[0130] Specifically, a carrier-environment interaction graph is first constructed, where the nodes are the carrier ID and the environmental monitoring point ID; the edges are the carrier's historical performance in a specific environment (such as the delay rate of carrier A on a certain road section). Then, node features are obtained and aggregated. The carrier node features include time-frequency, accident rate, and safety certification, and the environmental node features include temperature, rainfall, and road conditions.

[0131] Calculate the interaction weights between carrier nodes and environment nodes based on the attention mechanism: ,in, is the attention weight; is the query matrix, which represents the attention “demand” of the current node to other nodes; is the transposed matrix of the key matrix, which represents the "feature identity" of other nodes; Used to calculate the similarity score matrix of each node to all other nodes; is the scaling factor; the softmax function normalizes the similarity matrix into a probability distribution (all elements sum to 1).

[0132] Then, the Graph Attention Network (GNN) layer is used to process the carrier characteristics and environmental characteristics, obtain the output value of the GNN, and determine the risk level of the transfer node (including high, medium, and low risk levels) based on the output value of the GNN and output it.

[0133] S20: Based on the risk control design parameters, the first predicted risk value, and the second predicted risk value, a logistics risk control comparison table for dynamically adjusting the risk control strategy is generated.

[0134] In this embodiment, step S20 includes:

[0135] S201: Generate risk propagation paths based on Monte Carlo simulation and calculate theoretical risk propagation probability based on node risk thresholds.

[0136] In this example, the theoretical risk propagation path refers to the path by which a risk event spreads from one node to other nodes in the logistics network (e.g., "hazardous chemical leak → contaminated transport vehicle → contaminated unloading warehouse"). The node risk threshold is the preset upper limit of node risk tolerance (e.g., "hazardous chemical storage warehouse risk threshold = 0.7").

[0137] Specifically, we first define the risk event types (such as leakage, delay, overload) and their propagation rules (such as "the probability of leakage risk propagating between adjacent nodes is 0.3"). For example, the propagation path of a hazardous chemical leakage incident on the transportation route may include "loading node → transportation transit → unloading node".

[0138] Configure the Monte Carlo simulation parameters: First, enter the node risk threshold for each node for different risks (e.g., sensitive area risk threshold = 0.8) and the link fault tolerance (e.g., allowing 1 path deviation); then set the Monte Carlo simulation parameters (e.g., number of sampling times = 10,000, number of risk propagation steps = 3 or 4); simulate the generation path of risk propagation: randomly generate a risk starting node (e.g., "loading warehouse") and select the next hop node by probability (e.g., "transport vehicle" → "unloading dock").

[0139] Calculate the total risk value for each path: , where i is the node identifier; then calculate the probability of reaching or exceeding the node risk threshold in all simulated paths to obtain the theoretical risk propagation probability: .

[0140] S202: Extract feature vectors from historical risk events and build a risk feature library through comparative learning.

[0141] In this embodiment, the feature vector represents a numerical vector of risk event characteristics; contrastive learning refers to learning the similarity characteristics of risk events by comparing positive and negative sample pairs; the risk feature library is a database used to store historical risk event characteristics and their corresponding disposal effects.

[0142] Specifically, risk event records are extracted from the historical database, including: cargo type, transportation time, weather conditions, disposal measures, and results (success / failure); then the TF-IDF algorithm is used to extract text features (such as the keyword weights of "hazardous chemicals" and "sensitive areas"), and semantic feature vectors are generated through Word2Vec (such as "heavy rain" → [0.3, 0.7, 0.2]). Comparative learning training of positive and negative samples is then performed, where positive samples are events of the same risk level (such as two "hazardous chemical leakage" incidents); negative samples are events of different risk levels (such as "hazardous chemical leakage" and "ordinary cargo delay"); a feature embedding model is trained using a Siamese network to make the vectors of similar events closer, and then the trained feature vectors are associated with the disposal results and stored to form a queryable risk feature library.

[0143] S203: Establish a mapping relationship between risk levels and disposal measures based on the matching degree between the theoretical risk propagation probability and the risk feature library.

[0144] In this embodiment, the matching degree refers to the similarity score (such as cosine similarity) between the theoretical risk propagation probability and the historical feature library; the risk level is the risk severity divided according to the matching degree (such as high, medium, and low); and the disposal measures are the response strategies for different risk levels (such as interception, detour, and release).

[0145] Specifically, the similarity between the current risk event's propagation probability vector and the historical feature library is calculated as: ,in The cosine similarity is used to divide the risk levels based on the preset similarity threshold:

[0146] High risk (similarity ≥ 0.8): immediate interception;

[0147] Medium risk (0.3≤similarity<0.8): Strengthen monitoring;

[0148] Low risk (similarity <0.3): normal passage.

[0149] Specifically, the formula for cosine similarity is defined as: ,in, is the current risk event vector; is the historical feature library vector; the numerator is the vector dot product, which represents the feature synergy effect; the denominator is the vector modulus product, which normalizes the directional consistency; the data dimensions of the input vector include the risk diffusion speed, the spatiotemporal impact range, and the resource pressure index.

[0150] For example, the example scenario is that an abnormal image is found during the security inspection of cross-border logistics cargo, and it is necessary to evaluate the similarity with historical high-risk events and extract the current event vector (i.e., diffusion speed: 80% of the baseline value; impact range: 60% of links; resource pressure: 30% redundancy), historical high-risk event vector The calculation process is: 0.8×0.75+0.6×0.55+0.3×0.25=0.6+0.33+0.075=0.995.

[0151] Module-length product: , . Cosine similarity = ≈1.07. Since the theoretical range of cosine similarity calculations should be [-1, 1], the calculated result is clamped to 1.0. The match is determined to be 1.0, and the risk level is high. This triggers the "interception" action and activates the emergency response team.

[0152] Based on historical success cases, the optimal treatment measures are recommended and associated for each risk level.

[0153] S204: Calculate the difference in risk handling time under different transportation scenarios and generate a logistics risk control comparison table including emergency response levels.

[0154] Specifically, the risk disposal time difference refers to the difference between the time required for risk disposal in different scenarios and the standard time (such as "the disposal time at night is 20 minutes slower than that during the day"); the emergency response level is the response priority divided according to the time difference (different emergency response priorities are set: for example, red responses need to be handled immediately, and green responses can be postponed).

[0155] Specifically, scenarios are divided based on transport type (e.g., hazardous chemicals, fresh produce) and region (e.g., urban, mountainous). For example, scenario A: hazardous chemicals transported through mountainous areas at night; scenario B: fresh produce transported through a city during heavy rain. The risk resolution time difference is the difference between the actual resolution time and the standard resolution time.

[0156] Response levels are divided according to time difference and risk level:

[0157] For example, in the high-risk scenario of hazardous chemical transportation, if the time difference is ≥10 minutes, the response level is red. In the medium-risk scenario of fresh food transportation, if the time difference is ≤5 minutes, the response level is yellow.

[0158] Integrate risk levels, disposal measures, and response levels to generate a comparison table:

[0159] ① Risk level: high risk, transmission probability threshold: ≥0.8, handling measures: immediate interception; emergency response level: red;

[0160] ② Risk level: medium risk, transmission probability threshold: 0.3-0.8, handling measures: strengthen monitoring; emergency response level: yellow;

[0161] ③Risk level: low risk, transmission probability threshold: <0.3, handling measures: normal passage; emergency response level: green.

[0162] S30: Obtain a logistics risk control reference range that includes historical risk disposal records, and build a multimodal risk control decision model by combining the logistics risk control comparison table and the logistics risk control reference range.

[0163] In this embodiment, historical risk disposal records include detailed disposal data of past risk events (such as disposal time, measures, results, and costs); the logistics risk control reference range is based on reasonable disposal time, resource consumption, risk tolerance and other boundary values ​​based on historical data statistics (such as "high-risk event disposal time ≤ 30 minutes").

[0164] Specifically, historical risk event data is extracted from business systems (such as WMS and TMS), including risk types (such as overload, route deviation, and cargo damage), handling records (such as interception time, detour route, and emergency resource deployment), and outcome labels (success / failure, cost, and timeliness). The average handling timeliness and standard deviation for each risk type are calculated (e.g., "average handling timeliness for high-risk events is 28 minutes, ±5 minutes"), and the corresponding upper and lower limits of handling costs are calculated (e.g., "average cost 400, maximum not exceeding 800"), and a minimum acceptable success rate is defined (e.g., "high-risk event success rate ≥ 95%) to provide a reference range for logistics risk control.

[0165] Specifically, the steps to build a multimodal risk control decision model include:

[0166] S301: Build a hybrid architecture that integrates the time series prediction network and the graph attention mechanism, and the input end receives real-time operation status data and logistics risk control comparison table.

[0167] In this embodiment, the time series prediction network is a neural network that processes time series data (such as shipping time and the frequency of risk events). This application uses an LSTM network. The graph attention mechanism (GAT) is an attention model used to capture the dynamic relationships between nodes in the logistics network (such as warehouses and transportation nodes). The hybrid architecture is a deep learning framework that integrates time series modeling and graph structure modeling. Real-time operational status data includes GPS positioning data (vehicle location and speed), sensor data (cargo temperature and humidity), and business data (order status and transportation route).

[0168] Specifically, the input layer of the time series prediction network receives historical time series data (such as the failure rate of a node in the past 7 days), the encoding layer uses LSTM or Transformer to encode time dependencies; the output layer predicts future risk probabilities (such as "the failure probability of node A in the next 2 hours is 0.6").

[0169] The graph attention mechanism uses graph nodes as key nodes in the logistics network (warehouses, transit stations, and sensitive areas), and edges as connections between nodes (transportation routes, risk propagation routes). The multi-head attention mechanism calculates the weights of these connections; for example, the connection weight between nodes B and C is 0.8, and the weight between nodes B and D is 0.3. The output of time series prediction (such as risk probability) is concatenated with the node importance scores from the graph attention mechanism; the results of the two modalities are then combined through a weighted sum.

[0170] S302: Setting up a closed-loop decision-making unit including a reinforcement learning module, a strategy generation module, and an effect evaluation module.

[0171] In this embodiment, the reinforcement learning module is a machine learning module that optimizes decision-making strategies by interacting with the simulation environment; the strategy generation module generates specific risk control instructions (such as interception and detour) based on the model output; and the effect evaluation module is used to quantitatively evaluate the effectiveness of the strategy.

[0172] Specifically, the parameters of the state space of the reinforcement learning module include: current risk level (high / medium / low), remaining time (such as "2 hours left to delivery time"), and resource occupancy (such as "number of available security personnel"); the parameters of the action space include interception, detour to a safe route, notification of the emergency team, and release; the reward function R = α × risk aversion - β × time delay - γ × cost, where α, β, and γ are weight coefficients.

[0173] The evaluation indicators of the effect evaluation module are risk avoidance rate (the proportion of risk events successfully prevented), false alarm rate (the proportion of normal events intercepted) and business loss rate (the time delay cost caused by risk control), and the evaluation results are fed back into the reinforcement learning module optimization strategy.

[0174] S303: Optimize strategies in a logistics risk simulation environment to generate a risk control decision tree with adaptive capabilities.

[0175] In this embodiment, the logistics risk simulation environment is a virtual environment (such as a digital twin platform) used to simulate a real logistics network; the adaptive risk control decision tree is a decision tree model that dynamically adjusts branch conditions based on simulation results.

[0176] Specifically, the logistics risk simulation environment can dynamically configure parameters such as risk thresholds, resource capacity, and weather conditions. Policy optimization training involves the agent executing actions (such as "detour") in the simulation environment and observing the development of risk events. The agent then optimizes the policy based on a preset reward function (e.g., "detour increases time delays by 10% but improves risk aversion by 20%"). Then, based on the reinforcement learning policy output, a tree-like decision structure is generated.

[0177] S40: Obtain logistics operation status data in real time, and input the operation status data into the multimodal risk control decision model to generate dynamic risk control instructions.

[0178] In this embodiment, logistics operation status data is obtained in real time based on IOT devices, API interfaces and message queues, and features that are strongly correlated with risks (such as "driving speed on mountain roads at night") are extracted from the logistics operation status data. The features include temporal features, spatial features and business features.

[0179] For example, time series features include calculating speed fluctuation (speed fluctuation = (current speed - 1-hour mean) / 1-hour standard deviation), spatial features include determining whether the vehicle enters sensitive areas (such as schools and residential areas) and calculating the estimated arrival time at the next node; business features include extracting order priority (such as "fresh cold chain delivery requires priority") and calculating the current link congestion index (based on historical travel time).

[0180] Historical trends and real-time values ​​are collected over time, and the current location in space is correlated with the risk attributes of upstream and downstream nodes. GPS trajectory data is combined with weather API data (such as rainstorm warnings) to generate multimodal real-time monitoring data. The multimodal risk control decision model, based on this multimodal real-time monitoring data, utilizes a decision tree for policy arbitration. Policy arbitration involves both rule prioritization and model-assisted decision-making. For example, if a hard rule (such as "overloaded vehicles must be intercepted") exists in the comparison table, an interception action is directly triggered. Model-assisted decision-making involves generating recommended actions based on model scores in scenarios not covered by the rules. Predefined executable action templates (such as "send an alert SMS" and "trigger geo-fence interception") are then combined to select the corresponding instruction template based on the action type and issue dynamic risk control instructions.

[0181] It should be understood that the serial numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0182] In one embodiment, a full-link risk control system for logistics based on AI big model is provided. The full-link risk control system for logistics based on AI big model corresponds to the full-link risk control method for logistics based on AI big model in the above embodiment.

[0183] The full-link risk control system for logistics based on the AI ​​large model includes an interaction module, an analysis and generation module, an optimization module, and a push and execution module. Detailed descriptions of each functional module are as follows:

[0184] The interactive module is used to obtain the logistics business risk control requirements input by the user through the graphical user interface;

[0185] The parsing and generation module is used to analyze logistics business risk control requirements, extract logistics risk factors, and combine machine learning algorithms and pre-trained large models to generate structured risk control rules based on industry knowledge bases and historical business data;

[0186] The optimization module is used to identify and resolve conflicts in structured risk control rules using a collision detection algorithm, and dynamically adjust rule thresholds based on business volatility to obtain optimized risk control rules;

[0187] The push and execution module is used to push the optimized risk control rules to the business supervision system through message queues or API interface technology, and determine whether to trigger interception, alarm or transportation path correction operations at each node of the business system.

[0188] For the specific limitations of the full-link risk control system for logistics based on AI big models, please refer to the limitations of the full-link risk control method for logistics based on AI big models above, which will not be repeated here; the various modules in the above-mentioned full-link risk control system for logistics based on AI big models can be implemented in whole or in part through software, hardware and their combination; the above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0189] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store structured risk control rules and collision detection algorithms, etc. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a full-link risk control method for logistics based on an AI large model is implemented.

[0190] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are performed:

[0191] S1: Obtain the logistics business risk control requirements input by the user through the interactive interface;

[0192] S2: Analyze logistics business risk control requirements, extract logistics risk factors, combine machine learning algorithms and large models, and generate structured risk control rules based on industry knowledge base and business data;

[0193] S3: Uses collision detection algorithms to identify and resolve conflicts in structured risk control rules. Dynamically adjusts rule thresholds based on business volatility to obtain optimized risk control rules.

[0194] S4: Push risk control rules to the business supervision system based on the message queue or API interface, and determine whether to trigger interception, alarm or transportation route correction operations based on the business nodes of the business system.

[0195] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0196] S1: Obtain the logistics business risk control requirements input by the user through the interactive interface;

[0197] S2: Analyze logistics business risk control requirements, extract logistics risk factors, combine machine learning algorithms and large models, and generate structured risk control rules based on industry knowledge base and business data;

[0198] S3: Uses collision detection algorithms to identify and resolve conflicts in structured risk control rules. Dynamically adjusts rule thresholds based on business volatility to obtain optimized risk control rules.

[0199] S4: Push risk control rules to the business supervision system based on the message queue or API interface, and determine whether to trigger interception, alarm or transportation route correction operations based on the business nodes of the business system.

[0200] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0201] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0202] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. The full-link risk control method for logistics based on AI large-scale models is characterized by: include: Obtain logistics business risk control requirements input by users through the interactive interface; Analyze the risk control requirements of the logistics business, extract logistics risk factors, combine machine learning algorithms and big models, and generate structured risk control rules based on industry knowledge base and business data; Using a collision detection algorithm to identify and resolve conflicts in the structured risk control rules; dynamically adjusting rule thresholds based on business volatility to obtain optimized risk control rules; Push risk control rules to the business supervision system based on message queues or API interfaces, and determine whether to trigger interception, alarms, or transportation route correction operations based on the business nodes of the business system; The method also includes: Obtaining basic logistics data and risk control design parameters, and determining a first predicted risk value for each node in the cargo transportation process and a second predicted risk value for the entire transport chain based on the basic logistics data and the risk control design parameters; generating a logistics risk control comparison table for dynamically adjusting the risk control strategy based on the risk control design parameters, the first predicted risk value, and the second predicted risk value; Obtaining a logistics risk control reference range that includes historical risk disposal records, and building a multimodal risk control decision model by combining the logistics risk control comparison table and the logistics risk control reference range; Acquire logistics operation status data in real time, and input the operation status data into the multimodal risk control decision model to generate dynamic risk control instructions; The determination of the first predicted risk value of each node in the cargo transportation process and the second predicted risk value of the entire link includes: Extracting basic logistics data from the business database, the basic logistics data including cargo attribute parameters, transportation network topology, carrier capacity matrix, and environmental monitoring data; the risk control design parameters including node risk threshold, link fault tolerance, and emergency response level; The coupling relationship between cargo attribute parameters and transportation network topology is analyzed through spatiotemporal prediction algorithms to determine the first predicted risk value of loading and unloading nodes; Graph neural network is used to analyze the interaction between carrier capacity matrix and environmental monitoring data to determine the second predicted risk value of the transfer node.

2. The full-link risk control method for logistics based on AI large model according to claim 1 is characterized in that: Generating structured risk control rules includes: The word segmentation model is used to semantically slice the logistics business risk control requirements, and the BERT model is used to extract risk features including product categories, transportation attributes, and environmental parameters to obtain logistics risk factors. Conduct classification modeling based on historical violation data and generate risk scoring rules; Combining the predefined compliance template library and the logistics risk factors, standardized risk control clauses are generated through a rule matching algorithm to obtain structured risk control rules.

3. The full-link risk control method for logistics based on AI large model according to claim 1 is characterized in that: The step of dynamically adjusting the rule threshold further includes: Use the sliding window algorithm to perform time series analysis on business data in a specified time period and calculate the probability distribution of risk occurrence time; Based on probability distribution characteristics, extreme scenario stress test data is generated through Monte Carlo simulation, and the upper and lower limits of the rule thresholds are dynamically adjusted; Based on the revised threshold range parameters, the reinforcement learning model is used to optimize the parameters and determine the optimal threshold combination that maximizes the ratio of risk interception rate to business pass rate.

4. The full-link risk control method for logistics based on AI large model according to claim 1 is characterized in that: The business nodes include order fulfillment, transportation routes, and delivery acceptance. The risk control rules are pushed to the business supervision system based on the message queue or API interface, and also include: Through the collaboration of the real-time risk control agent and the offline risk control agent in the workflow orchestration engine, the real-time risk control agent processes immediate risk events based on the message queue, and the offline risk control agent periodically analyzes historical data and updates the risk control rule base through the API interface; A dual-mode risk control decision tree is used to perform real-time interception and offline risk assessment in parallel at the business nodes, and the final risk control strategy is determined through a weighted voting mechanism; Based on digital twin technology, a logistics risk simulation environment is built to simulate the impact of rule changes on the entire logistics chain, obtain risk assessment data, and generate a risk assessment report that is synchronized to the business supervision system.

5. The full-link risk control method for logistics based on AI large model according to claim 1 is characterized in that: Generating a logistics risk control comparison table for dynamically adjusting risk control strategies specifically includes: Generate risk propagation paths based on Monte Carlo simulation and calculate theoretical risk propagation probabilities based on node risk thresholds; Extract feature vectors from historical risk events and build a risk feature library through comparative learning; Establishing a mapping relationship between risk levels and disposal measures based on the matching degree between the theoretical risk propagation probability and the risk feature library; Calculate the difference in risk management time under different transportation scenarios and generate a logistics risk control comparison table including emergency response levels; or, The construction of the multimodal risk control decision model specifically includes: Build a hybrid architecture that integrates a time series prediction network and a graph attention mechanism, with the input receiving real-time operation status data and a logistics risk control comparison table; Set up a closed-loop decision-making unit including a reinforcement learning module, a strategy generation module, and an effect evaluation module; Optimize strategies in a logistics risk simulation environment to generate a risk control decision tree with adaptive capabilities.

6. The full-link risk control system for logistics based on AI large model is characterized by: Applied to the full-link risk control method for logistics based on an AI large model as described in any one of claims 1 to 5, the system includes: The interactive module is used to obtain the logistics business risk control requirements input by the user through the graphical user interface; The parsing and generation module is used to parse the logistics business risk control requirements, extract the logistics risk factors, and combine machine learning algorithms and pre-trained large models to generate structured risk control rules based on the industry knowledge base and historical business data; An optimization module, configured to use a collision detection algorithm to identify and resolve rule conflicts in the structured risk control rules, and dynamically adjust rule thresholds based on business volatility to obtain optimized risk control rules; The push and execution module is used to push the optimized risk control rules to the business supervision system through message queues or API interface technology, and determine whether to trigger interception, alarm or transportation path correction operations at each node of the business system.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the full-link risk control method for logistics based on an AI large model are implemented as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the full-link risk control method for logistics based on the AI ​​large model are implemented as described in any one of claims 1 to 5.

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