Intelligent early warning and rule engine cooperation method and system based on language model for logistics

Abstract

The application relates to the technical field of logistics early warning, and discloses a logistics intelligent early warning and rule engine cooperative method and system based on a language model, which comprises the following steps: acquiring logistics monitoring demand data of a business user, and generating executable monitoring rules based on a logistics language model. Logistics operation data is collected, sequentially subjected to cleaning, space-time alignment, feature extraction and feature fusion processing, and then input into a pre-trained risk prediction model to generate a multidimensional risk feature vector, and the logistics risk corresponding prediction probability is output. Early warning judgment is completed by using an early warning threshold and monitoring rules, and effective early warning events are obtained through multi-level false alarm suppression, which are pushed to a rule engine to match preset response rules, and hierarchical notification, work order creation and business process arrangement are automatically executed. The method solves the short boards of a logistics early warning system in terms of rule configuration efficiency, multi-source data fusion, dynamic risk judgment and automatic disposal, and adapts to the operation management needs of modern logistics fine and intelligent and fast response.

Description

Technical Field

[0001] This invention relates to the field of logistics early warning technology, specifically to a collaborative method and system for intelligent logistics early warning and rule engine based on language models. Background Technology

[0002] With the deepening of global supply chain collaboration and the continuous upgrading of the domestic consumer market, modern logistics operations have formed a full-chain network covering trunk transportation, warehousing and sorting, cross-border customs clearance, and last-mile delivery. The characteristics of numerous nodes, long links, and dispersed participants are becoming increasingly prominent. During operation, various uncertainties such as weather fluctuations, traffic congestion, port scheduling changes, equipment failures, and document anomalies can cause chain disruptions at any time, directly affecting delivery timeliness and service quality. Against this backdrop, proactive risk warning and efficient anomaly handling capabilities have become core tools for logistics companies to ensure operational stability and improve customer experience, directly impacting their operating costs and market competitiveness.

[0003] The logistics industry has already established a certain foundation for digital transformation. Various business systems, such as transportation management, warehousing management, and customs clearance management, have achieved large-scale application, accumulating massive amounts of operational data on order status, node flow, and resource scheduling, providing basic data support for risk monitoring and early warning. Most mainstream early warning solutions in the industry are built on rule modules built into business systems. By pre-setting judgment criteria, alarms are triggered for abnormal states exceeding thresholds, which to some extent replaces manual inspections and improves the efficiency of anomaly detection. However, in actual implementation, these solutions generally suffer from high technical barriers and long response cycles in the rule configuration stage. Adding and adjusting early warning rules heavily relies on technical personnel writing database query statements or completing code-level logic development; business operations personnel without a technical background cannot complete the configuration independently. When business scenarios change, such as adding monitoring requirements for specific routes, adjusting the stop judgment criteria in the customs clearance process, or responding to sudden business fluctuations, monitoring requirements need to go through a complete process of requirement submission, technical development, functional testing, and deployment, with a response cycle typically several working days. This is difficult to match the characteristics of logistics business—numerous sudden factors and flexible and ever-changing monitoring requirements—and many refined control demands cannot be promptly translated into implemented monitoring capabilities.

[0004] At the data support level, most current early warning solutions rely solely on time-series status data from a single business system to determine rules. For example, they trigger alarms based solely on whether the node dwell time or total transportation time exceeds preset standards, failing to effectively integrate multi-source heterogeneous information such as vehicle location trajectories, environmental sensor data, meteorological and traffic information, port operation dynamics, and customer feedback text. Single-dimensional data cannot comprehensively reflect the true risk status of the logistics chain. For potential risks caused by external factors such as severe weather, regional port congestion, and capacity fluctuations, the system cannot identify signals in advance and can only trigger alarms after the anomaly actually occurs. The lead time for early warning is severely insufficient, making it difficult for operators to activate emergency plans before risks escalate. Regarding risk assessment mechanisms, these solutions generally adopt fixed threshold judgment logic, unable to dynamically adjust judgment standards based on contextual information such as seasonal cycles, holiday effects, regional network operation status, and cargo attributes. During peak business periods and holidays, when network-wide timeliness generally fluctuates, fixed thresholds trigger a large number of alarms with no practical value, resulting in alarm overload. Furthermore, for high-value goods and cold chain goods requiring strict control, fixed thresholds cannot achieve more refined risk management. A persistently high percentage of invalid alarms gradually reduces the importance that operations personnel place on early warning information, ultimately leading to a significant decline in the actual control effectiveness of the early warning system. At the anomaly handling and system collaboration level, the early warning module and the business execution module are typically independent of each other. After an alarm signal is issued, operations personnel must manually determine the anomaly level, manually create work orders, notify relevant positions, and dispatch emergency resources. The handling process is highly dependent on personal experience, lacking standardized handling paths and automated execution capabilities, with overall response times generally measured in hours. Furthermore, the results of anomaly handling cannot be automatically fed back into the early warning system. The system cannot optimize the early warning model and rule parameters based on actual handling effects, making it difficult to form a continuously iterative closed-loop management system. Consequently, the system's operational effectiveness gradually diminishes as business scenarios change.

[0005] In summary, the relevant logistics early warning and rule enforcement solutions have significant shortcomings in several aspects, including rule configuration efficiency, multi-source data fusion, dynamic risk assessment, automated handling, and closed-loop optimization, making it difficult to meet the refined, intelligent, and rapid response operation and management needs of modern logistics. Summary of the Invention

[0006] In view of this, the present invention provides a collaborative method and system for intelligent logistics early warning and rule engine based on language model, in order to solve the problem that current logistics early warning and rule execution schemes have obvious shortcomings in many aspects such as rule configuration efficiency, multi-source data fusion, dynamic risk assessment, automated handling and closed-loop optimization, and are unable to meet the needs of modern logistics for refined, intelligent and rapid operation management.

[0007] Firstly, this invention provides a collaborative method for intelligent logistics early warning and rule engine based on a language model. The method includes: acquiring logistics monitoring requirement data input by business users; using a logistics language model to perform intent recognition and key element extraction on the logistics monitoring requirement data to generate executable monitoring rules; collecting logistics operation data; sequentially cleaning, spatiotemporally aligning, extracting features, and fusing features to generate a multi-dimensional risk feature vector; inputting the multi-dimensional risk feature vector into a pre-trained risk prediction model to output the risk prediction probability corresponding to each logistics risk type; calculating an early warning threshold; determining an early warning based on the early warning threshold and executable monitoring rules to determine the risk prediction probability corresponding to each logistics risk type, generating a first early warning signal, wherein the early warning threshold is dynamically adjusted based on real-time context; performing multi-level false alarm suppression processing on the first early warning signal to obtain a second early warning signal and its corresponding early warning event; pushing the early warning event to the rule engine to match the corresponding response rules, wherein the response rules are pre-placed in an abnormal scenario knowledge base; and using the rule engine based on the corresponding response rules to execute hierarchical notifications, work order creation, and business process orchestration.

[0008] This embodiment provides a collaborative method for intelligent logistics early warning and rule engine based on a language model. First, by receiving logistics monitoring request data input by business users, the method uses a logistics language model to identify the intent of the request and extract key elements, generating structured, executable monitoring rules. The logistics language model is trained on a corpus of logistics domains to enhance semantic understanding, accurately identifying monitoring objects, filtering conditions, and execution actions in business statements. This directly converts business requirements in natural language form into system-recognizable rule logic, bridging the conversion path from business requirements to system implementation, eliminating language barriers between business statements and technical rules, and ensuring that the rule generation process adapts to the expression habits of business personnel, while preserving complete semantic mapping relationships throughout the rule generation process. Second, by collecting various types of logistics operation data, the method sequentially performs data cleaning, spatiotemporal alignment, feature extraction, and feature fusion processing to generate a unified format multidimensional risk feature vector. Adaptive cleaning methods are employed for logistics data from different sources and with varying structures to remove outliers and fill in missing information. Spatiotemporal alignment of multi-source data is achieved based on waybill identifiers, integrating scattered heterogeneous data into feature sets corresponding to the same logistics entity. A multimodal fusion strategy allocates weights to different features based on an attention mechanism, integrating the risk representation capabilities of different data types and outputting a standardized feature vector format. This provides a standardized and complete data input foundation for subsequent risk prediction. Then, by inputting multidimensional risk feature vectors into a pre-trained risk prediction model, the model outputs the risk prediction probability corresponding to each logistics risk type. The risk prediction model is built on a multi-task learning framework. A bottom-level shared feature extraction network extracts and reuses basic features, reducing the resource overhead of redundant computation. A higher-order cross-network mines the complex risk correlations between different features, capturing combined risk scenarios that cannot be covered by a single feature. Multiple parallel prediction branches correspond to different types of logistics risks, each branch employing computational logic adapted to its corresponding risk characteristics, simultaneously covering the prediction needs of multiple risk types and improving the comprehensiveness and rationality of the risk prediction results. Subsequently, by calculating the warning thresholds corresponding to each type of logistics risk, and combining this with executable monitoring rules to filter and compare the predicted probabilities of risks, a first warning signal is generated. The warning thresholds are dynamically adjusted based on the real-time context. The threshold calculation is based on the statistical distribution of historical logistics data to set a benchmark standard, and dynamically adjusted by combining multi-dimensional influencing factors of real-time operating scenarios. This ensures that the risk judgment standard adapts to the operating status of different time periods and regions. Combined with executable monitoring rules, the target monitoring scope is filtered, so that the warning judgment simultaneously meets the dual requirements of business-defined monitoring scope and dynamic risk standards. The warning judgment logic integrates business rules and adaptive thresholds to ensure that the judgment results conform to the actual operational control needs. In addition, by performing multi-level false alarm suppression processing on the first warning signal, a second warning signal and its corresponding warning event are obtained.A multi-level filtering mechanism progressively eliminates invalid alarm signals from different dimensions. It merges similar alarms based on regional dimensions to avoid duplicate alarms from the same type of anomaly in the same region, filters fluctuations based on risk probability trends to eliminate occasional false alarms caused by data noise, and verifies the correlation between risk factors and alarm events based on causal reasoning to eliminate invalid alarms triggered by false correlations. This progressively layered filtering design continuously improves the credibility of alarm signals and reduces the interference of invalid alarms on normal operations. Furthermore, by pushing alarm events to the rule engine, corresponding response rules are matched against a pre-built anomaly scenario knowledge base. The risk type and risk level of the alarm event are extracted as the matching criteria. The rule matching engine uses logical reasoning to filter a set of response rules that meet the triggering conditions, sorts and filters multiple rules based on preset priority rules, and determines the final response rule to be executed. The anomaly scenario knowledge base contains standardized anomaly handling logic, supporting the updating and iteration of handling strategies. The rule matching process uses automated reasoning to ensure accurate matching between alarm events and response rules. Finally, the rule engine executes tiered notifications, work order creation, and business process orchestration operations based on the matched response rules. Automated execution replaces manual processes in standardized anomaly handling. Tiered notifications accurately deliver warning information and handling requirements to the appropriate positions and personnel. Automated work order creation simultaneously initiates the formal anomaly handling process. Cross-system business process orchestration breaks down operational barriers between different business systems, enabling automatic workflow after a warning is triggered. The entire execution logic follows pre-defined standardized paths, ensuring the standardization and consistency of the anomaly response process. By implementing this invention, significant shortcomings in current logistics warning and rule execution solutions exist in multiple aspects, including rule configuration efficiency, multi-source data fusion, dynamic risk assessment, automated handling, and closed-loop optimization, making it difficult to meet the refined, intelligent, and rapid-response operational management needs of modern logistics.

[0009] In one optional implementation, the method further includes: obtaining the execution results of the rule engine, business confirmation feedback, and actual effect data, which are used for risk prediction model, early warning threshold and periodic bottleneck analysis of monitoring rule execution, process digital twin simulation verification, and gray-scale testing.

[0010] In one optional implementation, logistics monitoring request data input by business users is obtained, and intent recognition and key element extraction are performed on the logistics monitoring request data using a logistics language model to generate executable monitoring rules. This includes: parsing the semantic structure of the logistics monitoring request data using the logistics language model to generate a rule abstract syntax tree; compiling the rule abstract syntax tree into corresponding format monitoring rules based on a preset execution scenario, wherein the corresponding format monitoring rules include database query mode and stream processing rule mode, used to adapt to batch scanning and real-time monitoring scenarios respectively; and performing syntax verification and permission verification on the corresponding format monitoring rules to generate executable monitoring rules.

[0011] In one optional implementation, logistics operation data is collected, and the data is sequentially cleaned, spatiotemporally aligned, feature extracted, and feature fused to generate a multidimensional risk feature vector. This includes: collecting time-series logistics operation data, business system-related logistics operation data, external environment-related logistics operation data, and text-based logistics operation data; performing corresponding cleaning processes on these data types to remove abnormal data and fill in missing data; using the waybill identifier as the primary key, performing spatiotemporal alignment processing on these data types to extract multidimensional risk features; and employing an attention-weighted multimodal fusion strategy to fuse the multidimensional risk features, generating a multidimensional risk feature vector.

[0012] In one optional implementation, a multidimensional risk feature vector is input into a pre-trained risk prediction model, and the risk prediction probability corresponding to each logistics risk type is output. This includes: using a shared feature extraction network to extract basic features from the multidimensional risk feature vector to obtain a shared feature vector; using a high-order cross network to mine the composite risk associations of the shared feature vector to generate a cross feature vector; and using three parallel prediction branches to calculate the risk prediction probability corresponding to each logistics risk type based on the shared feature vector and the cross feature vector. The three parallel prediction branches correspond to delay risk, cargo damage risk, and customer complaint risk, respectively, and are adapted to time series prediction models, gradient boosting tree models, and text classification models.

[0013] In one optional implementation, a warning threshold is calculated, and a warning determination is made based on the warning threshold and executable monitoring rules to determine the risk prediction probability corresponding to each logistics risk type, thereby generating a first warning signal. The warning threshold is dynamically adjusted based on real-time context. This includes: calculating the warning threshold corresponding to each logistics risk type; using executable monitoring rules to filter the risk prediction probability corresponding to each logistics risk type to obtain the filtered risk prediction probability; and comparing the filtered risk prediction probability with the corresponding warning threshold to generate a first warning signal.

[0014] In one optional implementation, the early warning threshold for each logistics risk type is calculated, including: setting a baseline early warning threshold for each logistics risk type based on the statistical distribution of historical logistics data; extracting multi-dimensional influencing factors from the real-time context and calculating a comprehensive adjustment coefficient; and obtaining the early warning threshold for each logistics risk type by multiplying the baseline early warning threshold and the comprehensive adjustment coefficient.

[0015] In one optional implementation, a multi-level false alarm suppression process is performed on the first warning signal to obtain a second warning signal and the corresponding warning event. This includes: performing a merging process on the first warning signal based on the region to generate several groups of regional warning signals; extracting the risk probability change trend of each group of regional warning signals and performing a first filtering process based on the trend threshold of each region; performing causal reasoning verification on the warning events of each group of regional warning signals after the first filtering process, and performing a second filtering process to obtain the second warning signal and the corresponding warning event.

[0016] In one optional implementation, the warning event is pushed to the rule engine to match the corresponding response rule. The response rule is pre-set in the abnormal scenario knowledge base, including: extracting the risk type and risk level of the warning event; performing logical reasoning on the abnormal scenario knowledge base based on the risk type and risk level through the rule matching engine to match the set of response rules that meet the triggering conditions; sorting the set of response rules based on preset priority rules to determine the corresponding response rule.

[0017] Secondly, this invention provides a collaborative system for intelligent logistics early warning and rule engine based on a language model. The system includes: an extraction module for acquiring logistics monitoring requirement data input by business users, using a logistics language model to perform intent recognition and key element extraction on the logistics monitoring requirement data, and generating executable monitoring rules; a generation module for collecting logistics operation data, sequentially cleaning, spatiotemporally aligning, extracting features, and fusing features to generate a multi-dimensional risk feature vector; a prediction module for inputting the multi-dimensional risk feature vector into a pre-trained risk prediction model, outputting the risk prediction probability corresponding to each logistics risk type; a judgment module for calculating an early warning threshold, determining the risk prediction probability corresponding to each logistics risk type based on the early warning threshold and executable monitoring rules, generating a first early warning signal, wherein the early warning threshold is dynamically adjusted based on real-time context; a suppression module for performing multi-level false alarm suppression processing on the first early warning signal to obtain a second early warning signal and corresponding early warning events; a matching module for pushing the early warning events to the rule engine, matching corresponding response rules, wherein the response rules are pre-placed in an abnormal scenario knowledge base; and an execution module for using the rule engine based on the corresponding response rules to execute hierarchical notifications, work order creation, and business process orchestration. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a specific example of a language model-based intelligent logistics early warning and rule engine collaborative method according to an embodiment of the present invention. Figure 2 This is an overall architecture diagram of the language model-based intelligent logistics early warning and rule engine collaborative method according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the closed-loop process of perception, prediction, decision-making, and execution of a language model-based intelligent logistics early warning and rule engine collaborative method according to an embodiment of the present invention. Figure 4 This is a flowchart of the natural language to monitoring rule conversion process of the collaborative method of intelligent logistics early warning and rule engine based on language model according to an embodiment of the present invention. Figure 5 This is a diagram of a multi-dimensional risk prediction model architecture based on a language model-based intelligent logistics early warning and rule engine collaborative method according to an embodiment of the present invention. Figure 6 This is a logic diagram of dynamic threshold adjustment and false alarm suppression of a language model-based intelligent logistics early warning and rule engine collaborative method according to an embodiment of the present invention. Figure 7 This is a sequence diagram of the collaborative interaction between early warning and rule engine in the logistics intelligent early warning and rule engine collaborative method based on language model according to an embodiment of the present invention; Figure 8 This is a principle block diagram of a specific example of a language model-based intelligent logistics early warning and rule engine collaborative system according to an embodiment of the present invention. Detailed Implementation

[0020] With the deepening of global supply chain collaboration and the continuous upgrading of the domestic consumer market, modern logistics operations have formed a full-chain network covering trunk transportation, warehousing and sorting, cross-border customs clearance, and last-mile delivery. The characteristics of numerous nodes, long links, and dispersed participants are becoming increasingly prominent. During operation, various uncertainties such as weather fluctuations, traffic congestion, port scheduling changes, equipment failures, and document anomalies can cause chain disruptions at any time, directly affecting delivery timeliness and service quality. Against this backdrop, proactive risk warning and efficient anomaly handling capabilities have become core tools for logistics companies to ensure operational stability and improve customer experience, directly impacting their operating costs and market competitiveness.

[0021] The logistics industry has already established a certain foundation for digital transformation. Various business systems, such as transportation management, warehousing management, and customs clearance management, have achieved large-scale application, accumulating massive amounts of operational data on order status, node flow, and resource scheduling, providing basic data support for risk monitoring and early warning. Most mainstream early warning solutions in the industry are built on rule modules built into business systems. By pre-setting judgment criteria, alarms are triggered for abnormal states exceeding thresholds, which to some extent replaces manual inspections and improves the efficiency of anomaly detection. However, in actual implementation, these solutions generally suffer from high technical barriers and long response cycles in the rule configuration stage. Adding and adjusting early warning rules heavily relies on technical personnel writing database query statements or completing code-level logic development; business operations personnel without a technical background cannot complete the configuration independently. When business scenarios change, such as adding monitoring requirements for specific routes, adjusting the stop judgment criteria in the customs clearance process, or responding to sudden business fluctuations, monitoring requirements need to go through a complete process of requirement submission, technical development, functional testing, and deployment, with a response cycle typically several working days. This is difficult to match the characteristics of logistics business—numerous sudden factors and flexible and ever-changing monitoring requirements—and many refined control demands cannot be promptly translated into implemented monitoring capabilities.

[0022] At the data support level, most current early warning solutions rely solely on time-series status data from a single business system to determine rules. For example, they trigger alarms based solely on whether the node dwell time or total transportation time exceeds preset standards, failing to effectively integrate multi-source heterogeneous information such as vehicle location trajectories, environmental sensor data, meteorological and traffic information, port operation dynamics, and customer feedback text. Single-dimensional data cannot comprehensively reflect the true risk status of the logistics chain. For potential risks caused by external factors such as severe weather, regional port congestion, and capacity fluctuations, the system cannot identify signals in advance and can only trigger alarms after the anomaly actually occurs. The lead time for early warning is severely insufficient, making it difficult for operators to activate emergency plans before risks escalate. Regarding risk assessment mechanisms, these solutions generally adopt fixed threshold judgment logic, unable to dynamically adjust judgment standards based on contextual information such as seasonal cycles, holiday effects, regional network operation status, and cargo attributes. During peak business periods and holidays, when network-wide timeliness generally fluctuates, fixed thresholds trigger a large number of alarms with no practical value, resulting in alarm overload. Furthermore, for high-value goods and cold chain goods requiring strict control, fixed thresholds cannot achieve more refined risk management. A persistently high percentage of invalid alarms gradually reduces the importance that operations personnel place on early warning information, ultimately leading to a significant decline in the actual control effectiveness of the early warning system. At the anomaly handling and system collaboration level, the early warning module and the business execution module are typically independent of each other. After an alarm signal is issued, operations personnel must manually determine the anomaly level, manually create work orders, notify relevant positions, and dispatch emergency resources. The handling process is highly dependent on personal experience, lacking standardized handling paths and automated execution capabilities, with overall response times generally measured in hours. Furthermore, the results of anomaly handling cannot be automatically fed back into the early warning system. The system cannot optimize the early warning model and rule parameters based on actual handling effects, making it difficult to form a continuously iterative closed-loop management system. Consequently, the system's operational effectiveness gradually diminishes as business scenarios change.

[0023] In summary, the relevant logistics early warning and rule enforcement solutions have significant shortcomings in several aspects, including rule configuration efficiency, multi-source data fusion, dynamic risk assessment, automated handling, and closed-loop optimization, making it difficult to meet the refined, intelligent, and rapid response operation and management needs of modern logistics.

[0024] To address the technical problems mentioned in the background section, this application provides a collaborative method for intelligent logistics early warning and rule engine based on a language model, as detailed below. Figure 1 As shown, the method includes: Step S101: Obtain logistics monitoring requirement data input by business users, use a logistics language model to perform intent recognition and key element extraction on the logistics monitoring requirement data, and generate executable monitoring rules.

[0025] Specifically, step S101 includes: Step a1: Use the logistics language model to parse the semantic structure of the logistics monitoring demand data and generate a rule abstract syntax tree.

[0026] Furthermore, Figure 2 This is an overall architecture diagram of the language model-based logistics intelligent early warning and rule engine collaborative method according to an embodiment of the present invention. From bottom to top, it consists of a data access layer, a data processing layer, an intelligent early warning layer, a rule engine layer, and a collaborative closed-loop layer. Each layer completes data collection, feature processing, risk early warning, rule matching, and iterative optimization, thus constructing a full-link intelligent early warning and response technology system. Figure 3 This is a flowchart illustrating the closed-loop process of perception, prediction, decision-making, and execution of a language model-based collaborative method for intelligent logistics early warning and rule engine implementation, according to an embodiment of the present invention. The process begins with data perception, integrates multi-source heterogeneous data, performs feature fusion, and outputs the fused features to the risk prediction module to obtain various risk probabilities. Subsequently, through dynamic threshold determination and multi-layered false alarm suppression filtering, valid early warning events are generated. Then, the rule matching module matches corresponding handling strategies, triggering automatic execution responses to complete the entire process. Finally, the execution results flow back to the feedback optimization stage, combining continuous self-learning and evolutionary mechanisms to iteratively optimize the parameters across the entire chain, achieving continuous evolution of the system's handling capabilities. The data access layer receives logistics monitoring request data in natural language form input from business users, such as monitoring all high-value orders shipped overseas that have exceeded the time limit for customs clearance. Based on a pre-trained language model in the logistics field, the input text undergoes full semantic parsing, first completing overall intent recognition to clarify the user's core request: setting targeted monitoring rules and corresponding alarm actions. Then, entity extraction is performed sentence by sentence, extracting three core elements in sequence: monitored object type, filtering constraints, and early warning trigger actions. Simultaneously, the logical relationships between these elements are analyzed, clarifying the hierarchical division of parallel and nested conditions. Following the preset syntax node definition specifications, the parsed elements and logical relationships are mapped to corresponding types of syntax nodes, including condition nodes, logic nodes, aggregation nodes, and action nodes. A complete rule abstract syntax tree is built from top to bottom according to the logical hierarchy, fully preserving all constraints and logical connections of the original requirements.

[0027] Step a2: Based on the preset execution scenario, the rule abstract syntax tree is compiled into the corresponding format monitoring rules. The corresponding format monitoring rules include database query mode and stream processing rule mode, which are used to adapt to batch scanning and real-time monitoring scenarios respectively.

[0028] Furthermore, Figure 4This is a flowchart illustrating the natural language to monitoring rule conversion process of a language model-based collaborative method for intelligent logistics early warning and rule engine implementation, according to an embodiment of the present invention. The process begins with natural language input, where business personnel submit monitoring requests in everyday language. A large language model performs intent recognition and entity extraction, breaking down the requests into three core elements: filtering conditions, monitoring objects, and early warning actions. Simultaneously, a feedback loop is continuously optimized, and semantic recognition accuracy is iterated based on operational data. The extracted core elements enter the rule semantic parsing stage, constructing an abstract syntax tree. This syntax tree contains four types of node units: comparison nodes, logic nodes, aggregation nodes, and operation nodes, forming a hierarchical structure that fully carries the logical constraints and execution actions of the requests, preserving all logical relationships. Subsequently, the corresponding compilation path is selected based on the target execution scenario. For batch scanning scenarios, a database query mode is selected to generate structured query statements adapted to the offline batch inspection architecture; for real-time monitoring scenarios, a stream processing rule mode is selected to generate standardized configuration rules adapted to the real-time data stream monitoring architecture. Rules in both formats enter the rule verification and confirmation stage, completing syntax compliance verification, permission scope verification, and natural language back-translation confirmation. After all verifications pass, the user confirms the rule's deployment. The entire process automates the conversion of business natural language into executable rules, creating a direct path from business requirements to implementation. It identifies the target runtime scenario for each rule, determining whether it's suitable for offline scenarios with periodic batch inspections or online scenarios with real-time data stream processing. Based on the identification results, it selects the corresponding compilation target. For database query patterns, it sequentially converts each node in the rule abstract syntax tree into structured query language syntax fragments, assembling them according to logical hierarchy to form a complete query statement, adapting to the periodic scanning of the business database. For stream processing rule patterns, it converts condition nodes in the rule abstract syntax tree into stream processing event filtering conditions, time constraint logic into stream processing window configuration parameters, and action nodes into stream processing trigger action configurations, ultimately generating a standardized stream processing rule configuration file, adapting to continuous monitoring scenarios with real-time message streams. The same syntax tree can output rules in two formats simultaneously, ensuring complete consistency of monitoring logic under different runtime architectures.

[0029] Step a3: Perform syntax and permission checks on the corresponding format monitoring rules to generate executable monitoring rules.

[0030] Furthermore, the generated monitoring rules in the corresponding format undergo syntax and permission checks sequentially. First, syntax checking is performed, verifying the statement structure, field references, and logical expression validity of each rule based on the official syntax specifications of the target execution environment. This checks for syntax errors, missing fields, misuse of logical operators, and other issues. If the check fails, a correction prompt is returned at the corresponding location. After syntax checking passes, permission checks are performed, verifying that the monitored data scope, triggered action types, and notification target scope of the rule match the configured user's operation permissions based on the built-in permission control system. Unauthorized content is automatically intercepted and a prompt is displayed. Once both checks pass, the rule is synchronously deployed to the corresponding runtime environment, and a unique identifier and version record are generated for each rule, forming a fully executable monitoring rule for online operation.

[0031] Step S102: Collect logistics operation data, and perform cleaning, spatiotemporal alignment, feature extraction and feature fusion processing on the logistics operation data in sequence to generate a multidimensional risk feature vector.

[0032] Specifically, step S102 includes: Step b1: Collect time-series logistics operation data, business system logistics operation data, external environment logistics operation data, and text-based logistics operation data.

[0033] Furthermore, by utilizing a universal access protocol for IoT devices, various vehicle positioning and cargo sensing devices are connected to continuously collect time-series logistics operation data such as vehicle location, speed, cargo temperature and humidity, and vibration amplitude. Standardized interfaces and message queues are used to connect with various business systems such as transportation management systems, warehouse management systems, customs management systems, and order management systems, synchronously collecting logistics operation data related to business systems, including order status, warehouse operation progress, clearance checkpoint flow, and resource scheduling status. Third-party service standard interfaces are used to connect with meteorological services, traffic information services, port information services, and public event information platforms to collect external environmental logistics operation data such as weather forecasts, real-time road conditions, port berthing status, and information on holidays and large-scale events. Text-based logistics operation data, including customer feedback, anomaly reports, and customer service interaction records, is collected through customer service systems and complaint platforms. All collected data is uniformly connected to the message bus for subsequent distribution and circulation.

[0034] Step b2 involves performing corresponding cleaning processes on time-series logistics operation data, business system logistics operation data, external environment logistics operation data, and text-based logistics operation data to remove abnormal data and fill in missing data.

[0035] Furthermore, appropriate cleaning processes are performed for different types of logistics operation data. For time-series logistics operation data, filtering algorithms are used to remove location drift points and abnormal sensor jump values, and abnormal data exceeding the normal value range is identified and removed. For short-term data interruptions, interpolation algorithms are used to fill in missing time-series data points. For business system-related logistics operation data, data formats and field naming conventions across different systems are standardized, and abnormal records with null values, garbled characters, and incorrect formats are identified. Missing field information is filled in based on statistical patterns of similar data in the same batch, and data records with non-standard formats are corrected. For external environment-related logistics operation data, the matching of data timestamps and geographical ranges is verified, redundant data collected repeatedly is removed, and environmental prediction data for missing time periods is filled in. For text-based logistics operation data, deduplication and noise reduction processing is performed, meaningless redundant text is removed, and text encoding formats are standardized to provide standardized text materials for subsequent feature extraction.

[0036] Step b3: Using the waybill identifier as the primary key, perform spatiotemporal alignment processing on time-series logistics operation data, business system logistics operation data, external environment logistics operation data, and text-based logistics operation data to extract multi-dimensional risk features.

[0037] Furthermore, using the waybill identifier as the primary key, a one-to-one mapping relationship is established between data from different sources and their corresponding logistics entities. A sliding time window mechanism is employed, aligning data from different sources according to event timestamps based on a preset window size and sliding step, ensuring that various types of data within the same time window correspond to the same operational state of the same logistics entity. After spatiotemporal alignment, time-series features such as the rate of change in driving speed, node dwell time, and temperature and humidity fluctuations are extracted from the time-series data. Statistical features such as historical delay probability, node flow efficiency, and resource utilization are extracted from the business system data. Textual features such as semantic vectors and abnormal keywords are extracted from the text data. Simultaneously, features from different dimensions are combined to generate cross-features, such as cross-features formed by combining cargo type and weather conditions, ultimately forming a risk feature set covering multiple dimensions.

[0038] Step b4: An attention-weighted multimodal fusion strategy is used to fuse multidimensional risk features to generate a multidimensional risk feature vector.

[0039] Furthermore, standardization is first performed on the risk features of different modalities, mapping features with different value ranges to a unified numerical interval to eliminate fusion bias caused by differences in units. A dynamic weight calculation network is constructed based on an attention mechanism. Combined with the operational context of the current logistics scenario, the fusion weights corresponding to different modal features are automatically calculated, assigning higher weights to features with stronger risk representation capabilities and lower weights to features with lower correlation. Based on the calculated weight values, weighted summation is performed on time-series features, statistical features, textual features, and cross-features to complete the deep fusion of multimodal features. Finally, the fused features are arranged according to a preset dimensional order to generate a multidimensional risk feature vector with a unified format and fixed dimensions, which serves as standardized input data for subsequent risk prediction models.

[0040] Step S103: Input the multidimensional risk feature vector into the pre-trained risk prediction model and output the risk prediction probability corresponding to each logistics risk type.

[0041] Specifically, step S103 includes: Step c1: Use a shared feature extraction network to extract basic features from the multidimensional risk feature vector to obtain a shared feature vector.

[0042] Furthermore, Figure 5This is an architecture diagram of a multi-dimensional risk prediction model based on a language model-based collaborative method for intelligent logistics early warning and rule engine, according to an embodiment of the present invention. The input side receives four types of data sources: GPS time-series data, business system data, text data, and external environment data. The underlying shared feature extraction network sets up multiple feature encoders to adapt to the data processing needs of different modalities. The time-series encoder processes GPS time-series data, the text encoder processes text data, and the multilayer perceptron encoder processes structured business data and external environment data. Each encoder completes feature encoding for its corresponding modality data, outputting basic features of a unified dimension, achieving standardized feature transformation of different modalities. Shared layer parameters are reused across multiple prediction tasks, reducing the resource overhead of redundant computation. The encoded multi-modal features are input to an attention-weighted fusion unit, which dynamically allocates the weight ratios of different modal features based on an attention mechanism. Combined with cross-features, deep fusion of multi-modal features is completed, generating a fused feature vector that is simultaneously supplied to three parallel prediction heads. Three prediction heads correspond to different logistics risk types: the delay prediction head is based on a time-series prediction model, outputting the delay risk probability and corresponding risk level; the cargo damage prediction head is based on a gradient boosting tree model, outputting the cargo damage risk probability; and the complaint prediction head is based on a text classification model, outputting the complaint risk probability. The final unified output includes the warning event result, containing the risk probability value, risk level label, explanation of core risk factors, warning event time, and related order information. The shared feature extraction network is a low-level general feature extraction structure within a multi-task learning framework, possessing multi-layer nonlinear transformation capabilities. It can extract common basic risk representations for different risk types from the input data. Multi-dimensional risk feature vectors are input into the shared feature extraction network, undergoing multi-layer convolution and fully connected transformations to extract basic features layer by layer. It performs unified feature encoding on various types of information contained in the input vector, such as time-series status, cargo attributes, environmental conditions, and business nodes, transforming original features of different dimensions and sources into a unified-dimensional deep feature expression. During the extraction process, the basic correlation information of features of different dimensions is retained, while redundant noise in the original data is filtered out, outputting a shared feature vector with fixed dimensions. Shared feature vectors can be used by multiple subsequent risk prediction branches simultaneously, avoiding repeated execution of underlying feature extraction operations in different branches. This ensures that the prediction process for different risk types is based on a unified underlying feature foundation, such as common risk elements like transportation node status and basic cargo attributes, which can simultaneously support subsequent calculations of delay risk and cargo damage risk.

[0043] Step c2 involves using a high-order cross-network to mine composite risk associations of shared feature vectors and generate cross-feature vectors.

[0044] Furthermore, the high-order cross-network is a network structure specifically designed to mine high-order interactions between features. It can automatically identify the compound risk effects generated by combinations of multiple features, overcoming the limitations of independent judgment based on a single feature. By inputting shared feature vectors into the high-order cross-network, multi-layer cross-computation units perform sequential cross-combination operations on features of different dimensions, automatically learning the compound risk relationships formed by pairwise combinations and multiple feature superpositions, without the need for manually pre-defined feature combination rules. During the operation, it automatically captures the risk amplification or attenuation patterns when different features act together, such as scenarios where cold chain cargo attributes are combined with heavy rain conditions, or specific port nodes are combined with holiday periods—scenarios where multiple factors act together—accurately representing the compound risk level generated by the superposition of multiple factors. After completing multi-order cross-operations, the cross-operation results are integrated and dimensionally normalized to generate cross-feature vectors containing high-order compound risk information. These cross-feature vectors complement the original shared feature vectors, jointly supporting subsequent risk probability calculations.

[0045] Step c3: Using three parallel prediction branches based on shared feature vectors and cross feature vectors, calculate the risk prediction probability corresponding to each logistics risk type. The three parallel prediction branches correspond to delay risk, cargo damage risk and customer complaint risk, respectively, and are adapted to time series prediction model, gradient boosting tree model and text classification model.

[0046] Furthermore, the three parallel prediction branches correspond to three different types of logistics risks. Each branch adopts a model structure adapted to the characteristics of the corresponding risk data and independently completes the probability calculation for its respective risk. Shared feature vectors and cross-feature vectors are simultaneously input into the three parallel prediction branches. The delay risk branch operates based on a time-series prediction model, focusing on combining time-series features and node flow characteristics to perform time-series extrapolation calculations of the probability of logistics node delays in future periods, outputting the predicted probability of delay risk. The cargo damage risk branch operates based on a gradient boosting tree model, focusing on combining physical sensor features such as temperature, humidity, and vibration with cargo attribute features. It calculates the probability of cargo damage through ensemble voting of multiple decision trees, outputting the predicted probability of cargo damage risk. The customer complaint risk branch operates based on a text classification model, focusing on combining text features and historical complaint statistics. It calculates the probability of customers initiating complaints through semantic matching and classification, outputting the predicted probability of customer complaint risk. The three branches execute the calculations in parallel without interfering with each other, and finally output the predicted probability results of the three types of risks simultaneously. For example, for the same batch of cross-border transportation orders, three independent prediction results can be output at the same time: the probability of customs clearance delay, the probability of cargo damage, and the probability of customer complaints.

[0047] Step S104: Calculate the warning threshold, and make a warning judgment on the risk prediction probability corresponding to each logistics risk type based on the warning threshold and executable monitoring rules, and generate a first warning signal. The warning threshold is dynamically adjusted based on the real-time context.

[0048] Specifically, step S104 includes: Step d1: Calculate the warning threshold corresponding to each logistics risk type.

[0049] Specifically, step d1 includes: Step e1: Based on the statistical distribution of historical logistics data, set the baseline warning thresholds for each type of logistics risk.

[0050] Furthermore, the baseline warning threshold serves as a benchmark reference standard for judging various logistics risks. It is set based on the statistical regularities of historical logistics data and possesses universal benchmark judgment capabilities. Full historical logistics operation data for the corresponding risk type is extracted, and statistical analysis is performed on the probability of risk events and the distribution patterns of corresponding indicators. A quantile statistical method is used to select the critical value of the normal operating range as the benchmark judgment standard. Statistical calculations are performed separately for different logistics risk types. For delay risk, a corresponding baseline is set based on the distribution of historical node dwell time and delay occurrences. For cargo damage risk, a corresponding baseline is set based on the correlation distribution of historical cargo damage events and environmental indicators. For customer complaint risk, a corresponding baseline is set based on the correlation distribution of historical complaint events and service indicators. For example, for port clearance delay risk, the dwell time data of all clearance orders within past continuous periods is extracted to complete statistical distribution fitting. The high-order critical value of the normal clearance time distribution is selected as the baseline warning threshold for clearance delay risk, ensuring that the benchmark threshold conforms to the risk distribution patterns under normal operating conditions.

[0051] Step e2: Extract the multi-dimensional influencing factors of the real-time context and calculate the comprehensive adjustment coefficient.

[0052] Furthermore, the multi-dimensional influencing factors are various environmental and business factors that can change the level of logistics operation risk in the real-time context, covering four core influencing dimensions: time, region, event, and cargo. Real-time status data for each dimension is extracted under the current operational scenario, and the individual adjustment coefficient for each dimension is calculated based on the historical influence patterns of each dimension. For the time dimension, a time adjustment coefficient is calculated considering the weekday, holiday, and peak / off-peak season attributes of the current time period. For the region dimension, a region adjustment coefficient is calculated considering the road network congestion level and port operation efficiency of the current logistics node's area. For the event dimension, an event adjustment coefficient is calculated considering special circumstances such as abnormal weather, port scheduling changes, and public events within the current area. For the cargo dimension, a cargo adjustment coefficient is calculated considering the value level, transportation requirements, and category attributes of the corresponding cargo. A weighted summation is performed on the individual adjustment coefficients of each dimension to obtain the comprehensive adjustment coefficient. For example, when widespread heavy rainfall occurs, the individual adjustment coefficient for the event dimension is adjusted upwards, resulting in a corresponding increase in the comprehensive adjustment coefficient, adapting to the actual scenario of rising risk levels.

[0053] Step e3: The warning thresholds for each type of logistics risk are obtained by multiplying the baseline warning threshold and the comprehensive adjustment coefficient.

[0054] Furthermore, by combining the baseline standard and the real-time adjustment coefficient through multiplication, a dynamic early warning threshold adapted to the current scenario is generated. For each type of logistics risk, the corresponding baseline early warning threshold is multiplied by the comprehensive adjustment coefficient under the current scenario to obtain the early warning threshold corresponding to the current real-time context. Different logistics risk types calculate their corresponding thresholds independently, and the baseline standards and adjustment coefficients for each type of risk do not interfere with each other, ensuring that the judgment criteria for different risks are adapted to their respective risk characteristics. For different logistics nodes, different transportation routes, and different cargo batches, exclusive early warning thresholds can be calculated independently based on their respective real-time contexts, achieving refined threshold adaptation for each scenario. For example, for the risk of trunk line transportation delays during holidays, the judgment criteria are relaxed accordingly after multiplying the baseline early warning threshold by the comprehensive adjustment coefficient for the corresponding time period, adapting to the actual operating state of the general fluctuation in transportation timeliness across the entire network, and avoiding invalid early warnings triggered by regular fluctuations.

[0055] Step d2: Use executable monitoring rules to filter the risk prediction probabilities corresponding to each logistics risk type to obtain the filtered risk prediction probabilities.

[0056] Furthermore, the executable monitoring rules include the scope of monitoring objects and multi-dimensional filtering conditions set by business users, used to define the scope of logistics entities participating in the early warning judgment. The rules iterate through all logistics entities, matching and verifying the business attributes of each entity against the filtering conditions set in the executable monitoring rules. The filtering conditions include multi-dimensional attributes such as destination, current node, goods category, and transportation route, supporting matching and verification of multiple conditional logical combinations, fully meeting the monitoring scope requirements set by business users. Logistics entities with all matching attributes have their corresponding risk prediction probabilities included in the judgment set; logistics entities with no matching attributes have their corresponding risk prediction probabilities excluded from subsequent early warning judgment processes. For example, the executable monitoring rules limit the scope to orders with overseas destinations and current nodes in the customs clearance stage. During the filtering process, the destination and current node attributes of each order are checked one by one, and only the risk prediction probabilities corresponding to orders that meet the scope requirements are retained for subsequent comparison, ensuring that the early warning scope is completely consistent with business monitoring needs.

[0057] Step d3 involves comparing the filtered risk prediction probabilities with their corresponding early warning thresholds to generate the first early warning signal.

[0058] Furthermore, the predicted probability of each selected risk group is compared one by one with the corresponding dynamic early warning threshold to determine the trigger for an early warning. For each logistics entity included in the screening scope, the predicted probability of each type of risk is compared with the dynamic early warning threshold of the same type. Logistics entities with a predicted risk probability greater than or equal to the corresponding early warning threshold are triggered to generate the corresponding first early warning signal. Logistics entities with a predicted risk probability less than the corresponding early warning threshold maintain normal monitoring status, and the risk prediction results are continuously updated. The generated first early warning signal carries basic information such as the unique identifier of the corresponding logistics entity, risk type, risk prediction probability value, and trigger time, and is classified, organized, and stored according to risk type and region. For example, when the predicted probability of customs clearance delay risk for a cross-border transportation order exceeds the corresponding dynamic early warning threshold, a first early warning signal for customs clearance delay is generated, and the basic business information of the corresponding waybill is linked simultaneously, providing an accurate triggering basis for subsequent false alarm suppression and anomaly response handling.

[0059] Step S105: Perform multi-level false alarm suppression processing on the first warning signal to obtain the second warning signal and the corresponding warning event.

[0060] Specifically, step S105 includes: Step f1: Perform merging processing on the first warning signal based on the region to generate several sets of regional warning signals.

[0061] Furthermore, Figure 6This is a logic diagram of dynamic threshold adjustment and false alarm suppression in a language model-based logistics intelligent early warning and rule engine collaborative method according to an embodiment of the present invention. The threshold calculation follows the core formula that the adjusted threshold equals the baseline threshold multiplied by the context adjustment factor. The context adjustment factor covers four dimensions: event factor, time factor, cargo factor, and regional factor, corresponding to four influencing factors: sudden public events, differences between holidays and working days, cargo value attributes, and regional operational efficiency, respectively. Multiple factors are used together to generate a comprehensive adjustment coefficient. Simultaneously, online learning updates are performed based on continuous periodic business data sources to continuously iterate threshold-related parameters and output dynamic thresholds adapted to real-time operating scenarios. The risk prediction probability value is compared with the dynamic threshold; if the probability is lower than the threshold, normal monitoring is maintained; if the probability is higher than the threshold, the subsequent false alarm suppression stage begins. The lower part is a three-layer filtering mechanism for false alarm suppression, which sequentially sets up a correlation analysis filter, a trend analysis filter, and a causal reasoning verification three-layer progressive filtering link. The correlation analysis filter merges similar anomalies across multiple orders, generating regional aggregated alerts. The trend analysis filter judges alerts based on the rate of increase in risk probability; if the acceleration does not reach a threshold, it is considered a false alarm due to data fluctuation and filtered out. Causal reasoning verification verifies the causal relationship between risk factors and events using a structural causal model, eliminating invalid alerts triggered by false correlations. Alerts not eliminated after these three layers of filtering are ultimately output as high-confidence alert events, continuously improving the credibility of alert results by filtering out invalid alerts layer by layer. All first alert signals are spatially categorized and matched according to preset regional division rules. Regional division is based on operational units such as logistics nodes, geographical areas, port operation areas, and warehousing parks, with each region corresponding to a fixed geographical scope and operating entity. All first alert signals are traversed, extracting the region and risk type of the logistics entity corresponding to each alert signal, and grouping first alert signals of the same risk type within the same region into the same alert set. Aggregation processing is performed on the aggregated alert sets, merging duplicate alert trigger information, and statistically analyzing the number of logistics entities involved in the set and the overall risk level to generate corresponding regional alert signals. Regional-level early warning signals are aggregated early warning identifiers covering the same operating area and the same type of risk, replacing scattered single early warning signals. For example, if multiple cross-border orders in a port's operating area simultaneously trigger the first early warning signal for customs clearance delays, after regional merging processing, a single regional-level early warning signal for customs clearance delays corresponding to the port is generated, avoiding the duplicate push of multiple homogeneous early warnings.

[0062] Step f2: Extract the risk probability change trend of each group of regional early warning signals, and perform the first filtering process based on the trend threshold of each region.

[0063] Furthermore, for each set of regional warning signals, time-series data of risk probability within the corresponding region's continuous monitoring period are extracted, and the trend and acceleration of risk probability changes are calculated based on a sliding time window. The trend reflects the overall rise and fall of risk probability, while the acceleration reflects the severity of the risk increase. Trend thresholds are pre-set for different regions and risk types; these thresholds serve as the critical standard for determining whether risk fluctuations are normal noise or a genuine increase in risk. The calculated acceleration of risk probability changes is compared with the corresponding trend threshold. Regional warning signals whose acceleration does not reach the trend threshold are identified as noise fluctuations and are filtered out. Regional warning signals whose acceleration reaches the trend threshold are retained, completing the first filtering process. For example, if the probability of cargo damage in a certain warehousing area experiences short-term, small fluctuations but a slow overall rate of increase, and the acceleration is below the corresponding trend threshold, it is determined to be an invalid warning caused by random data fluctuations and is directly removed in the first filtering process, retaining only warning signals with continuously accelerating risk increases.

[0064] Step f3: Perform causal reasoning verification on the warning events of each group of regional warning signals after the first filtering process, and perform the second filtering process to obtain the second warning signal and the corresponding warning event.

[0065] Furthermore, a causal network between risk factors and risk events is constructed based on a structural causal model. This structural causal model is an inference model used to verify the true causal relationship between variables, capable of distinguishing between genuine causal relationships and spurious correlations. For regional-level early warning signals that have undergone the first filtering process, the core risk factors triggering the early warning are extracted one by one and substituted into the causal network for causal inference verification. The inference process traces the risk transmission chain, verifying whether the current risk factor can directly or indirectly lead to the occurrence of the corresponding early warning event, excluding early warning signals triggered by non-genuine risk factors such as accidental data coincidences or abnormal data collection. Early warning signals verified through causal inference are determined to be genuine and valid early warnings, completing the second filtering process and ultimately generating a second early warning signal and its corresponding early warning event. The early warning event includes complete information such as risk type, risk level, affected area, and associated logistics entities. For example, if the core reason for a delay early warning in a certain region is a probability calculation deviation caused by a temporary interruption in data collection, rather than a genuine operational anomaly, it is determined to be a spurious correlation after causal inference verification and is filtered out, retaining only valid early warning events with genuine causal links.

[0066] Step S106: Push the warning event to the rule engine and match the corresponding response rule. The response rule is pre-set in the abnormal scenario knowledge base.

[0067] Specifically, step S106 includes: Step g1: Extract the risk type and risk level of the warning event.

[0068] Furthermore, Figure 7This is a sequence diagram of the collaborative interaction between early warning and rule engine in a logistics intelligent early warning and rule engine collaborative method based on a language model, according to an embodiment of the present invention. The full-process time-series swimlane diagram is divided into six vertical swimlanes according to functional modules: data source, data processing layer, risk prediction model, dynamic threshold determination, false alarm suppression, and rule engine. It presents a complete end-to-end link from data input to rule processing according to the execution sequence, and the bottom dotted line is the feedback optimization loop of the whole process. Step 1: The data source side completes the collection and output of multi-source heterogeneous raw data, and transmits GPS time-series data, business system data, external environment data, and text data to the data processing layer to start the data input stage of the whole process. Step 2: The data processing layer completes data cleaning, spatiotemporal alignment, feature extraction, and multimodal feature fusion processing, and outputs standardized multi-dimensional risk feature vectors, which are transmitted to the risk prediction model as prediction input. Step 3: The risk prediction model performs parallel prediction operations based on a multi-task learning framework to calculate the probability values ​​of three types of risks: delay risk, cargo damage risk, and complaint risk, and completes the quantitative output of risk probability. Step 4: The risk prediction model transmits the calculated risk probability values ​​to the dynamic threshold determination module to enter the risk determination stage. Step 5: The dynamic threshold determination module calculates a dynamic determination threshold based on the baseline threshold and multi-dimensional context factors. It compares the risk probability with the corresponding threshold value. Warnings with probabilities higher than the threshold pass the determination and proceed to the subsequent false alarm suppression stage. Step 6: The initial warning signal determined by the threshold is transmitted to the false alarm suppression module, initiating multi-layered false alarm filtering. Step 7: The false alarm suppression module first performs correlation analysis, merging similar anomalies from multiple orders into a regional aggregated warning. Then, it sequentially passes trend analysis filtering and causal reasoning verification, eliminating invalid false alarms layer by layer. Step 8: The highly reliable warning event after three layers of filtering is pushed to the rule engine module, which matches the corresponding response rules and initiates an automated handling process. After handling, the execution results flow back along the bottom feedback loop to the front-end modules to optimize model parameters, threshold configurations, and monitoring rules, forming a complete closed-loop operation mechanism. The complete data structure of the warning event is parsed, extracting the risk type and risk level identifiers carried by the event. The warning event data includes multi-dimensional fields such as risk category identifier, risk probability value, related logistics entity information, and regional information. All fields are stored using a unified coding standard. The risk types correspond to the three core risks predicted in the early stages: delay risk, cargo damage risk, and customer complaint risk. The risk levels are generated based on the interval division results of the risk prediction probabilities, covering multiple levels from low to extremely high. The extraction process directly reads the standardized field values ​​from the warning events, completes field format validation and semantic mapping, and ensures that the extracted risk types and risk levels are completely consistent with the output of the early warning judgment stage.For example, for an early warning event triggered in a port operation area, the risk type is extracted as delay risk and the risk level is high. These two types of labels serve as the core matching basis for the subsequent rule matching process, accurately limiting the scope of matching rules.

[0069] Step g2 involves using a rule matching engine to perform logical reasoning on the abnormal scenario knowledge base based on risk type and risk level, and matching a set of response rules that meet the triggering conditions.

[0070] Furthermore, the anomaly scenario knowledge base is a pre-built standardized anomaly handling strategy repository, internally storing response rules corresponding to various anomaly scenarios. Each response rule includes three core elements: triggering conditions, handling strategies, and applicable scope. Triggering conditions are based on risk type and risk level as core judgment factors. The rule matching engine employs a forward chain reasoning mechanism, using the extracted risk type and risk level as fact input, traversing all response rules in the anomaly scenario knowledge base, and verifying the degree of matching between the rule's triggering conditions and the input facts. The reasoning process verifies the logical combination relationship of conditions layer by layer, incorporating response rules that fully meet the triggering conditions into the matching results, ultimately forming a set of response rules that meet the triggering conditions. For example, for the input fact of high-level delay risk, multiple response rules meeting the triggering conditions, such as backup capacity scheduling, customer notification, and internal work order creation, are matched, forming a set of response rules for the corresponding early warning event, providing a complete range of candidate rules for subsequent priority ranking.

[0071] Step g3: Sort the set of response rules based on preset priority rules to determine the corresponding response rules.

[0072] Furthermore, the preset priority rules are pre-defined rules for ranking and judging. Priority scoring rules are set based on multiple dimensions, including the urgency of the situation, the scope of resource impact, and business value weight, with different dimensions corresponding to different weight percentages. For each response rule in the response rule set, a priority score is calculated according to the preset priority rules; a higher score indicates a higher execution priority for the rule. The response rule set is then sorted in descending order of priority score, and the response rule at the top of the list is selected as the final response rule to be executed. For example, if the response rule set for a certain early warning event includes backup capacity scheduling rules, customer service notification rules, and internal operations reporting rules, based on the preset priority rule that emergency response takes precedence over information synchronization, the backup capacity scheduling rule has the highest priority score and is determined as the final execution response rule for the corresponding early warning event, ensuring that core response actions are initiated first.

[0073] Step S107: Utilize the rule engine to execute hierarchical notifications, work order creation, and business process orchestration based on the corresponding response rules.

[0074] Specifically, based on the tiered notification strategy in the corresponding response rules, the notification level and recipients are matched according to the risk level of the warning event. The tiered notification strategy pre-divides different notification scopes according to risk levels: low-risk levels correspond to regional operation positions, medium-risk levels to department heads and operation teams, and high-risk levels to operation management heads and cross-departmental collaborative positions. Based on the matched notification scope, a corresponding notification text is generated, containing basic information about the warning event, risk level, handling requirements, and reference guidelines. The notification content is simultaneously pushed to the corresponding personnel's office terminals and mobile terminals via multi-channel message push interfaces, ensuring that relevant personnel receive the warning information in a timely manner. For example, for a high-level port clearance delay warning, the notification is simultaneously pushed to the customs operations supervisor, on-site dispatcher, and customer service personnel. Each position receives the unified warning information and handling guidelines simultaneously, ensuring coordinated handling actions and avoiding delays caused by information transmission discrepancies. Based on the work order creation requirements in the corresponding response rules, an exception handling work order is created by calling the standardized interface of the work order management system. The work order creation process automatically fills in relevant information about the warning event, including risk type, risk level, scope of involved logistics entities, trigger time, and preliminary handling suggestions. Based on the responsibility allocation rules in the response rules, the system automatically matches the corresponding processing department and responsible person, and sets the processing priority and time limit requirements for work orders according to the risk level. After the work order is created, it automatically flows to the to-do list of the corresponding responsible person, and a work order creation notification is generated and pushed to the responsible person's terminal simultaneously. For example, for a cargo damage risk warning in the warehouse area, a cargo damage investigation and processing work order is automatically created, assigned to the corresponding investigation team in the warehouse operations department, and set with corresponding high priority and processing time limit. The work order automatically enters the team's to-do queue, eliminating the need for manual entry of work order information, ensuring the rapid initiation of the abnormal handling process, and achieving full traceability of the handling process. Based on the process orchestration logic in the corresponding response rules, pre-set cross-system business workflows are triggered to complete the automated process connection and resource scheduling of multiple systems. Business process orchestration is implemented based on a workflow engine, and the pre-set workflows define the cross-system operation sequence, data transfer rules, and resource call logic under different abnormal scenarios. After the corresponding workflow is triggered according to the response rules, the interfaces of different business systems are called in sequence to execute the corresponding operations. During the process, data synchronization and status verification between systems are automatically completed, eliminating the need for manual intervention in cross-system operations. For example, in response to a delay warning for trunk line transportation, a backup capacity scheduling workflow is triggered. This workflow sequentially calls the transportation management system to query the currently available capacity resources, calls the capacity scheduling system to perform capacity matching, and calls the order management system to update the estimated delivery time. The entire process is executed automatically in sequence, realizing an automated workflow for handling the situation after the warning is triggered and shortening the overall time required for abnormal response.

[0075] In some optional embodiments, the execution results of the rule engine, business confirmation feedback, and actual effect data are obtained for analysis of periodic bottlenecks in the execution of risk prediction models, early warning thresholds, and monitoring rules, as well as for process digital twin simulation verification and gray-scale testing.

[0076] Specifically, after completing the handling process, the execution results, business confirmation feedback, and actual effect data of the entire chain are collected from various business systems, work order systems, and feedback channels. Execution result data includes the completion status of rule execution, execution time of each handling node, resource call status, and process flow records. Business confirmation feedback data includes the handling personnel's annotations on the effectiveness of the warning, supplementary explanations of the handling process, and records of abnormal reasons. Actual effect data includes the final business results corresponding to the warning event, the actual risk occurrence, customer feedback information, and loss statistics. The collected data are standardized and organized, establishing a one-to-one mapping relationship according to the unique identifier of the warning event, forming a complete closed-loop dataset for warning handling. For example, for a delay warning, the completion status of the handling work order, the warning effectiveness judgment annotated by the operations personnel, the actual order delivery time, and customer feedback content are collected and uniformly aggregated into closed-loop feedback data for the corresponding warning event, providing complete data support for subsequent system optimization. According to a preset analysis cycle, all full-chain data of warnings and handling within the cycle are extracted, and bottleneck identification and efficiency analysis of the entire process are performed. The analysis process covers the entire chain of nodes, including rule generation, data processing, risk prediction, early warning judgment, false alarm suppression, rule matching, and response execution. Key performance indicators such as processing time, accuracy, false alarm rate, and response effectiveness are statistically analyzed for each node. Through indicator comparison and chain tracing, inefficient nodes with excessively long processing times, weak links with low accuracy, and process steps with poor response results are identified, pinpointing the bottlenecks in system operation. For the identified bottlenecks, different types of optimization solutions are generated, such as adjusting risk prediction model parameters, optimizing early warning thresholds, supplementing monitoring rules, and adjusting response processes. For example, if statistics show that the false alarm rate in the false alarm suppression stage is too high, and the accuracy of the risk prediction model's delay risk prediction is lower than expected, corresponding optimization solutions for model parameter tuning and threshold adjustment are generated, clarifying the optimization direction and expected goals, ensuring that optimization work accurately targets system weaknesses. A digital twin model of the process is constructed based on historical operational data across the entire chain. This digital twin model is a virtual simulation model that digitally maps the entire real early warning and response process, capable of simulating the system's operational effects under different parameter configurations. The generated optimization plan is input into the process digital twin model, and the entire process operation after the optimization plan is implemented is simulated in a virtual simulation environment, reproducing the business scenarios and data inputs within the historical period. After the simulation is completed, the performance indicators of each link in the simulation results are extracted and compared with the actual operating indicators before optimization to verify the improvement effect of the optimization plan on system performance and to identify any secondary problems that the optimization plan may cause. For example, for the early warning threshold adjustment plan, the adjusted threshold parameters are input into the digital twin model, and historical business data from the same period are run to compare the false alarm rate and false negative rate of early warnings before and after adjustment, verifying the actual effect of the threshold adjustment, ensuring the feasibility and effectiveness of the optimization plan, and avoiding the operational risks of direct deployment. The optimization plan, after being verified by simulation, is gradually deployed for testing using a phased rollout approach.Gray-scale testing, based on pre-defined traffic distribution rules, selects certain business areas or scenarios as the test scope. Within the test scope, the optimized solution is applied, while the original solution continues to be used in other areas. During the testing period, operational data is continuously collected from both scopes, and the core performance indicators of the test and control groups are compared to evaluate the actual operational effectiveness and stability of the optimized solution. Once the test indicators reach the pre-defined compliance standards, the coverage of the gray-scale test is gradually expanded until full deployment. If any abnormal indicators occur during the test, the optimized solution is rolled back promptly. For example, for an optimized version of the risk prediction model, a portion of port areas are selected as the gray-scale test scope. The risk prediction accuracy and false alarm rate of the test and control areas are compared. After verification that the standards are met, the test is gradually expanded to all areas to ensure a smooth implementation of the optimized solution without impacting overall business operations.

[0077] The present invention proposes the following embodiments for different scenarios: Example 1 is applied to a periodic batch delay warning scenario in cross-border logistics customs clearance. The monitoring rules adopt a database query mode adapted to the batch scanning scenario. It acquires logistics monitoring request data in natural language form input by business users, specifying the monitoring of all orders shipped overseas that exceed the time limit for customs clearance. Based on a logistics language model, the semantic structure of the logistics monitoring request data is parsed, identifying three core elements: filtering conditions, monitoring objects, and warning actions, constructing a rule abstract syntax tree. Based on a preset batch scanning execution scenario, the rule abstract syntax tree is compiled into corresponding format monitoring rules in the database query mode, generating structured query statements adapted to the offline operation architecture of periodic batch inspections. Syntax and permission checks are performed on the generated structured query statements; after successful checks, executable monitoring rules are generated. Time-series logistics operation data, business system logistics operation data, external environment logistics operation data, and text-based logistics operation data are collected. Corresponding cleaning processes are performed on these four types of logistics operation data, removing abnormal data and filling in missing data. Using the waybill identifier as the primary key, spatiotemporal alignment processing is performed on the four types of logistics operation data to extract multi-dimensional risk features. A multimodal fusion strategy with attention weighting is employed to fuse multidimensional risk features, generating a multidimensional risk feature vector. This multidimensional risk feature vector is input into a pre-trained risk prediction model. A shared feature extraction network is used to extract basic features, resulting in a shared feature vector. A higher-order cross-network is used to mine composite risk associations, yielding a cross-feature vector. The predicted probability of customs clearance delay risk is calculated using the prediction branch corresponding to the delay risk. A baseline warning threshold for customs clearance delay risk is set based on the statistical distribution of historical logistics data. Multidimensional influencing factors from the real-time context are extracted to calculate a comprehensive adjustment coefficient. The warning threshold for customs clearance delay risk is obtained by multiplying the baseline warning threshold and the comprehensive adjustment coefficient. Executable monitoring rules are used to filter the risk prediction probabilities, retaining only the risk prediction probabilities for orders meeting the criteria of overseas destinations and customs clearance points. The filtered risk prediction probabilities are compared with the corresponding warning thresholds to generate a first warning signal. The first warning signals are then merged based on region, generating multiple sets of regional warning signals. The risk probability change trends of each set of regional warning signals are extracted, and a first filtering process is performed based on the trend thresholds of each region. Causal reasoning verification is performed on the warning events of each group of regional-level warning signals after the first filtering process. A second filtering process is then performed to obtain the second warning signal and its corresponding warning event. The risk type and risk level of the warning event are extracted. A rule matching engine performs logical reasoning on the abnormal scenario knowledge base based on the risk type and risk level to match a set of response rules that meet the triggering conditions. These response rules are then sorted according to preset priority rules to determine the corresponding response rules. Using the rule engine and the corresponding response rules, tiered notifications, work order creation, and business process orchestration operations are executed to complete the entire process of handling customs clearance delay warnings.

[0078] Example 2 is applied to a real-time cargo damage early warning scenario in cold chain warehousing. The monitoring rules adopt a stream processing rule model adapted to the real-time monitoring scenario. It acquires logistics monitoring request data in natural language form input by business users, specifying the risk of cargo damage caused by excessive temperature and humidity within the cold chain storage area. Based on a logistics language model, the semantic structure of the logistics monitoring request data is parsed, identifying three core elements: filtering conditions, monitoring objects, and early warning actions, thus constructing a rule abstract syntax tree. Based on a preset real-time monitoring execution scenario, the rule abstract syntax tree is compiled into corresponding format monitoring rules in the stream processing rule model, generating standardized stream processing configuration rules adapted to the continuous monitoring architecture of real-time data streams. Syntax and permission checks are performed on the generated stream processing configuration rules; after successful checks, executable monitoring rules are generated. Real-time collection of time-series logistics operation data, business system-related logistics operation data, external environment-related logistics operation data, and text-based logistics operation data is performed. Time-series data includes real-time sensor data such as cargo temperature and humidity, and vibration amplitude. Corresponding cleaning processing is performed on the four types of logistics operation data, removing abnormal jump data and supplementing short-term missing data. Using waybill identifiers as the primary key, real-time spatiotemporal alignment processing is performed on four types of logistics operation data to extract multi-dimensional risk features. An attention-weighted multimodal fusion strategy is employed to fuse these multi-dimensional risk features, generating a multi-dimensional risk feature vector. This multi-dimensional risk feature vector is input into a pre-trained risk prediction model. A shared feature extraction network is used to extract basic features, resulting in a shared feature vector. A higher-order cross-network is used to mine composite risk associations, yielding a cross-feature vector. The prediction probability for cold chain cargo damage risk is calculated using the prediction branch corresponding to cargo damage risk. A baseline warning threshold for cold chain cargo damage risk is set based on the statistical distribution of historical logistics data. Multi-dimensional influencing factors from the real-time context are extracted to calculate a comprehensive adjustment coefficient. The warning threshold for cold chain cargo damage risk is obtained by multiplying the baseline warning threshold and the comprehensive adjustment coefficient. Executable monitoring rules are used to filter the risk prediction probabilities, retaining only the risk prediction probabilities for goods within the cold chain storage area. The filtered risk prediction probabilities are compared with the corresponding warning thresholds to generate a first warning signal. The first warning signal is then merged based on the storage area to generate multiple sets of regional-level warning signals. The risk probability change trend of each group of regional early warning signals is extracted, and a first filtering process is performed based on the trend threshold of each storage area. Causal reasoning verification is performed on the early warning events of each group of regional early warning signals after the first filtering process, followed by a second filtering process to obtain the second early warning signal and its corresponding early warning event. The risk type and risk level of the early warning event are extracted, and logical reasoning is performed on the abnormal scenario knowledge base based on the risk type and risk level using a rule matching engine. A set of response rules that meet the triggering conditions is matched, and the set of response rules is sorted based on preset priority rules to determine the corresponding response rule.By leveraging the rule engine and corresponding response rules, hierarchical notifications, work order creation, and business process orchestration are executed to complete the entire process of real-time early warning of cold chain cargo damage.

[0079] Example 4 is applied to a comprehensive risk early warning scenario for the entire trunk transportation chain. Risk prediction employs a multi-task parallel prediction architecture to simultaneously output three types of risk results. It acquires logistics monitoring request data in natural language form input from business users, specifying the monitoring of all dimensions of operational risks along trunk transportation routes. Based on a logistics language model, the semantic structure of the logistics monitoring request data is parsed to construct a rule abstract syntax tree. This tree is then compiled into corresponding format monitoring rules based on a preset execution scenario. After syntax and permission verification, executable monitoring rules are generated. Time-series logistics operation data, business system logistics operation data, external environment logistics operation data, and text-based logistics operation data are collected. Data cleaning, spatiotemporal alignment, feature extraction, and feature fusion are performed sequentially to generate multi-dimensional risk feature vectors. These multi-dimensional risk feature vectors are input into a pre-trained risk prediction model. A shared feature extraction network is used to perform basic feature extraction on the multi-dimensional risk feature vectors, yielding shared feature vectors. A high-order cross-network is used to mine the composite risk correlations of the shared feature vectors, generating cross-feature vectors. The shared feature vector and cross feature vector are simultaneously input into three parallel prediction branches. The delay risk branch is calculated based on a time-series prediction model, outputting the risk prediction probability corresponding to trunk transportation delay risk; the cargo damage risk branch is calculated based on a gradient boosting tree model, outputting the risk prediction probability corresponding to transportation cargo damage risk; and the customer complaint risk branch is calculated based on a text classification model, outputting the risk prediction probability corresponding to customer complaint risk. The three parallel prediction branches operate in parallel without interference, synchronously outputting the risk prediction probabilities corresponding to the three types of logistics risk. Warning thresholds are calculated for each of the three types of logistics risk. Executable monitoring rules are used to filter the prediction probabilities of each type of risk. The filtered risk prediction probabilities are compared with the corresponding warning thresholds to generate the first warning signal for each of the three risk types. Multi-level false alarm suppression processing is performed on the first warning signal, sequentially completing region merging, trend filtering, and causal verification to obtain the second warning signal and the corresponding warning event. The warning event is pushed to the rule engine to match the corresponding response rules. The rule engine uses the corresponding response rules to execute hierarchical notifications, work order creation, and business process orchestration operations to complete the warning handling of comprehensive risks across the entire supply chain.

[0080] Example 4 is applied to the continuous iterative optimization scenario of the trunk transportation early warning system, including a complete closed-loop self-optimization process. It executes a complete collaborative process between intelligent logistics early warning and the rule engine, completing the entire chain operation from rule generation, data processing, risk prediction, early warning judgment, false alarm suppression to rule execution. Simultaneously, it acquires the execution results of the rule engine, business confirmation feedback, and actual effect data, and aggregates them into a closed-loop optimization dataset according to the unique identifier of the early warning event. According to a preset analysis cycle, it extracts all early warning and handling data within the cycle, performs full-chain periodic bottleneck analysis, statistically analyzes the core performance indicators of each link, locates weak points in system operation, and generates corresponding optimization solutions. These optimization solutions include different types such as risk prediction model parameter adjustment, early warning threshold configuration optimization, monitoring rule supplementation, and handling process adjustment. Based on the historical operational data of the entire chain, a process digital twin model is constructed. The generated optimization solutions are input into the process digital twin model, and historical business scenarios are reproduced in a virtual simulation environment. The full-process operation status after the optimization solution is implemented is simulated, and the core performance indicators before and after optimization are compared to verify the actual effect and feasibility of the optimization solution and identify potential secondary problems. The optimized solution, validated through simulation, was deployed using a canary testing approach. A subset of transportation routes and business areas were selected for the canary testing, applying the optimized solution to these areas while retaining the original solution for the remaining areas. During the testing period, operational data was continuously collected from both areas, and the optimization effect was evaluated by comparing core performance indicators. Once the indicators met the targets, the testing scope was gradually expanded until full deployment. Any test anomalies were promptly rolled back. The optimized parameters were then synchronously updated to the risk prediction model, early warning threshold configuration, and monitoring rule system, enabling continuous iterative upgrades to system capabilities.

[0081] This embodiment provides a collaborative method for intelligent logistics early warning and rule engine based on a language model. First, by receiving logistics monitoring request data input by business users, the method uses a logistics language model to identify the intent of the request and extract key elements, generating structured, executable monitoring rules. The logistics language model is trained on a corpus of logistics domains to enhance semantic understanding, accurately identifying monitoring objects, filtering conditions, and execution actions in business statements. This directly converts business requirements in natural language form into system-recognizable rule logic, bridging the conversion path from business requirements to system implementation, eliminating language barriers between business statements and technical rules, and ensuring that the rule generation process adapts to the expression habits of business personnel, while preserving complete semantic mapping relationships throughout the rule generation process. Second, by collecting various types of logistics operation data, the method sequentially performs data cleaning, spatiotemporal alignment, feature extraction, and feature fusion processing to generate a unified format multidimensional risk feature vector. Adaptive cleaning methods are employed for logistics data from different sources and with varying structures to remove outliers and fill in missing information. Spatiotemporal alignment of multi-source data is achieved based on waybill identifiers, integrating scattered heterogeneous data into feature sets corresponding to the same logistics entity. A multimodal fusion strategy allocates weights to different features based on an attention mechanism, integrating the risk representation capabilities of different data types and outputting a standardized feature vector format. This provides a standardized and complete data input foundation for subsequent risk prediction. Then, by inputting multidimensional risk feature vectors into a pre-trained risk prediction model, the model outputs the risk prediction probability corresponding to each logistics risk type. The risk prediction model is built on a multi-task learning framework. A bottom-level shared feature extraction network extracts and reuses basic features, reducing the resource overhead of redundant computation. A higher-order cross-network mines the complex risk correlations between different features, capturing combined risk scenarios that cannot be covered by a single feature. Multiple parallel prediction branches correspond to different types of logistics risks, each branch employing computational logic adapted to its corresponding risk characteristics, simultaneously covering the prediction needs of multiple risk types and improving the comprehensiveness and rationality of the risk prediction results. Subsequently, by calculating the warning thresholds corresponding to each type of logistics risk, and combining this with executable monitoring rules to filter and compare the predicted probabilities of risks, a first warning signal is generated. The warning thresholds are dynamically adjusted based on the real-time context. The threshold calculation is based on the statistical distribution of historical logistics data to set a benchmark standard, and dynamically adjusted by combining multi-dimensional influencing factors of real-time operating scenarios. This ensures that the risk judgment standard adapts to the operating status of different time periods and regions. Combined with executable monitoring rules, the target monitoring scope is filtered, so that the warning judgment simultaneously meets the dual requirements of business-defined monitoring scope and dynamic risk standards. The warning judgment logic integrates business rules and adaptive thresholds to ensure that the judgment results conform to the actual operational control needs. In addition, by performing multi-level false alarm suppression processing on the first warning signal, a second warning signal and its corresponding warning event are obtained.A multi-level filtering mechanism progressively eliminates invalid alarm signals from different dimensions. It merges similar alarms based on regional dimensions to avoid duplicate alarms from the same type of anomaly in the same region, filters fluctuations based on risk probability trends to eliminate occasional false alarms caused by data noise, and verifies the correlation between risk factors and alarm events based on causal reasoning to eliminate invalid alarms triggered by false correlations. This progressively layered filtering design continuously improves the credibility of alarm signals and reduces the interference of invalid alarms on normal operations. Furthermore, by pushing alarm events to the rule engine, corresponding response rules are matched against a pre-built anomaly scenario knowledge base. The risk type and risk level of the alarm event are extracted as the matching criteria. The rule matching engine uses logical reasoning to filter a set of response rules that meet the triggering conditions, sorts and filters multiple rules based on preset priority rules, and determines the final response rule to be executed. The anomaly scenario knowledge base contains standardized anomaly handling logic, supporting the updating and iteration of handling strategies. The rule matching process uses automated reasoning to ensure accurate matching between alarm events and response rules. Finally, the rule engine executes tiered notifications, work order creation, and business process orchestration operations based on the matched response rules. Automated execution replaces manual processes in standardized anomaly handling. Tiered notifications accurately deliver warning information and handling requirements to the appropriate positions and personnel. Automated work order creation simultaneously initiates the formal anomaly handling process. Cross-system business process orchestration breaks down operational barriers between different business systems, enabling automatic workflow after a warning is triggered. The entire execution logic follows pre-defined standardized paths, ensuring the standardization and consistency of the anomaly response process. By implementing this invention, significant shortcomings in current logistics warning and rule execution solutions exist in multiple aspects, including rule configuration efficiency, multi-source data fusion, dynamic risk assessment, automated handling, and closed-loop optimization, making it difficult to meet the refined, intelligent, and rapid-response operational management needs of modern logistics.

[0082] The above are embodiments of the language model-based intelligent logistics early warning and rule engine collaboration method provided in this application. Other embodiments of the language model-based intelligent logistics early warning and rule engine collaboration method provided in this application are described below.

[0083] This invention also discloses a collaborative system for intelligent logistics early warning and rule engine based on language models, such as... Figure 2 As shown, the system includes: The extraction module is used to acquire logistics monitoring requirement data input by business users, and uses a logistics language model to perform intent recognition and key element extraction on the logistics monitoring requirement data to generate executable monitoring rules.

[0084] The generation module is used to collect logistics operation data, and sequentially clean, align, extract and fuse the logistics operation data to generate a multi-dimensional risk feature vector.

[0085] The prediction module is used to input multi-dimensional risk feature vectors into a pre-trained risk prediction model and output the risk prediction probability corresponding to each logistics risk type.

[0086] The judgment module is used to calculate the warning threshold, and based on the warning threshold and executable monitoring rules, it makes a warning judgment on the risk prediction probability corresponding to each logistics risk type, and generates a first warning signal. The warning threshold is dynamically adjusted based on the real-time context.

[0087] The suppression module is used to perform multi-level false alarm suppression processing on the first warning signal to obtain the second warning signal and the corresponding warning event.

[0088] The matching module is used to push warning events to the rule engine and match the corresponding response rules. The response rules are pre-set in the abnormal scenario knowledge base.

[0089] The execution module is used to execute hierarchical notifications, work order creation, and business process orchestration based on corresponding response rules using the rules engine.

[0090] The technical solution of this invention has the following advantages: The logistics intelligent early warning and rule engine collaborative system based on a language model provided in this embodiment first receives logistics monitoring request data input by business users. Based on a logistics language model, it identifies the intent of the request and extracts key elements to generate structured, executable monitoring rules. The logistics language model is trained on a corpus of logistics-related language to accurately identify monitoring objects, filtering conditions, and execution actions in business statements. It directly converts business requirements in natural language form into system-recognizable rule logic, bridging the conversion path from business requirements to system implementation, eliminating language barriers between business statements and technical rules, and ensuring that the generation process of monitoring rules adapts to the expression habits of business personnel, while maintaining complete semantic mapping relationships throughout the rule generation process. Secondly, by collecting various types of logistics operation data, it sequentially completes data cleaning, spatiotemporal alignment, feature extraction, and feature fusion processing to generate a unified format of multidimensional risk feature vectors. Adaptive cleaning methods are employed for logistics data from different sources and with varying structures to remove outliers and fill in missing information. Spatiotemporal alignment of multi-source data is achieved based on waybill identifiers, integrating scattered heterogeneous data into feature sets corresponding to the same logistics entity. A multimodal fusion strategy allocates weights to different features based on an attention mechanism, integrating the risk representation capabilities of different data types and outputting a standardized feature vector format. This provides a standardized and complete data input foundation for subsequent risk prediction. Then, by inputting multidimensional risk feature vectors into a pre-trained risk prediction model, the model outputs the risk prediction probability corresponding to each logistics risk type. The risk prediction model is built on a multi-task learning framework. A bottom-level shared feature extraction network extracts and reuses basic features, reducing the resource overhead of redundant computation. A higher-order cross-network mines the complex risk correlations between different features, capturing combined risk scenarios that cannot be covered by a single feature. Multiple parallel prediction branches correspond to different types of logistics risks, each branch employing computational logic adapted to its corresponding risk characteristics, simultaneously covering the prediction needs of multiple risk types and improving the comprehensiveness and rationality of the risk prediction results. Subsequently, by calculating the warning thresholds corresponding to each type of logistics risk, and combining this with executable monitoring rules to filter and compare the predicted probabilities of risks, a first warning signal is generated. The warning thresholds are dynamically adjusted based on the real-time context. The threshold calculation is based on the statistical distribution of historical logistics data to set a benchmark standard, and dynamically adjusted by combining multi-dimensional influencing factors of real-time operating scenarios. This ensures that the risk judgment standard adapts to the operating status of different time periods and regions. Combined with executable monitoring rules, the target monitoring scope is filtered, so that the warning judgment simultaneously meets the dual requirements of business-defined monitoring scope and dynamic risk standards. The warning judgment logic integrates business rules and adaptive thresholds to ensure that the judgment results conform to the actual operational control needs. In addition, by performing multi-level false alarm suppression processing on the first warning signal, a second warning signal and its corresponding warning event are obtained.A multi-level filtering mechanism progressively eliminates invalid alarm signals from different dimensions. It merges similar alarms based on regional dimensions to avoid duplicate alarms from the same type of anomaly in the same region, filters fluctuations based on risk probability trends to eliminate occasional false alarms caused by data noise, and verifies the correlation between risk factors and alarm events based on causal reasoning to eliminate invalid alarms triggered by false correlations. This progressively layered filtering design continuously improves the credibility of alarm signals and reduces the interference of invalid alarms on normal operations. Furthermore, by pushing alarm events to the rule engine, corresponding response rules are matched against a pre-built anomaly scenario knowledge base. The risk type and risk level of the alarm event are extracted as the matching criteria. The rule matching engine uses logical reasoning to filter a set of response rules that meet the triggering conditions, sorts and filters multiple rules based on preset priority rules, and determines the final response rule to be executed. The anomaly scenario knowledge base contains standardized anomaly handling logic, supporting the updating and iteration of handling strategies. The rule matching process uses automated reasoning to ensure accurate matching between alarm events and response rules. Finally, the rule engine executes tiered notifications, work order creation, and business process orchestration operations based on the matched response rules. Automated execution replaces manual processes in standardized anomaly handling. Tiered notifications accurately deliver warning information and handling requirements to the appropriate positions and personnel. Automated work order creation simultaneously initiates the formal anomaly handling process. Cross-system business process orchestration breaks down operational barriers between different business systems, enabling automatic workflow after a warning is triggered. The entire execution logic follows pre-defined standardized paths, ensuring the standardization and consistency of the anomaly response process. By implementing this invention, significant shortcomings in current logistics warning and rule execution solutions exist in multiple aspects, including rule configuration efficiency, multi-source data fusion, dynamic risk assessment, automated handling, and closed-loop optimization, making it difficult to meet the refined, intelligent, and rapid-response operational management needs of modern logistics.

[0091] In this embodiment, the language model-based intelligent logistics early warning and rule engine collaborative system is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

Claims

1. A collaborative method for intelligent logistics early warning and rule engine based on language model, characterized in that, The method includes: Obtain logistics monitoring request data input by business users, and use a logistics language model to perform intent recognition and key element extraction on the logistics monitoring request data to generate executable monitoring rules. Collect logistics operation data, and sequentially perform cleaning, spatiotemporal alignment, feature extraction and feature fusion processing on the logistics operation data to generate a multidimensional risk feature vector; The multidimensional risk feature vector is input into a pre-trained risk prediction model, which outputs the risk prediction probability corresponding to each logistics risk type. Calculate the warning threshold, and based on the warning threshold and the executable monitoring rules, determine the risk prediction probability corresponding to each logistics risk type and generate a first warning signal. The warning threshold is dynamically adjusted based on the real-time context. The first warning signal is subjected to multi-level false alarm suppression processing to obtain the second warning signal and the corresponding warning event; The warning event is pushed to the rule engine to match the corresponding response rule, and the response rule is pre-stored in the abnormal scenario knowledge base; The rules engine is used to execute hierarchical notifications, create work orders, and orchestrate business processes based on corresponding response rules.

2. The method according to claim 1, characterized in that, The method further includes: The system acquires the execution results, business confirmation feedback, and actual effect data of the rule engine, which are used for analyzing the periodic bottlenecks in the execution of risk prediction models, early warning thresholds, and monitoring rules, as well as for digital twin simulation verification of processes and gray-scale testing.

3. The method according to claim 1, characterized in that, The process involves acquiring logistics monitoring request data input by business users, using a logistics language model to perform intent recognition and key element extraction on the logistics monitoring request data, and generating executable monitoring rules, including: The semantic structure of the logistics monitoring demand data is analyzed using a logistics language model to generate a rule abstract syntax tree. Based on the preset execution scenario, the rule abstract syntax tree is compiled into a corresponding format monitoring rule. The corresponding format monitoring rule includes a database query mode and a stream processing rule mode, which are used to adapt to batch scanning and real-time monitoring scenarios respectively. The corresponding format monitoring rules are subjected to syntax and permission checks to generate executable monitoring rules.

4. The method according to claim 3, characterized in that, The collected logistics operation data undergoes sequential cleaning, spatiotemporal alignment, feature extraction, and feature fusion processing to generate a multidimensional risk feature vector, including: Collect time-series logistics operation data, business system-related logistics operation data, external environment-related logistics operation data, and text-based logistics operation data; Perform corresponding cleaning processes on time-series logistics operation data, business system logistics operation data, external environment logistics operation data, and text-based logistics operation data to remove abnormal data and fill in missing data. Using waybill identifiers as the primary key, spatiotemporal alignment processing is performed on time-series logistics operation data, business system logistics operation data, external environment logistics operation data, and text-based logistics operation data to extract multi-dimensional risk features; An attention-weighted multimodal fusion strategy is used to fuse the multidimensional risk features to generate a multidimensional risk feature vector.

5. The method according to claim 4, characterized in that, The step of inputting the multidimensional risk feature vector into a pre-trained risk prediction model and outputting the risk prediction probability corresponding to each logistics risk type includes: A shared feature vector is obtained by using a shared feature extraction network to extract basic features from the multidimensional risk feature vector. High-order cross-networks are used to mine composite risk associations with shared feature vectors and generate cross feature vectors; Using three parallel prediction branches based on the shared feature vector and the cross feature vector, the risk prediction probability corresponding to each logistics risk type is calculated respectively. The three parallel prediction branches correspond to delay risk, cargo damage risk and customer complaint risk, and are respectively adapted to time series prediction model, gradient boosting tree model and text classification model.

6. The method according to claim 5, characterized in that, The calculation of the early warning threshold involves determining the risk prediction probability corresponding to each logistics risk type based on the early warning threshold and the executable monitoring rules, generating a first early warning signal. The early warning threshold is dynamically adjusted based on real-time context, including: Calculate the early warning thresholds corresponding to each type of logistics risk; The risk prediction probabilities corresponding to each logistics risk type are filtered using executable monitoring rules to obtain the filtered risk prediction probabilities. The predicted probabilities of the screened risks are compared with the corresponding warning thresholds to generate the first warning signal.

7. The method according to claim 6, characterized in that, The calculation of the early warning thresholds corresponding to each logistics risk type includes: Based on the statistical distribution of historical logistics data, baseline warning thresholds are set for each type of logistics risk. Extract multi-dimensional influencing factors from the real-time context and calculate the comprehensive adjustment coefficient; The warning thresholds for each type of logistics risk are obtained by multiplying the baseline warning threshold and the comprehensive adjustment coefficient.

8. The method according to claim 7, characterized in that, The step of performing multi-level false alarm suppression processing on the first warning signal to obtain the second warning signal and the corresponding warning event includes: The first warning signal is processed by merging based on the region to generate several sets of regional warning signals; Extract the risk probability change trend of each group of regional early warning signals, and perform the first filtering process based on the trend threshold of each region; Causal reasoning verification is performed on the warning events of each group of regional warning signals after the first filtering process, and the second filtering process is performed to obtain the second warning signal and the corresponding warning event.

9. The method according to claim 8, characterized in that, The step of pushing the warning event to the rule engine to match the corresponding response rule, wherein the response rule is pre-stored in the abnormal scenario knowledge base, includes: Extract the risk type and risk level of the warning event; The rule matching engine performs logical reasoning on the abnormal scenario knowledge base based on risk type and risk level to match a set of response rules that meet the triggering conditions. The set of response rules is sorted according to a preset priority rule to determine the corresponding response rule.

10. A collaborative system for intelligent early warning and rule engine in logistics based on a language model, characterized in that, The system includes: The extraction module is used to acquire logistics monitoring requirement data input by business users, and use a logistics language model to perform intent recognition and key element extraction on the logistics monitoring requirement data to generate executable monitoring rules. The generation module is used to collect logistics operation data, and sequentially clean, align, extract and fuse the logistics operation data to generate a multi-dimensional risk feature vector. The prediction module is used to input the multidimensional risk feature vector into the pre-trained risk prediction model and output the risk prediction probability corresponding to each logistics risk type. The judgment module is used to calculate the warning threshold, and based on the warning threshold and the executable monitoring rules, to make a warning judgment on the risk prediction probability corresponding to each logistics risk type, and generate a first warning signal. The warning threshold is dynamically adjusted based on the real-time context. The suppression module is used to perform multi-level false alarm suppression processing on the first warning signal to obtain the second warning signal and the corresponding warning event; The matching module is used to push the warning event to the rule engine and match the corresponding response rule. The response rule is pre-stored in the abnormal scenario knowledge base. The execution module is used to execute hierarchical notifications, work order creation, and business process orchestration based on corresponding response rules using the rules engine.

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