Intelligent Logistics Vehicle Early Warning Digital Collaborative Analysis and Anomaly Monitoring Early Warning Method and System

Abstract

This application discloses a digital collaborative analysis and anomaly monitoring and early warning method and system for intelligent logistics vehicles. The method identifies early warning events based on multi-source operational data and a configurable strategy library. It drives the flow of events through multiple states, including pending verification, effective early warning, and handling, using a finite state machine model to achieve closed-loop management throughout the entire lifecycle. It introduces external data such as roadside units and weather radar, along with causal inference, to accurately distinguish root causes such as sudden changes in road conditions or mechanical failures. Differentiated handling and feedback mechanisms are matched to different states, driving iterative optimization of strategies. This application solves the problems of discrete processes, single triggers, and missing feedback in existing systems, improving the accuracy of early warnings and operational efficiency.

Description

Technical Field

[0001] This invention relates to the field of logistics vehicle monitoring and information technology, and in particular to a method and system for intelligent logistics vehicle early warning digital collaborative analysis and abnormal monitoring and early warning. Background Technology

[0002] With the rapid development of e-commerce and logistics, higher demands are being placed on the real-time performance, accuracy, and intelligence of vehicle status monitoring, cargo safety management, and response to abnormal events during transportation. Logistics vehicles may face various abnormal scenarios during transport, such as speeding, route deviation, abnormal tire pressure, excessive temperature and humidity, and prolonged stops. Failure to detect and effectively address these issues in a timely manner could lead to cargo damage, transportation delays, or even safety accidents.

[0003] Currently, mainstream logistics vehicle monitoring systems typically employ rule-based early warning mechanisms. These systems collect data from onboard terminals, including GPS positioning, temperature and humidity sensors, and OBD diagnostics, and compare this data with preset fixed thresholds. When real-time data exceeds the threshold range, an early warning is triggered and a notification is sent. However, in practical applications, such systems still suffer from the following technical problems: (i) The early warning process management is fragmented and lacks full life cycle status control. Existing systems often manage early warning events using fragmented code logic or simple status indicators. Each processing step (such as anomaly identification, policy matching, early warning triggering, action execution, and result feedback) operates independently, relying on manual intervention or loosely coupled interfaces for connection. When an early warning event occurs, there is a lack of a unified state model to describe and track its current stage, the processing steps it has undergone, and the next action to be taken. This fragmented management approach leads to the following consequences: first, the process is prone to interruption or freeze, making it difficult to trace abnormal states; second, there is a lack of effective correlation between early warning events and their handling results, making closed-loop management difficult; and third, when the system malfunctions, multiple steps require manual troubleshooting, resulting in low operational efficiency.

[0004] (ii) The early warning triggering mechanism is too simple and cannot adapt to complex and ever-changing transportation scenarios. Existing systems employ limited warning triggering methods, either supporting only rule-based automatic triggering or manual reporting. In automatic triggering mode, the system executes warning judgments according to fixed cycles or event-driven approaches. While this provides a fast response, its automatic identification capability is limited when facing unstructured anomalies such as sudden extreme road conditions, temporary traffic control, or hidden vehicle malfunctions, leading to potential missed warnings. Manual triggering mode relies entirely on human experience, resulting in significant response delays and high subjectivity. More importantly, existing systems lack a mechanism for unified scheduling and collaborative management of automatic and manual triggering, making switching between the two modes difficult and preventing the dynamic selection of the optimal triggering method based on the scenario.

[0005] (iii) The early warning result feedback mechanism is imperfect, making it impossible to form an effective closed-loop management. Existing early warning systems typically stop at simply pushing out warning information, lacking effective tracking and feedback mechanisms for subsequent stages such as the progress of handling after the warning is issued, whether the anomaly has been eliminated, and the effectiveness of the handling results. Even in systems that support the input of handling results, there is often a lack of automatic correlation between the results and the original warning event, requiring manual matching and verification, resulting in low utilization and poor real-time performance of feedback data. This open-loop early warning management model makes it impossible for managers to accurately assess the effectiveness of warnings and the timeliness of handling, and also makes it difficult to optimize and iterate early warning strategies based on historical data.

[0006] (iv) The system architecture has poor scalability and is difficult to support multi-source data fusion and high-concurrency processing. Traditional logistics monitoring systems often employ a monolithic application architecture with highly coupled functional modules. As the number of connected vehicles increases and data sources diversify (such as GPS, temperature and humidity sensors, tire pressure monitoring, weather APIs, and road condition APIs), the system faces challenges including heavy data processing pressure, difficulty in functional expansion, and high maintenance costs. Adding a new warning type or adjusting warning rules often requires modifying the core code and redeploying the entire system, resulting in long response times and high risks.

[0007] Therefore, how to construct a logistics vehicle early warning monitoring method and system that can perform standardized state management of early warning events throughout their entire lifecycle, support automatic and manual dual-mode collaborative triggering, and realize real-time feedback of handling results and iterative optimization of strategies is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a digital collaborative analysis and anomaly monitoring and early warning method and system for intelligent logistics vehicles, to solve the technical problems existing in the prior art.

[0009] In a first aspect, the present invention provides a method for intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning, the method comprising: Based on the multi-source operational data collected during the transportation process of logistics vehicles, combined with a pre-set configurable early warning strategy library, abnormal vehicle events are identified and early warning events are generated. The warning event is automatically verified for authenticity, the automatic verification result is recorded, and the warning event is initialized to a state to be verified. Using roadside units, weather radar, traffic flow detectors, and anonymous data of social vehicles on the same road segment as external intervention variables, causal inference is performed to perform root cause attribution and obtain causal inference results. The causal inference includes: performing causal inference based on road condition causal graphs at the road segment level to determine the posterior probability that the anomaly is attributed to sudden changes in road conditions or vehicle mechanical failures, and introducing a cargo vulnerability factor as a moderating variable in the road condition causal graph according to the type of transported goods. Based on a pre-built finite state machine model, and combined with the automatic verification results and the causal inference results, the early warning event is driven to flow between predefined discrete states, which are the state to be verified, the effective early warning state, the state in progress, the closed-loop state, the false alarm invalidation state, the missed alarm supplementation state, or the path cause waiting to be coordinated state. For the early warning events that have flowed to different discrete states, a corresponding digital collaborative handling strategy and data feedback mechanism are matched to complete one or more of the following operations in sequence: early warning handling, result feedback, report generation, and strategy iteration.

[0010] In some embodiments, the step of performing causal inference based on roadside units, weather radar, traffic flow detectors, and anonymous data of social vehicles on the same road segment as external intervention variables to perform root cause attribution and obtain causal inference results includes: Using the road segment where the logistics vehicle associated with the warning event is located as the analysis granularity, a Bayesian network structure is constructed for the road condition cause-effect graph; the road condition cause-effect graph contains various nodes used for anomaly attribution and directed edges between nodes; When the warning event is triggered, the current values ​​of the roadside unit, weather radar, traffic flow detector and anonymous data of social vehicles on the same road segment are used as evidence to input the road condition causal graph. Bayesian inference is used to calculate the first predicted probability that the latent variable node of the road condition change is true under the given evidence conditions, and the second predicted probability that the latent variable node of the vehicle mechanical failure is true. Based on the type of transported goods, the corresponding vulnerability factor coefficient α is read from the preset cargo parameter table, where 0 < α < 1; the first predicted probability is weighted and corrected using the vulnerability factor coefficient α to obtain the corrected road condition attribution probability P_road, and the second predicted probability is weighted and corrected to obtain the corrected vehicle attribution probability P_vehicle. If P_road is greater than or equal to the first attribution threshold, the anomaly is determined to be attributed to a sudden change in road conditions, and P_road is output as the posterior probability of the anomaly being attributed to the sudden change in road conditions. If P_vehicle is greater than or equal to the second attribution threshold, the anomaly is determined to be attributable to vehicle mechanical failure, and P_vehicle is output as the posterior probability that is usually attributable to vehicle mechanical failure. If P_road is less than the first attribution threshold and P_vehicle is less than the second attribution threshold, then it is determined that the attribution cannot be clearly defined.

[0011] In some embodiments, the step of driving the early warning event to flow between predefined discrete states based on a pre-built finite state machine model, combined with the automatic verification result and the causal inference result, includes: When the automatic verification result shows that the abnormality is real and the posterior probability of the road condition change in the causal inference result is greater than the first preset threshold, the warning event jumps from the pending verification state to the road cause pending coordination state. When the automatic verification result shows that the anomaly is real and the posterior probability of the vehicle mechanical fault in the causal inference result is greater than the second preset threshold, the warning event jumps from the pending verification state or the effective warning state to the handling state. When the automatic verification result indicates that the anomaly is real but the causal inference result indicates that the cause cannot be clearly attributed, the warning event will switch from the pending verification state to the effective warning state. When the automatic verification result is a false alarm, the warning event changes from the pending verification state to the false alarm invalidation state; When an automatic or manual trigger command is received, the warning event transitions from a valid warning state to a pending state. When the warning response result is received from the external system and the vehicle abnormality has been eliminated, the warning event transitions from the handling state to the closed-loop state. When a manually recorded abnormal event is confirmed and enters the handling process, the warning event will change from the missed reporting status to the handling status. The finite state machine model restricts closed-loop states and false alarm invalid states from transitioning to other states.

[0012] In some embodiments, the automatic authenticity verification further includes: The preset spatiotemporal consistency verification model is invoked to align the vehicle positioning data, on-board diagnostic timestamp data, and vehicle passage time data collected by the roadside unit in the multi-source operation data, and to construct the vehicle operation sequence feature vector. Based on the vehicle runtime sequence feature vector, the dynamic time warping distance between the actual observed sequence and the expected sequence in the preset normal driving mode library is calculated. When the dynamic time warping distance exceeds the preset threshold, it is determined that there is a time-space inconsistency anomaly. The method further includes: when the dynamic time warping distance output by the spatiotemporal consistency verification model shows a monotonically increasing trend within three consecutive verification cycles, automatically triggering the switching of the on-board device clock drift warning strategy of the corresponding vehicle in the configurable warning strategy library, and recording the switching operation in the log.

[0013] In some embodiments, the method further includes performing permission verification, condition verification, and log persistence for each state transition, wherein the permission verification includes verifying whether the current operating role has the permission to trigger the state transition; and / or, The condition verification includes checking whether the current state meets the preset jump trigger condition; and / or, The log solidification process involves storing the event ID, timestamp, source state, target state, triggering condition, operating role, and executed action for each state transition into the database, forming a state transition audit log.

[0014] In some embodiments, the digital collaborative processing strategy and data feedback mechanism includes at least one of the following: For early warning events that have progressed to the "in progress" stage, the notification service is automatically invoked to send early warning information to relevant personnel, and a collaborative handling work order is automatically generated. For early warning events that have reached the closed-loop state, the front-end page is automatically refreshed in real time and the early warning report data is updated synchronously. Each early warning message is assigned a unique identifier, which is then linked to the entire process of anomaly occurrence, early warning push, manual handling, anomaly elimination, and result verification, forming a closed-loop management system that integrates anomaly identification, status verification, early warning triggering, collaborative handling, result feedback, and data accumulation.

[0015] In some embodiments, the policy iteration operation includes: Regularly analyze the accuracy, false alarm rate, and missed alarm rate of early warnings under different business scenarios; The warning threshold is adjusted based on the warning accuracy, and the warning judgment rule is updated based on the false alarm rate and the missed alarm rate.

[0016] In some embodiments, the method further includes: A dynamic spatiotemporal correlation graph of early warning events is constructed, wherein the dynamic spatiotemporal correlation graph uses early warning events as nodes and uses the temporal correlation, spatial trajectory overlap and business attribute similarity between events as edge weights to form a traceable chain of early warning events; When the finite state machine model drives the early warning event to the effective early warning state, the graph query operation of the dynamic spatiotemporal correlation graph is automatically triggered to identify the set of related early warning events that meet the preset correlation threshold with the early warning event; Based on the current state of each associated early warning event in the set of associated early warning events, the processing priority of the early warning events and the state transition determination rules in the finite state machine model are dynamically adjusted, wherein: If there are homogeneous events in the set of associated early warning events that are in the process of being handled or in the closed-loop state, then the handling priority of the early warning event is increased and the waiting time window in the state transition determination rule of the homogeneous event is shortened by a preset ratio. If there is a homogeneous event in the set of associated early warning events that is in the false alarm invalidation state, then the confidence score of the early warning event is lowered, and the priority prediction level in the automatic verification result is downgraded by one level.

[0017] In some embodiments, the method further includes: Construct a road segment-level hybrid traffic flow twin with highway segments and service areas or toll station nodes as twin units, and simultaneously evolve logistics vehicle intelligent agents, social vehicle intelligent agents and roadside facility intelligent agents in the twin; When performing the strategy iteration operation, the new early warning strategy is rehearsed in the twin, and the strategy risk is assessed using a strategy infectious disease model: when the new early warning strategy causes any vehicle to trigger an early warning and enter the handling state, the vehicle is regarded as the source of infection, the abnormal transmission coefficient R0 is calculated, and whether the number of abnormal vehicles of the same type in the service area exceeds the capacity threshold is monitored. If the abnormal propagation coefficient R0>1, or if the number of abnormal vehicles of the same type in the service area exceeds the capacity threshold within a preset time, then the new early warning strategy is determined to be a high-risk strategy for infectiousness, and the new early warning strategy is automatically rejected. In response to mandatory inspection strategies for cold chain or hazardous chemicals, the twin simulates the parking conflicts caused by the intervention of the mandatory inspection strategy. If the simulation shows that subsequent vehicles will not be able to find compliant parking spaces within the specified time limit, the status of some vehicles in the process of being processed will be automatically delayed to the next qualified service area. For specific road sections, digital vaccines are injected into the twin to simulate the attribution correctness and treatment path effectiveness of the finite state machine model under extreme conditions. An immunization certificate for the road section is obtained through a vaccine testing strategy, and strategies that fail to pass are prohibited from being effective on the road section.

[0018] In a second aspect, the present invention provides a smart logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning system, the system being used to implement the method as described in any one of the first aspects, the system comprising: The multi-source data acquisition module is used to collect multi-source operational data during the transportation process of logistics vehicles; The early warning strategy configuration module is used to build and configure a configurable early warning strategy library; An abnormal event identification module, connected to the multi-source data acquisition module and the early warning strategy configuration module, is used to identify abnormal vehicle events based on the multi-source operational data and in combination with a preset configurable early warning strategy library, and generate early warning events. An automatic verification module, connected to the abnormal event identification module, is used to automatically verify the authenticity of the warning event, record the automatic verification result, and initialize the warning event to a state to be verified. The causal inference module is used to perform causal inference based on roadside units, weather radar, traffic flow detectors, and anonymous data of social vehicles on the same road segment as external intervention variables to perform root cause attribution and obtain causal inference results. The causal inference includes: performing causal inference based on a road condition causal graph with road segment as the granularity to determine the posterior probability that the anomaly is attributed to a sudden change in road conditions or a mechanical failure of the vehicle, and introducing a cargo vulnerability factor as a moderating variable in the road condition causal graph according to the type of transported goods. The finite state machine model module is used to store pre-built finite state machine models; The state transition control module, connected to the finite state machine model module, the automatic verification module, and the causal inference module, is used to drive the early warning event to flow between predefined discrete states based on the pre-built finite state machine model and the automatic verification result and the causal inference result. The discrete states are the state to be verified, the effective early warning state, the state in progress, the closed-loop state, the false alarm invalidation state, the missed alarm supplementation state, or the path cause waiting to be coordinated state. The collaborative handling module, connected to the state transition control module, is used to match the corresponding digital collaborative handling strategy and data feedback mechanism for the early warning event that has transitioned to different discrete states, and sequentially complete one or more of the following operations: early warning handling, result feedback, report generation, and strategy iteration.

[0019] In a third aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the computer program is executed by the processor, it implements the intelligent logistics vehicle early warning digital collaborative analysis and abnormal monitoring early warning method as described in any of the first aspects.

[0020] In a fourth aspect, the present invention provides a readable storage medium storing a computer program, wherein when the computer program is executed, it implements the intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method as described in any of the first aspects.

[0021] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: In implementing the technical solution of this invention, a finite state machine model is introduced to define early warning events as seven discrete states: pending verification, effective early warning, in progress, closed loop, false alarm invalidation, missed alarm supplementation, and road-related factors awaiting coordination. This drives the standardized flow of these states, achieving refined control and closed-loop management of the entire lifecycle of early warning events. This solves the problems of discrete process management, opaque states, and easily broken links in traditional systems. Simultaneously, it breaks through the limitations of a single triggering mechanism by introducing roadside units, weather radar, traffic flow detectors, and anonymous data from other vehicles on the same road segment as external intervention variables. A road condition causal graph is constructed at the road segment level, and causal inference is performed. Combined with cargo vulnerability factors as adjustment variables, this enables… It accurately distinguishes between different root causes such as sudden changes in road conditions and vehicle mechanical failures, significantly reducing the false alarm and missed alarm rates in complex transportation scenarios. In addition, it matches differentiated digital collaborative handling strategies and data feedback mechanisms to different flow states, forces the return of handling results and automatically associates them with the original early warning events, supports automatic report generation and iterative optimization of the early warning strategy library, forming a complete feedback and evolution closed loop. The overall system adopts a configurable strategy library and modular decoupled design, which makes it easy to add new early warning types or adjust rules without redeploying the entire system. It effectively supports multi-source data fusion and high-concurrency expansion, thereby significantly improving the real-time performance, accuracy, intelligence level and system operation and maintenance efficiency of logistics vehicle early warning monitoring.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Furthermore, similar numbers in the drawings are used to denote similar components, wherein: Figure 1This is a flowchart of the intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram of the structure of the intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning system provided in Embodiment 2 of this application.

[0024] Figure 3 This is a schematic diagram of the structure of the computer device provided in Embodiment 3 of this application. Detailed Implementation

[0025] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] To address the issues of discrete early warning process management, single triggering mechanism, imperfect feedback loop, and poor architectural scalability in the aforementioned background technologies, this application proposes a digital collaborative analysis and anomaly monitoring and early warning method and system for intelligent logistics vehicles. This method constructs a full lifecycle state control mechanism for early warning events based on a finite state machine model. It incorporates discrete states such as pending verification, valid early warning, in progress, closed loop, false alarm invalidation, and missed alarm supplementation into a unified state transition framework. Combined with permission verification, condition verification, and log solidification, it achieves traceability, auditability, and backtracking of the early warning process, effectively solving the problems of process interruption, missing correlations, and difficulty in closing loops in traditional systems.

[0027] Meanwhile, this method accurately distinguishes the root causes of anomalies by introducing automatic authenticity verification and roadside causal inference, effectively avoiding mis-assignment of repairs for road condition issues, and can automatically push work orders to road administration and broadcast warnings to subsequent vehicles; relying on a finite state machine to perform full lifecycle state management of warning events (including road cause pending coordination state), combined with permission verification, condition verification, and log solidification, it achieves traceable and auditable processes; and it matches differentiated collaborative handling strategies for different states, forming a complete closed loop from anomaly identification, causal attribution, state transition, handling collaboration to result feedback, report generation, and strategy iteration, significantly shortening response time, improving multi-party collaboration efficiency, enhancing system adaptability and continuous optimization capabilities, and providing logistics companies with standardized and quantifiable anomaly monitoring and responsibility definition support, which is conducive to improving management transparency and audit compliance.

[0028] Example 1 Figure 1This is a flowchart of the intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method provided in Embodiment 1 of this application. This method is applicable to various logistics and transportation application scenarios, such as express delivery, cold chain logistics, hazardous chemical transportation, pharmaceutical logistics, bulk commodity transportation, and urban distribution. Under different business scenarios, the system can flexibly configure differentiated early warning strategies and status transition rules according to the characteristics of the transported goods and management requirements. This provides logistics companies, fleet managers, drivers, and safety monitoring centers with accurate vehicle anomaly identification and collaborative handling services, meeting the differentiated needs of various transportation scenarios for real-time performance, accuracy, and closed-loop management capabilities. It has broad industry applicability and significant socio-economic benefits.

[0029] Reference Figure 1 As shown, the method includes the following steps: S100: Based on the multi-source operational data collected during the transportation process of logistics vehicles, combined with a pre-set configurable early warning strategy library, it identifies abnormal vehicle events and generates early warning events.

[0030] In some specific embodiments, multi-source operational data of the vehicle during transportation is acquired through various data acquisition terminals deployed on the logistics vehicle and connected to external data sources. This multi-source operational data includes, but is not limited to: real-time vehicle location, speed, and trajectory data obtained through GPS / BeiDou positioning modules; on-board equipment status data such as engine speed, fuel consumption, tire pressure, and fault codes collected through on-board diagnostic systems (OBD); cargo compartment environmental data obtained through temperature and humidity sensors; and transportation environment data and road condition scenario data obtained through access to external data sources such as meteorological service systems, road condition information systems, and traffic control notices.

[0031] Based on data collection, this application also pre-builds and stores a configurable early warning strategy library. This strategy library defines identification rules for various abnormal events, each rule including at least triggering conditions, threshold parameters, time windows, priority levels, and corresponding handling suggestions. For example, early warning strategies may include, but are not limited to: speeding strategies (speed continuously exceeding road limits for a certain period), route deviation strategies (the vehicle's actual trajectory deviates from the preset electronic fence or navigation path by an allowed distance), temperature and humidity exceeding limits strategies (in cold chain scenarios, the temperature or humidity of the cargo compartment exceeds a set range and remains within a set time), excessive dwelling strategies (the vehicle stays at a non-permitted stopping point for more than a specified time), abnormal tire pressure strategies, and rapid acceleration / deceleration strategies, etc., which will not be listed here in detail.

[0032] In some embodiments, the abnormal event identification process includes: real-time or near-real-time streaming processing of the collected multi-source operational data, and matching each data stream with identification rules in the early warning strategy library. When a data item or a combination of data items is detected to meet the triggering condition defined by a certain strategy, an abnormal vehicle event is determined to have occurred, and a corresponding early warning event is generated according to the predefined rules in the strategy library. The generated early warning event includes at least the following information: unique event identifier, event type, trigger time, trigger location (latitude, longitude, and road segment information), associated vehicle identifier, triggering strategy number, original data snapshot, priority level, etc.

[0033] S200: Perform automatic authenticity verification on the warning event, record the automatic verification result, and initialize the warning event to a state to be verified.

[0034] It should be noted that the early warning events generated in step S100 do not directly enter the handling process, but first enter the automatic authenticity verification stage. This stage aims to filter out false, invalid, or low-quality early warning events caused by momentary data fluctuations at a single point, occasional sensor failures, communication packet loss, or duplicate reporting, thereby improving the accuracy and reliability of the early warning system from the source. Specifically, the automatic authenticity verification includes the following sub-steps: 1. Multi-source data cross-validation: Utilizing the redundancy and complementarity among multi-source operational data, the authenticity of warning events is cross-validated. For example, when a warning event triggered by a certain data source needs verification, information from other independent data related to that warning event is automatically retrieved and compared. For instance, when GPS data triggers a "speeding" warning, the vehicle speed information recorded by the vehicle's OBD and the instantaneous speed data fed back by roadside radar or electronic eyes can be simultaneously compared. If the speed estimates from multiple sources are basically consistent within the time window, the speeding warning is deemed genuine and valid. If only the GPS data is abnormal while neither the OBD nor the roadside data shows speeding, the warning is likely caused by GPS signal drift or positioning error, and its credibility should be reduced or marked for manual review. Similarly, for "route deviation" warnings, a comprehensive judgment can be made by combining vehicle heading angle sensor data, the road probability distribution output by the map matching algorithm, and historical trajectory data of adjacent vehicles to avoid false alarms caused by outdated map data or positioning errors.

[0035] 2. Repetition Detection: Repeat detection is performed on early warning events generated by the same logistics vehicle within a similar time window to prevent the same anomaly from triggering numerous redundant early warnings and interfering with the judgment of personnel. In practice, a sliding time window (e.g., a list of early warning events generated within the last 5 minutes) can be maintained for each logistics vehicle. When a newly generated early warning event arrives, the system extracts its key features, including event type, trigger location area (discretely encoded grid), and associated sensor or data source identifiers. This feature is then compared with existing early warning events within the window for similarity calculation. If the new event is found to have the same event type as an existing event within the window, a trigger location distance less than a preset threshold, and a time interval less than a preset window length, it is determined to be a duplicate early warning. In this case, no independent new early warning event is generated. Instead, the new trigger record is aggregated as supplementary evidence to the existing early warning event, updating the occurrence count, latest trigger time, and original data snapshot list of the existing early warning event, and appropriately increasing its priority weight. Otherwise, the newly generated early warning event is determined to be a non-duplicate early warning, confirming its novelty in both time and space dimensions, and is thus considered a valid event for subsequent processing.

[0036] 3. Priority Prediction: Based on the type, urgency, potential impact, and business context of the warning event, an initial priority assessment is performed on warning events that have passed multi-source data cross-validation and repeatability testing. This provides a basis for decision-making regarding subsequent state transitions and resource allocation. Priority prediction follows a configurable rule system, which may include, for example, the following factors: the basic weight of the event type (e.g., "sudden tire pressure drop" has higher priority than "minor speeding"), the deviation of the current vehicle speed from the road limit (the larger the deviation, the higher the priority), the magnitude and duration of temperature and humidity deviations from target values, whether the vehicle is currently traveling at high speed or transporting hazardous materials, traffic flow and weather conditions on the road segment where the vehicle is located, etc. Priority prediction results can be divided into several levels, such as "low," "medium," "high," and "urgent." The priority level will directly affect the state transition weight of the warning event in the finite state machine, the order of task assignment, and the urgency of the notification method (e.g., emergency warnings use a dual notification method of telephone and SMS, while ordinary warnings only push in-app messages).

[0037] After completing the above automatic authenticity verification, the generated verification results (including cross-validation confidence score, repeatability test results, priority level, and corresponding judgment criteria) are structured and recorded, and stored in association with the original warning event. Subsequently, the warning event can be initialized to a pending verification state, indicating that the event has passed the initial automated authenticity screening, but whether to upgrade to a valid warning or mark it as a false alarm still needs to be determined according to subsequent state transition rules. Initializing the warning event to a pending verification state, rather than directly entering a valid warning state, reflects the design philosophy of this method for cautious confirmation of warnings, providing a unified initial entry point for subsequent refined state management driven by the finite state machine model.

[0038] S300: Based on roadside units, weather radar, traffic flow detectors, and anonymous data of social vehicles on the same road segment as external intervention variables, causal inference is performed to perform root cause attribution and obtain causal inference results. The causal inference includes: performing causal inference based on road condition causal graphs at the road segment level to determine the posterior probability that the anomaly is attributed to a sudden change in road conditions or a vehicle mechanical failure, and introducing a cargo vulnerability factor as a moderating variable in the road condition causal graph according to the type of transported goods.

[0039] In this embodiment, to achieve accurate root cause attribution of abnormal vehicle events, the method introduces roadside units, weather radar, traffic flow detectors, and anonymous data of other vehicles on the same road segment as external intervention variables. Specifically, a road condition causal graph is pre-constructed at the road segment level. This causal graph describes the potential causal dependencies between road segment traffic conditions, weather conditions, infrastructure conditions, vehicle operating parameters, and abnormal events. When a warning event is triggered, the aforementioned external intervention variables are used as input conditions, and causal inference is performed based on the causal graph to calculate the posterior probabilities that the current anomaly is attributed to "sudden road condition changes" and "vehicle mechanical failure," respectively.

[0040] Furthermore, a cargo vulnerability factor is introduced based on the type of transported goods (e.g., perishable goods, dangerous goods, high-value electronic equipment, etc.) as a moderating variable in the causal graph. This moderating variable differentiates the risk weights for different cargo types under the same abnormal conditions: for goods susceptible to road conditions (e.g., perishable goods sensitive to bumps), the posterior probability weight of attributing sudden road condition changes is appropriately increased; for goods more sensitive to the vehicle's own condition (e.g., dangerous goods requiring high braking performance), the weight of attributing vehicle mechanical failures is appropriately increased. Through this causal inference and moderating variable mechanism, the root cause of the anomaly can be more accurately determined, providing an interpretable and quantifiable basis for matching subsequent differentiated handling strategies.

[0041] S400: Based on a pre-built finite state machine model, and combining the automatic verification results with the causal inference results, the early warning event is driven to flow between predefined discrete states, which are the state to be verified, the effective early warning state, the state in progress, the closed-loop state, the false alarm invalidation state, the missed alarm supplementation state, or the path factor waiting to be coordinated state.

[0042] To overcome the shortcomings of traditional logistics monitoring systems, such as discrete management of early warning processes and untraceable states, this application introduces a finite state machine model to standardize and trace the state management of each early warning event throughout its entire lifecycle. The finite state machine model is a pre-built core control logic that explicitly defines all discrete states allowed for an early warning event, the legal triggering conditions for transitions between states, the transition judgment rules, and the state locking mechanism. Through this model, the entire process of an early warning event from its generation to its final closure or invalidation is abstracted into a series of standardized state transition paths. Any state change must satisfy predefined rules to be executed, thereby achieving standardized and automated control of the early warning process.

[0043] In some embodiments, the discrete states include at least the core states such as the pending verification state, valid early warning state, in-process state, closed-loop state, false alarm invalidation state, missed alarm supplementation state, and road-related coordination pending state. The pending verification state is the default state initialized after the early warning event completes the automatic authenticity verification step. It indicates that the event has passed the initial automated screening but has not yet been definitively confirmed as a valid early warning or false alarm, and is in an intermediate state awaiting further state transition. The valid early warning state is the state before the early warning event is confirmed as a real anomaly after verification or manual review and enters the formal handling process. Events in this state indicate that the system has confirmed the existence of the anomaly and will enter the collaborative handling stage. The in-process state is the state where the early warning event has been assigned to a designated handling role (such as driver, team leader, safety officer, etc.), and the handling operation is in progress but not yet completed. This state is used to indicate that the event is in an active handling process, avoiding duplicate dispatches or resource conflicts. The closed-loop state is the final state where the anomaly corresponding to the early warning event has been confirmed to be eliminated, or the handling operation has been completed and the result has been returned, and the event lifecycle has ended normally. Events in this state no longer participate in subsequent processing and are only used as historical records for querying and analysis. The "False Alarm Cancellation" state is the final state where a warning event, after verification or manual review, is determined to be a false anomaly (such as momentary sensor malfunction, data communication error, non-substantial deviation, etc.) and requires no further action. This state is used to mark invalid warnings to avoid interfering with normal monitoring and handling. The "Missed Report Supplementation" state is a special state corresponding to a warning event that the system automatically identified but failed to capture in time, but which has been confirmed as actually occurring by manual intervention or other channels. This state reflects the system's support for manual triggering mode and compensates for potential missed reports in purely automatic identification. The "Road Condition Awaiting Collaboration" state is an intermediate state reached by the finite state machine after the warning event is determined to be of road condition attribution type through causal inference. It indicates that the anomaly is mainly caused by sudden changes in road conditions (such as potholes, sudden fog, sudden braking by the vehicle in front, etc.), rather than vehicle malfunction or driver error. It is in a collaborative processing stage awaiting the push of work orders to the road administration management system, the broadcast of warnings to subsequent vehicles, and feedback from road administration.

[0044] It should be noted that in some other embodiments, more states can be added on the basis of the above states (such as "confirmed to be dispatched", "processing timed out", "second review", etc.) according to the needs of specific business scenarios, which will not be elaborated here.

[0045] In some embodiments, the finite state machine model defines two core elements for each pair of legal state transitions: first, "legal state triggering conditions," which are the specific events or signals that cause the state transition to occur, including automatic triggers (such as real-time data stream matching, timer timeout, and verification completion signals) and manual triggers (such as the reviewer clicking the confirmation button or the handler submitting a report); second, "state transition judgment rules," which are a series of logical conditions used to determine whether the state transition is allowed after the triggering conditions are met. These usually involve a comprehensive evaluation of the context data, time constraints, and business rules in the current state.

[0046] To avoid state conflicts caused by concurrent operations or abnormal processes, the finite state machine model introduces a state locking mechanism. When a warning event is undergoing a state transition operation (i.e., the transition transaction has started but not yet committed), a row-level lock can be applied to the event instance. During this period, any other operations (including automatically triggered new transition requests and manually initiated change operations) are blocked or fail quickly and return a retry prompt. Simultaneously, for warning events in the processing state, a business locking mechanism is supported. Once an event enters the processing state, its state is locked, preventing other processing personnel from repeatedly claiming it or performing concurrent state changes until the current processing flow is completed or the lock is released after a timeout. The state locking mechanism effectively ensures the atomicity, consistency, and isolation of the state transition of warning events.

[0047] S500: For the early warning events that have flowed to different discrete states, match the corresponding digital collaborative handling strategy and data feedback mechanism, and sequentially complete one or more of the following operations: early warning handling, result feedback, report generation, and strategy iteration.

[0048] In some embodiments, after an early warning event is driven to different states by a finite state machine model, based on the current state of each early warning event and its associated early warning type, priority level, business context, and other characteristics, a differentiated digital collaborative handling strategy and data feedback mechanism are dynamically matched, and one or more of the four stages of early warning handling, result feedback, report generation, and strategy iteration are completed in sequence, forming a complete closed loop from anomaly detection to strategy optimization.

[0049] In some embodiments, a collaborative handling strategy library is provided for different states and alert types. This strategy library supports dynamic configuration and hot loading, allowing the digital collaborative handling strategies in the strategy library to be adjusted at any time according to business needs. For example, the digital collaborative handling strategies matched for each state are as follows: 1. Pending Verification Status: Warning events in this status have not yet been verified. The appropriate digital collaborative handling strategy is "automated assisted review + low-weight manual sampling verification." Specifically, the multi-source data cross-validation, repeatability detection, and priority prediction in step S200 are automatically executed, and the verification results are pushed to the security auditor's workbench in the form of visual cards. For events with a high automatic verification credibility score (e.g., above 85%), they can be configured to automatically jump to the effective warning status without manual intervention; for events with scores in the critical range (e.g., 60%-85%), they can be prioritized and pushed to the auditor's queue for quick manual confirmation; for events with scores below the lower limit, they can be automatically marked as false alarms and discarded. High-cost handling resources (such as telephone notifications and on-site rescue) are not activated at this stage; only lightweight audit resources are consumed.

[0050] 2. Effective Warning Status: Warning events in this status have been confirmed as genuine anomalies, and the matching digital collaborative handling strategy can be "automatic dispatch + multi-level notification + handling time limit constraints." Specifically, preset handling role templates can be automatically matched according to the warning type and priority. For example, speeding warnings are prioritized to the driver's terminal (voice broadcast + pop-up reminder), and simultaneously copied to the fleet manager; temperature and humidity exceeding limits warnings are prioritized to the cold chain administrator and the receiving party at the destination warehouse; abnormal tire pressure warnings are simultaneously pushed to the driver and the nearest repair service point. A handling commitment time limit is pre-set for each dispatch task (e.g., emergency warnings must be accepted within 3 minutes and handled within 15 minutes), and countdown monitoring is initiated.

[0051] 3. In Progress Status: Events in this status are being processed. The corresponding digital collaborative processing strategy can be "progress tracking + timeout warning + collaborative communication." Specifically, the progress of the processing personnel can be monitored in real time, and changes in abnormal indicators (such as real-time temperature and humidity curves, and the deviation of vehicle location from the planned route) can be continuously obtained through a data feedback mechanism. If the processing is not completed within half of the promised time limit, a reminder is sent to the processing personnel; if the processing is still not completed after the time limit, the escalation rule is executed, automatically increasing the priority of the event and reporting it to the next higher level of management (such as from the team leader to the safety director), while simultaneously activating backup processing resources (such as dispatching other nearby vehicles or maintenance personnel for support). Furthermore, a collaborative communication channel can be opened for events in the "In Progress" status, supporting real-time messaging, location sharing, and image transmission among processing personnel, the monitoring center, and relevant third parties (such as the recipient, traffic police, and rescue agencies) within the same event context.

[0052] 4. Closed-Loop Status: Warning events in this status have been handled. The system's matched digital collaborative handling strategy can be "case closure and archiving + effectiveness evaluation + knowledge accumulation." Specifically, the complete lifecycle data of this warning event (from generation, verification, status transition to handling result) can be packaged and archived to form a standardized case closure report. Simultaneously, the handling effectiveness of the event can be evaluated, including indicators such as response time, handling time, whether the anomaly was completely eliminated, and whether there was a secondary recurrence. The evaluation results serve as input data for subsequent strategy iterations.

[0053] 5. False Alarm Invalidation Status: Warning events in this status are deemed invalid. The matching digital collaborative handling strategy can be "cause labeling + feature write-back + threshold calibration". Specifically, reviewers can be required to select the false alarm cause category (e.g., sensor momentary failure, data communication error, GPS drift, non-substantial deviation, etc.) when invalidating the warning, and write back the original data characteristics of the false alarm event (e.g., data value at the trigger time, fluctuation pattern, environmental context) to the feedback learning module of the warning strategy library. This module periodically analyzes the false alarm characteristics and automatically generates strategy optimization suggestions (e.g., suggesting relaxing a certain threshold, increasing the time window length, excluding triggers in specific scenarios, etc.). After manual confirmation, the strategy library can be updated.

[0054] 6. Missed Reporting Status: Warning events in this status require manual post-event recording. The matching digital collaborative handling strategy can be "cause tracing + strategy enhancement + responsibility definition." Specifically, the recording personnel can be required to provide a detailed explanation of the reasons for the missed report (such as automatic identification logic defects, sensor blind spots, incomplete rule coverage, etc.), and the feature data of the recorded event can be used as training samples input into the strategy optimization module. For missed reports due to missing strategies, the system automatically generates a draft new strategy for review; for missed reports due to sensor blind spots, it can generate equipment deployment optimization suggestions. At the same time, based on the actual impact of the recorded event, the management responsibilities of relevant responsible parties can be defined, forming a safety management assessment record.

[0055] In some embodiments, early warning handling operations include, but are not limited to, task assignment, notification confirmation, handling action recording, and collaborative interaction support. Task assignment refers to automatically pushing handling tasks to the terminals of designated personnel (web, mobile app, SMS, voice call, etc.) according to policy configuration. Task details include an overview of the early warning event, abnormal location / time, suggested handling measures, and promised time limits. Notification confirmation involves tracking the delivery status of notifications. For notifications not confirmed in a timely manner, a backup notification channel is automatically switched (e.g., if an APP push is unread, an SMS will be sent after 2 minutes; if an SMS is not confirmed, a voice call will be initiated after 5 minutes) to ensure that early warning information is not missed. Handling action recording allows personnel to record their handling actions in real time during task execution (e.g., "Speed ​​reduced to the speed limit," "Repair personnel notified to change tires," "Air conditioning temperature setting adjusted"). Uploading on-site photos, videos, or sensor recovery curves as evidence is supported. Collaborative interaction support provides a multi-role collaborative workspace for complex early warning events, supporting real-time communication, location marking, file sharing, and task handover between monitoring center dispatchers, on-site drivers, and external rescue forces within the same interface.

[0056] In some embodiments, result feedback may include data collection of handling results, structured entry of handling reports, closed-loop confirmation, and asynchronous callbacks and message broadcasts. Data collection of handling results refers to automatically collecting operational data after handling is completed through a data feedback mechanism, comparing whether abnormal indicators have returned to normal ranges. For example, for temperature and humidity exceeding limits warnings, the system continuously monitors temperature and humidity data for 5 minutes after handling to confirm that they remain stable within the threshold; for route deviation warnings, the system confirms that the vehicle has returned to the predetermined trajectory or that the dispatch center confirms a new valid route. Structured entry of handling reports refers to the handling conclusions submitted by the handling personnel (such as "abnormality eliminated," "partially alleviated but requires continued monitoring," "unresolved and escalated"), descriptions of handling measures, and supporting evidence, which are structured and parsed by the system and linked to the warning event record. Closed-loop confirmation refers to the automatic triggering of the state machine to transition the warning event from the handling state to the closed-loop state when the data feedback confirms that the abnormality has been eliminated and the handling report is complete, recording the closed-loop time, the closed-loop operator, and the basis for the closed-loop. If the abnormality has not been completely eliminated, the system refuses to close the loop and returns to the handling state, adding a prompt for unmet indicators. Asynchronous callback and message broadcasting refer to broadcasting a closed-loop notification to all relevant parties (such as the monitoring center, fleet manager, and associated third parties) that are concerned about the warning event after the result is returned, and updating the status display of the collaborative workspace.

[0057] In some embodiments, report generation refers to the automatic generation of multi-dimensional, configurable statistical reports based on structured data accumulated from early warning handling and result feedback, providing data support for management decisions and strategy optimization. The types of reports generated can be configured according to actual needs, and no specific limitations are imposed here.

[0058] In some embodiments, strategy iteration is a key step in forming a closed-loop management system using the method provided in this application. By re-injecting feedback data from historical early warning responses into the strategy configuration system, continuous optimization and adaptive evolution of the early warning strategy library are achieved. Strategy iteration may include strategy effect retrospective analysis, automatic adjustment of threshold parameters, addition and obsolescence of strategy rules, optimization of finite state machine rules, and strategy version management.

[0059] The strategy effectiveness backtracking analysis involves periodically analyzing all closed-loop warning events to evaluate the accuracy (number of effective warnings / total number of triggers), false negative rate (number of missed warnings to be supplemented / total number of actual anomalies), and average handling cost of each strategy in the current strategy library. Strategies with an accuracy rate below a set threshold (e.g., below 70%) can be optimized.

[0060] Automatic threshold parameter adjustment refers to the system's ability to automatically generate parameter optimization suggestions based on statistical analysis of historical data for strategies that support parameter optimization (such as overspeed threshold, temperature and humidity tolerance range, and dwell timeout window). For example, if the analysis finds that the current "overspeed threshold" setting of 90 km / h is frequently triggered in actual road conditions, but mostly as brief fluctuations rather than continuous speeding, it may suggest adding an additional condition of "duration ≥ 10 seconds," or suggesting raising the threshold to 95 km / h.

[0061] Adding and deprecating policy rules refers to extracting characteristic combinations of abnormal events to generate draft candidate policy rules for policy gaps reflected in missed reporting events. These draft rules include triggering conditions, suggested thresholds, suggested priorities, and suggested handling strategies. Once approved, the new policy is dynamically loaded into the policy library. Conversely, policy rules that have not been triggered for a long time or have been assessed as having no business value will be marked as "deprecated candidates" and removed from the policy library after confirmation.

[0062] Finite state machine rule optimization refers to the ability to propose optimization suggestions for state transition judgment rules, condition verification dimensions, and permission matrices based on statistical data of state transitions in actual operation (such as excessively high rejection rates for certain types of transitions or frequent timeout rollbacks). These suggestions may include adjusting commitment time limits, relaxing or tightening certain verification conditions, or adding or adjusting role permissions.

[0063] Strategy version management means that all strategy iteration operations are included in version management, recording the time, operator, content of the change, and basis for the change (related historical data analysis reports or audit records) for each strategy change. It supports strategy version rollback, ensuring a quick revert to a stable version if strategy optimization results are not as expected.

[0064] In some embodiments, the step of performing causal inference based on roadside units, weather radar, traffic flow detectors, and anonymous data of social vehicles on the same road segment as external intervention variables to perform root cause attribution and obtain causal inference results includes: Using the road segment where the logistics vehicle associated with the warning event is located as the analysis granularity, a Bayesian network structure is constructed for the road condition cause-effect graph; the road condition cause-effect graph contains various nodes used for anomaly attribution and directed edges between nodes; When the warning event is triggered, the current values ​​of the roadside unit, weather radar, traffic flow detector and anonymous data of social vehicles on the same road segment are used as evidence to input the road condition causal graph. Bayesian inference is used to calculate the first predicted probability that the latent variable node of the road condition change is true under the given evidence conditions, and the second predicted probability that the latent variable node of the vehicle mechanical failure is true. Based on the type of transported goods, the corresponding vulnerability factor coefficient α is read from the preset cargo parameter table, where 0 < α < 1; the first predicted probability is weighted and corrected using the vulnerability factor coefficient α to obtain the corrected road condition attribution probability P_road, and the second predicted probability is weighted and corrected to obtain the corrected vehicle attribution probability P_vehicle. If P_road is greater than or equal to the first attribution threshold, the anomaly is determined to be attributed to a sudden change in road conditions, and P_road is output as the posterior probability of the anomaly being attributed to the sudden change in road conditions. If P_vehicle is greater than or equal to the second attribution threshold, the anomaly is determined to be attributable to vehicle mechanical failure, and P_vehicle is output as the posterior probability that is usually attributable to vehicle mechanical failure. If P_road is less than the first attribution threshold and P_vehicle is less than the second attribution threshold, then it is determined that the attribution cannot be clearly defined.

[0065] In some embodiments, the above causal inference can be implemented using a Bayesian network structure to achieve root cause attribution analysis. When an early warning event is triggered, the current road segment of the logistics vehicle associated with the early warning event (e.g., using highway mileage intervals or urban road link IDs as granularity) is first used as the analysis unit to construct a Bayesian network structure for that road segment as a road condition causal graph.

[0066] In some embodiments, the traffic cause-effect graph includes various nodes for anomaly attribution and directed edges between nodes.

[0067] In some embodiments, the road condition cause-effect graph includes multiple nodes selected from the following: road condition state decision node, vehicle state decision node, external environment observation node, and vehicle response observation node; wherein, the road condition state decision node and the external environment observation node are connected by a directed connection, and the vehicle state decision node and the vehicle response observation node are connected by a directed connection. This scheme supports proactive intervention decision-making and is suitable for real-time early warning scenarios requiring rapid response.

[0068] In some embodiments, the road condition cause-effect graph includes cause-level nodes and result-level nodes; the cause-level nodes contain road condition-related external variable nodes and vehicle-related internal variable nodes; the result-level nodes contain abnormal event observation nodes; and there are directed connections between the cause-level nodes and the result-level nodes. This scheme has a clear causal hierarchy, strong interpretability, and facilitates post-event auditing and accountability.

[0069] In some embodiments, the traffic cause-effect graph comprises multiple strongly connected components, each containing a set of nodes with interdependent relationships; the strongly connected components are topologically connected. This scheme can handle complex coupling relationships between multiple variables and achieves high inference accuracy.

[0070] In some embodiments, the Bayesian network includes the following nodes: latent variable nodes for road condition changes, latent variable nodes for vehicle mechanical failures, nodes for observing abnormalities in the vehicle itself, nodes for road surface conditions reported by roadside units, nodes for weather conditions output by weather radar, nodes for average vehicle speed output by traffic flow detectors, and nodes for group deceleration behavior reflected by anonymous data from other vehicles on the same road segment. The latent variable nodes for road condition changes have directed edges connected to the roadside unit nodes, weather radar nodes, traffic flow detector nodes, and nodes for group deceleration behavior, indicating that these external observed variables are affected by the latent variable of road condition changes. The latent variable nodes for vehicle mechanical failures only have directed edges connected to the nodes for observing abnormalities in the vehicle itself, indicating the causal relationship between the vehicle's own failure and the observed data, and there are no direct directed edge connections between them and external environmental variables. This scheme introduces latent variables to abstract root causes that cannot be directly observed, resulting in a model with strong expressive power, high computational efficiency, and attribution results that can be directly used for state transitions in a finite state machine. The above network structure is based on domain prior knowledge and is dynamically optimized using a structure learning algorithm based on historical abnormal event data during system operation.

[0071] After the Bayesian network is constructed, a prior probability and conditional probability table are configured for each node. For example, the prior probability of a latent variable node representing a sudden road condition change can be set based on historical anomaly statistics for that road segment. For instance, if a road segment historically experiences a sudden road condition change on average once every 100 trips, then the prior probability P(sudden road condition change = true) = 0.01. The prior probability of a latent variable node representing a vehicle mechanical failure can be set based on the historical failure rate of that vehicle model. The conditional probability table is trained using maximum likelihood estimation or expectation-maximization algorithms based on labeled data from historical anomaly events, and is used to describe the probability distribution of the observed node taking specific values ​​under the given latent variable values.

[0072] When an early warning event is triggered, real-time external data is acquired as evidence input into the Bayesian network: road surface conditions (such as coded values ​​for dryness, wetness, water accumulation, and icing) are read from roadside units; weather conditions (such as visibility values ​​and rainfall intensity levels) are read from the weather radar interface; the average vehicle speed of the current road segment is read from the traffic flow detector; and group deceleration behavior characteristic values ​​(e.g., the proportion of social vehicles on the same road segment whose speed standard deviation exceeds a threshold in the past 5 minutes) are calculated based on anonymous GPS data of multiple social vehicles on the same road segment. The values ​​of these evidence nodes are fixed to the current observation values, and Bayesian inference algorithms (such as clustered tree propagation algorithm or variational inference) are used to calculate the first predicted probability that the latent variable node of road condition change is "true" under the given evidence conditions, and the second predicted probability that the latent variable node of vehicle mechanical failure is "true".

[0073] To differentiate the sensitivity differences of different cargo types to attribution results, the method also introduces a cargo fragility factor adjustment mechanism. Specifically, a cargo parameter table is pre-constructed, and a corresponding fragility factor coefficient α is configured for each type of transported cargo, where 0 < α < 1. For example, for precision instruments or fragile items, α = 0.8 is set, indicating that this type of cargo is more sensitive to sudden changes in road conditions; for cold chain cargo, α = 0.3 is set, indicating that abnormal temperature and humidity exceeding limits is more likely to be caused by a malfunction in the vehicle's refrigeration system; for ordinary express delivery cargo, α = 0.5 is set, indicating a neutral adjustment. Based on the cargo type associated with the current warning event, the corresponding α value is read from the cargo parameter table, and then the first and second predicted probabilities are weighted and corrected using this coefficient: the corrected road condition attribution probability P_road = α × first predicted probability, and the corrected vehicle attribution probability P_vehicle = (1-α) × second predicted probability.

[0074] Subsequently, the two corrected probabilities are compared with preset attribution thresholds: if P_road is greater than or equal to the first attribution threshold (e.g., 0.7), the root cause of the abnormal event is determined to be a sudden change in road conditions, and P_road is output as the posterior probability of attributing the anomaly to the sudden change in road conditions; if P_vehicle is greater than or equal to the second attribution threshold (e.g., 0.7), the root cause is determined to be a mechanical failure of the vehicle, and P_vehicle is output as the posterior probability of attributing the anomaly to the mechanical failure of the vehicle; if P_road is less than the first attribution threshold and P_vehicle is less than the second attribution threshold, it is determined that the cause cannot be clearly identified. This determination result is recorded as a causal inference result and stored in the early warning event record along with the automatic verification result for use when the finite state machine model drives state transitions.

[0075] Through the causal inference mechanism of Bayesian network reasoning and cargo vulnerability factor adjustment, this embodiment can accurately distinguish whether an abnormal event is caused by a sudden change in road conditions or by mechanical failure of the vehicle itself. It fundamentally solves the mixed problem of traditional solutions being unable to distinguish between "road-related" and "vehicle-related" causes. It provides a reliable root cause attribution basis for the subsequent state transition of early warning events (the road-related state transitions to the road administration process, or the state in progress transitions to the maintenance process). At the same time, it avoids erroneously assigning road condition problems to repair shops, significantly improving the accuracy and efficiency of early warning and handling.

[0076] In some embodiments, based on a pre-built finite state machine model, and combining the automatic verification results with the causal inference results, the warning event is driven to flow between predefined discrete states according to a clear transition path. Specifically, the finite state machine model defines the following core state transition logic: 1. Transition from the state to be verified to the state to be coordinated. When the automatic verification result confirms the existence of an anomaly, and the posterior probability P(anomaly|do(sudden road condition change)) attributing the anomaly to a sudden change in road conditions in the causal inference result is greater than the first preset threshold, the warning event transitions from the pending verification state to the road-cause coordination pending state. This transition signifies that the anomaly has been determined to be road-condition-driven (such as potholes, sudden fog, sudden braking by the vehicle in front, etc.), rather than a vehicle malfunction or driver error, and the event enters the road administration coordination and handling channel. The system automatically pushes a road condition repair work order to the road administration management system and broadcasts a speed reduction suggestion to vehicles on the same route within a subsequent 5 kilometers, avoiding incorrect dispatching of work orders to repair shops or driver alerts, and providing evidence of road liability for subsequent insurance determination.

[0077] 2. Transition from pending verification status or valid early warning status to processing status. When the automatic verification result confirms the existence of an anomaly, and the posterior probability P(anomaly|do(vehicle mechanical failure)) attributing the anomaly to a vehicle mechanical failure in the causal inference result is greater than the second preset threshold, the warning event can transition from the pending verification state (if it has not yet entered a valid warning stage) or the valid warning state (if it has been confirmed as a valid warning but has not yet been handled) to the handling state. This transition signifies that the anomaly has been attributed to a failure of the vehicle's own components (such as engine, braking system, abnormal tire pressure, etc.), and the event enters the repair or driver alarm handling process. The system automatically generates a repair work order and notifies the carrier or driver, while simultaneously initiating cargo status verification (if hazardous chemicals or cold chain are involved) to ensure that the faulty vehicle is promptly stopped for repair or driven to a designated inspection station.

[0078] 3. Transition from pending verification status to valid early warning status When the automatic verification result indicates that the anomaly truly exists, but the causal inference result shows that no clear attribution can be given, meaning that the warning event has been confirmed to have high credibility through cross-validation of multi-source data, the warning event jumps from the pending verification state to the valid warning state. This jump signifies that the warning event has completed the automated authenticity screening and has been confirmed as a genuine and valid anomaly, officially entering the pending processing queue, preparing for subsequent task dispatch and collaborative processing.

[0079] 4. Transition from pending verification status to false alarm invalidation status When the automatic verification result is a false alarm, meaning that cross-validation reveals serious contradictions between multiple data sources, or repetitive testing determines that the current warning event is highly similar to existing events within the window with no new evidence, or priority prediction indicates that the impact of the event is negligible, the warning event will transition from the pending verification state to the false alarm invalidation state. This transition directly terminates the invalid warning event, preventing it from consuming resources and interfering with the normal judgment of monitoring personnel in subsequent processing stages.

[0080] 5. Transition from effective early warning status to ongoing response status When an automatic or manual trigger command is received, the warning event transitions from a valid warning status to a status of being processed. This transition signifies that the warning event has moved from the "pending processing" stage to the "being processed" active stage, and the system accordingly initiates supporting mechanisms such as progress tracking, time limit monitoring, and collaborative communication.

[0081] 6. Transition from the processing state to the closed-loop state When the system receives a notification from an external system indicating that the vehicle anomaly has been resolved, the alert event transitions from the "in progress" state to the "closed-loop" state. External systems include, but are not limited to, driver mobile terminals, fleet management systems, vehicle sensor systems, and third-party rescue systems. For example, the resolution of the vehicle anomaly can be confirmed by real-time data feedback that the anomaly indicators remain consistently stable within a preset safety threshold. This transition signifies the normal end of the alert event's lifecycle, indicating that the anomaly has been effectively resolved.

[0082] 7. The transition from the "missed report supplementation" status to the "processing" status. When a manually recorded anomaly is confirmed and enters the handling process, the warning event transitions from the missed reporting status to the handling status. Specifically, operators can manually create a recorded event through the backend interface, filling in the time, location, type, and supporting evidence (such as surveillance footage retrieved afterward, customer complaint records, incident reports, etc.) and submitting it for confirmation. The warning event then enters the system in the missed reporting status. If, after review, it is determined that the recorded event requires initiating the handling process (for example, although the automatic warning system did not capture it in time, the anomaly has indeed occurred and may have subsequent impacts), and a manually initiated handling instruction is received, the warning event directly transitions from the missed reporting status to the handling status, incorporating it into the same collaborative handling and closed-loop management process as automatically triggered events. This transition compensates for the missed reporting deficiency of the purely automatic warning mechanism, demonstrating the integrated support of this method for the manual triggering mode.

[0083] 8. Final state locking mechanism The finite state machine model predefines the closed-loop state and the false alarm invalidation state as the two final states (i.e., termination states) of an early warning event. Once an early warning event transitions to either of these final states, the model restricts it from transitioning to any other state. In other words, any request for a further state change to a final state event is unconditionally rejected by the system. The purpose of this final state locking mechanism is twofold: firstly, to ensure that each early warning event has a clear termination identifier, preventing accidental reactivation of closed events and the resulting process chaos; and secondly, to provide clear and unambiguous state boundaries for subsequent state-based statistical analysis (such as closed-loop rate and false alarm rate), ensuring the accuracy and consistency of report data.

[0084] It should be noted that automatic triggering commands and manual triggering commands are the two core triggering sources that drive the early warning event to transition from the effective early warning state to the handling state. In this embodiment, the independent operation and collaborative work of the two triggering modes are supported, and the optimal triggering method is dynamically selected based on the actual business scenario.

[0085] In some embodiments, the automatic trigger command is automatically generated by polling and collecting real-time data of the logistics vehicles at a preset period and matching it with an early warning strategy. Specifically, an independent polling scheduling engine is deployed in the system background. This engine traverses all logistics vehicles in transit according to a configurable time period, collecting multi-source real-time operating data of each vehicle, including but not limited to GPS positioning, driving speed, engine status, temperature and humidity sensor readings, tire pressure monitoring data, etc. For each vehicle, the scheduling engine matches and calculates the collected real-time data with each strategy in a preset configurable early warning strategy library. When a data item or combination of data items is detected to meet the triggering condition defined by an early warning strategy, a corresponding automatic trigger command is generated. This command serves as a legitimate trigger signal for transitioning from an effective early warning state to a handling state, driving the early warning event into the handling process without any manual intervention. The advantage of the automatic trigger mode is its fast response speed and strong real-time performance, making it suitable for routine abnormal scenarios with clear rules and distinct characteristics.

[0086] In some embodiments, manual triggering commands are input by the user through a terminal. Specifically, graphical operation terminals can be provided for roles such as monitoring center operators, fleet managers, and safety administrators. After logging into the system and having the corresponding operating permissions, users can generate manual triggering commands through the terminal interface by performing the following operations: browsing the list of warning events currently in a valid warning state, selecting a specific event, and clicking the "Dispatch" or "Start Processing" button; or, while viewing the warning event details page, triggering the generation of commands by clicking the "Immediate Processing" control; in addition, for warning events that the system automatically identifies as having low confidence but are confirmed as genuine by manual review, users can also actively initiate manual triggering commands. The advantage of the manual triggering mode is that it can integrate human experience and judgment, and is suitable for complex abnormal scenarios with limited automatic identification capabilities, ambiguous boundary conditions, or those requiring special handling.

[0087] To avoid command conflicts and ensure the authority of manual intervention, this application embodiment sets the priority of manually triggered commands higher than that of automatically triggered commands. Specifically, when the same warning event receives both automatically triggered and manually triggered commands within a similar time window, the manually triggered command is responded to first according to the priority rule, and the manually triggered command serves as the final basis for driving the state transition. If the manually triggered command arrives first and successfully transitions the warning event from the valid warning state to the handling state, subsequent automatically triggered commands are automatically ignored by the system, and the repeated state transition operation is not performed. A "command ignored" log is recorded for future reference.

[0088] In some embodiments, automatic authenticity verification further includes a deep verification mechanism based on a spatiotemporal consistency verification model. This mechanism effectively identifies spatiotemporal inconsistencies caused by positioning drift, clock asynchrony, or data forgery by fusing multi-source time series data, and can trigger automatic adjustment of the on-board device's clock drift warning strategy.

[0089] In practical implementation, firstly, the three key time-series data from the collected multi-source operational data—vehicle positioning data, on-board diagnostic timestamp data, and vehicle passage time data collected by roadside units—can be aligned. Since these three types of data come from different sources, have different sampling frequencies, and may have different time bases, a unified time base can be used to align and interpolate the timestamps, forming a multi-source data sequence aligned in the time dimension. Secondly, based on the aligned data, a vehicle operational time-series feature vector is constructed. This feature vector is a multi-dimensional time series, and each time slice includes, but is not limited to, the following feature dimensions: GPS positioning coordinates (longitude and latitude), instantaneous speed, driving direction angle, vehicle speed and engine speed recorded by OBD, driving distance between two consecutive frames, and the time and location of passing through roadside units. This feature vector comprehensively depicts the vehicle's motion state and spatial position changes within a continuous time window.

[0090] After constructing the vehicle's runtime sequence feature vector, it is used as the actual observed sequence and compared with the expected sequence in a preset normal driving mode library for similarity. The preset normal driving mode library is constructed through machine learning clustering analysis of massive historical normal transportation task data, storing typical normal driving time sequence feature templates for different vehicle types, road sections, time periods, and weather conditions. In practice, a dynamic time warping algorithm can be used to calculate the distance between the actual observed sequence and the expected sequence. Dynamic time warping is a similarity measurement method that allows for non-linear alignment of the time axis, effectively handling situations where two time series have local scaling, offset, or phase differences in the time dimension. It is particularly suitable for scenarios where logistics vehicles experience non-rigid deformations between their actual driving trajectories and theoretical patterns due to acceleration / deceleration, temporary stops, traffic congestion, etc. The smaller the dynamic time warping distance, the closer the actual driving mode is to the normal mode; the larger the distance, the more significant the deviation. When the calculated dynamic time warping distance exceeds a preset threshold, a temporal-spatial inconsistency anomaly is identified. Once the anomaly is determined to exist, the automatic verification result of the corresponding early warning event can be marked as "spatiotemporal inconsistency risk" and used as an important reference for transitioning from the pending verification state to the valid early warning state or the false alarm invalidation state.

[0091] In some embodiments, the method further expands the application boundaries of the spatiotemporal consistency verification model, using it not only for verifying the authenticity of a single warning event but also as an analytical tool for continuously monitoring the health status of onboard equipment. Specifically, it continuously tracks the dynamic time warping distance output by each vehicle over multiple consecutive verification cycles and monitors and analyzes its changing trends. When the dynamic time warping distance output by the spatiotemporal consistency verification model shows a monotonically increasing trend over three consecutive verification cycles, it is determined that the onboard equipment of the logistics vehicle has a problem of accumulated and aggravated clock drift. Clock drift refers to the gradual and continuously increasing deviation between the vehicle's local clock and the standard time due to insufficient crystal oscillator accuracy, temperature changes, or battery aging. As the drift accumulates, the inconsistency between the OBD timestamp data and GPS time, as well as the RSU vehicle passage time, gradually intensifies, reflected in the gradual increase of the dynamic time warping distance.

[0092] Upon detecting the aforementioned monotonically increasing trend, the activation status of the corresponding vehicle's onboard device clock drift warning strategy in the configurable warning strategy library can be switched. For example, this warning strategy initially defaults to "disabled" or "silent monitoring" mode to avoid generating unnecessary alarms within the normal clock error range. When it is confirmed that the clock drift trend continues to worsen (monotonically increasing for three consecutive cycles, and the dynamic time warping distance of the latest cycle is close to or exceeds the abnormal threshold), the activation status of the strategy is automatically switched to "enabled" or "active warning" mode. After the strategy is enabled, a corresponding onboard device clock drift warning event will be generated according to the strategy rules, notifying maintenance personnel to perform clock calibration or equipment maintenance on the vehicle's onboard terminal.

[0093] Simultaneously, the state switching operation for this strategy is automatically recorded in the log to ensure the traceability of this linkage operation. The log record includes the following fields: trigger time, vehicle identifier, dynamic time-normalized distance value sequence for three consecutive verification cycles, monotonically increasing trend determination criteria, state before strategy switching, state after strategy switching, and operation trigger source (system automatic trigger identifier). This log can serve as an audit basis for subsequent equipment maintenance decisions and strategy optimization analysis.

[0094] In some embodiments, the method further includes performing permission verification, condition verification, and log persistence for each state transition.

[0095] To prevent unauthorized state change operations, this embodiment employs strict permission verification for every state transition request. Permission verification can be based on a combined determination of user role, operation type, and current state. In some embodiments, multiple operation roles can be predefined, including but not limited to: system administrator (with full state transition permissions), security auditor (able to transition between pending verification state and valid warning state, or false alarm invalidation state), handling executor (able to transition between handling state and closed-loop state), and read-only observer (only able to view states, without transition permissions), etc. When a role initiates a state transition request, the pre-defined permission matrix is ​​first queried to verify whether the role is authorized to execute the target transition operation in the current state. If permissions are insufficient, the transition request is rejected and a violation log is recorded. This mechanism effectively avoids unauthorized operations and state chaos.

[0096] Condition verification is the core verification step before state transition execution. It is independent of and complementary to permission verification. Permission verification solves the problem of "who can operate," while condition verification solves the problem of "whether the operation is allowed." In some embodiments, a multi-dimensional set of verification conditions is pre-defined for each pair of valid state transitions. The state transition is only allowed to execute when all verification conditions are met. In specific implementations, condition verification includes, but is not limited to, the following dimensions: 1. Time Window Verification: Checks whether the current time meets the time constraints allowed for state transition. For example, when transitioning from a pending verification state to a valid warning state, it can be verified whether the duration of the warning event has not exceeded the maximum pending verification time window (e.g., 30 minutes). If it exceeds the time limit, the transition is prohibited and a timeout alarm is triggered. When transitioning from a valid warning state to a processing state, it verifies whether the time from the state entry time to the current time has not exceeded the maximum dispatch delay time (e.g., dispatch must be made within 5 minutes, otherwise the escalation mechanism is triggered).

[0097] 2. Data Integrity Verification: Check whether the key data required for the execution status transition has been completely collected and is valid. For example, when transitioning from the pending verification status to the false alarm invalidation status, verify whether the automatic verification results contain detailed evidence data for cross-validation (such as multi-source data comparison records and repeatability test reports). If the evidence is missing, refuse the transition and request supplementation. When transitioning from the handling status to the closed-loop status, verify whether the data returned from the handling results is complete (including handling time, handling measures, proof of anomaly elimination, on-site images or sensor recovery data, etc.). If any required field is missing, the transition will be rejected.

[0098] 3. Business Logic Consistency Verification: Check whether the status transition conforms to the logical constraints of the business scenario. For example, when transitioning from an effective warning status to a pending status, it can be verified whether the current logistics vehicle has other similar warning events that have not yet been closed. If so, the merged handling logic is triggered instead of a direct transition, to avoid the same logistics vehicle being repeatedly dispatched due to the same type of anomaly. When transitioning from a pending status to an effective warning status (rollback), it is verified whether the number of rollbacks has exceeded the preset maximum number of rollbacks (e.g., a maximum of 2 rollbacks are allowed). If it exceeds this limit, further rollbacks are prohibited and the process is forcibly escalated to administrator handling.

[0099] 4. Resource Availability Verification: Check whether the system resources or external services required after the status transition are available. For example, when transitioning from an effective alert status to a pending status, it can verify whether the target personnel's terminal device is online and whether the notification channels (push, SMS, voice) are available; if not, the transition is suspended and automatically switched to alternative personnel or alternative notification channels until the resource availability conditions are met.

[0100] 5. Debouncing and Concurrency Prevention Verification: To prevent unexpected multiple state transitions for the same alert event within a very short period due to system jitter or concurrent requests, a debouncing verification mechanism can be introduced. Specifically, for the same alert event, the timestamp of its most recent state transition can be recorded. If the time interval between the current transition request and the most recent transition is less than a preset threshold (e.g., 500 milliseconds), and the transition paths are the same or constitute a loop transition, it is determined to be abnormal jitter, and this transition request is rejected and recorded in the debouncing interception log. Simultaneously, for multiple concurrent transition requests targeting the same alert event, only the request that first passes the condition verification is allowed to execute; the remaining requests automatically become invalid due to the changed state.

[0101] 6. Dependency Status Verification: Check whether other related objects (such as parent tasks, associated vehicles, and policy instances) on which the alert event depends are in a state that allows this jump. For example, when an alert event is derived from a parent task (such as a transport work order), before jumping from the processing state to the closed state, it can be verified whether the parent task is not in the "paused" or "cancelled" state. If the parent task has been cancelled, this alert event should jump directly to the "false alarm invalidated" state instead of the "closed state".

[0102] In some embodiments, the execution order of conditional verifications can follow the principle of lightweight verification preceding heavyweight verification: for example, first perform time window verification and debouncing verification (low computational overhead, can quickly filter invalid requests), then perform data integrity verification and dependency status verification (requires querying related data), and finally perform business logic consistency verification and resource availability verification (may involve external calls). If any verification condition is not met, the redirection process can be terminated immediately, returning a clear rejection reason code and verification failure details, allowing the upper-level caller or operator to know the specific reason for the blockage and take appropriate action. All verification processes are recorded in the log persistence system, forming a complete redirection audit trail.

[0103] To ensure the traceability and auditability of the entire process of state transitions in early warning events, this application embodiment logs each successfully executed state transition. In some embodiments, log logging includes storing the event ID, timestamp, source state, target state, triggering conditions, operating role, and execution action of each state transition into a database, forming a state transition audit log. Specifically, after each successful state transition, the log logging operation is performed simultaneously with the state change transaction commit, ensuring the atomicity of log recording and state change. Each audit log entry contains at least the following fields: unique identifier of the early warning event (event ID), timestamp of the transition, source state before the transition, target state after the transition, description of the conditions triggering the transition, role identifier of the executing operation, and description of the specific execution action. All audit log entries are written to a dedicated audit database table or blockchain storage system, using an append-only, non-modifiable storage strategy; no subsequent operation may delete or overwrite the written logs. This log logging provides complete and reliable data support for post-event traceability, fault diagnosis, compliance auditing, process optimization, and dispute resolution.

[0104] In some embodiments, the configurable warning strategy library can be configured to support users in editing, adding, querying, enabling, and disabling all-scenario anomaly warning strategies for logistics vehicles, providing comprehensive configuration management. Unlike traditional systems where warning rules are hard-coded into program logic, making modification difficult and response times long, this application decouples warning strategies from the core system code, storing and managing them independently in a structured configuration format. This provides users with a visual strategy management interface, supporting the aforementioned comprehensive configuration operations.

[0105] Meanwhile, the configurable early warning strategy library is pre-configured with early warning thresholds, anomaly judgment logic, and early warning handling priorities for different business scenarios. Among them, the preset configurations for different business scenarios can be continuously optimized and adjusted based on actual operational data.

[0106] Digital collaborative handling strategies and data feedback mechanisms are key links connecting the status flow of early warning events with actual business handling operations. In this embodiment, differentiated handling actions are triggered for different statuses, and data association and closed-loop management are established throughout the entire process to ensure that every link of the early warning event from its generation to its termination is effectively responded to and fully recorded.

[0107] In some embodiments, for warning events that have progressed to the "in progress" status, the notification service is automatically invoked to send warning information to relevant personnel and a collaborative handling work order is automatically generated. Specifically, when a warning event transitions from the "valid warning" status to the "in progress" status, the notification service's API is immediately triggered. The notification service is a unified message distribution middleware that supports the integration and intelligent routing of multiple notification channels, including but not limited to: mobile app push notifications, SMS gateways, voice calls, instant messaging tools, and web browser notifications. Based on the type and priority level of the warning event and the preset notification object templates in the policy library, the recipient list, notification channel combination, and notification content template are dynamically determined. Simultaneously with sending the notification, a collaborative handling work order is automatically generated. This work order can be a structured digital task document, containing at least the following fields: work order number, warning event type, time and location of the anomaly, associated vehicle information, detailed description of the anomaly, suggested handling measures, committed handling timeframe, current work order status, handling personnel, and handling records. After generation, the work order is automatically pushed to the workbench of the corresponding handling personnel and supports operations such as work order claiming, assignment, collaboration, and escalation. By linking automatic notifications with work order generation, a seamless connection between "discovery" and "response" of early warning events is achieved, significantly shortening the delay in abnormal response.

[0108] In some embodiments, for warning events that have transitioned to a closed-loop state, the front-end page is automatically refreshed in real time, and the warning report data is updated synchronously. Specifically, when a warning event transitions from the "in progress" state to the closed-loop state, a status change event is sent to the back-end service via the message bus. After this event is captured by the data subscription module of the front-end page, the following synchronization operations can be triggered: For front-end pages such as the warning monitoring dashboard, real-time warning list, and vehicle status map currently being viewed by monitored personnel or managers, partial or full data refresh is automatically performed, updating the status of the just-closed warning event to "closed," and providing visual feedback on the interface with a differentiated style, enabling operators to perceive the completion status of the event handling in real time and avoiding repeated interventions on closed-loop events. At the same time, the warning report data is updated synchronously. The report engine updates the aggregated indicators in various statistical reports in real time based on the detailed information of the closed-loop event, including but not limited to: daily closure rate, average response time, average handling time, distribution of the number of closed loops for each type of warning, and closure performance of each fleet / driver.

[0109] In some embodiments, a unique identifier is established for each early warning message, linking it to data from the entire process of anomaly occurrence, early warning push, manual handling, anomaly elimination, and result verification, forming a closed-loop control system encompassing anomaly identification, status verification, early warning triggering, collaborative handling, result feedback, and data accumulation. Specifically, each early warning event is assigned a globally unique identifier upon generation, which persists throughout the event's lifecycle within the system. Using this unique identifier as the primary key, the system links and stores data generated during anomaly occurrence, early warning push, manual handling, anomaly elimination, and result verification into a unified data model.

[0110] It is important to note that strategy iteration is a crucial step in forming a closed-loop management system for early warning monitoring. By periodically performing quantitative analysis on historical early warning data and dynamically optimizing threshold parameters and judgment rules in the early warning strategy library based on the analysis results, the system achieves continuous adaptive evolution. In specific implementation, the early warning accuracy, false alarm rate, and missed alarm rate are first periodically calculated for different business scenarios. The early warning accuracy rate refers to the ratio of valid early warning events to all confirmed real events, or the proportion of verified anomalies among closed-loop events. The false alarm rate refers to the ratio of early warning events deemed false alarms and invalidated to all verified events. The missed alarm rate refers to the ratio of manually recorded events entering the system through the missed alarm reporting status to the total number of actual abnormal events (i.e., the sum of valid early warning events and missed alarm reporting events). Secondly, the early warning threshold is adjusted based on the early warning accuracy rate, and the early warning judgment rules are updated based on the false alarm rate and missed alarm rate. Specifically, when the accuracy rate of a certain early warning strategy in a specific business scenario falls below a preset lower threshold, it indicates that the strategy has generated too many false alarm events. Analyzing the distribution characteristics of false alarms identifies the main data fluctuation patterns leading to them, and automatically generating threshold adjustment suggestions based on a pre-set adjustment algorithm. When the false alarm rate of a certain strategy remains consistently high, it indicates a systemic flaw in the strategy's judgment rules. In this case, feature clustering analysis of false alarms can be used to identify common patterns and update the judgment rules accordingly. When a certain type of missed reporting event occurs frequently, it indicates a coverage blind spot in the current early warning strategy. In this case, feature vectors of missed reporting events can be extracted and compared with existing strategy rules to identify specific missing links in the strategy, and judgment rules can be added or modified accordingly.

[0111] To further enhance the intelligence level and context awareness of early warning event handling, in some embodiments, the method further includes constructing a dynamic spatiotemporal correlation graph of early warning events and linking the graph with a finite state machine model to achieve dynamic adjustment of early warning event priority and state transition rules based on correlation relationships.

[0112] In some embodiments, a dynamic spatiotemporal correlation graph is constructed using early warning events as basic nodes. This graph is a weighted graph structure that evolves dynamically over time, where each early warning event corresponds to a node in the graph; the edges between nodes represent a quantifiable correlation between two early warning events, and the weight values ​​of the edges comprehensively reflect the correlation strength between events in three dimensions: time, space, and business attributes. Specifically, temporal correlation can be calculated based on the time difference between the occurrence of two early warning events; the smaller the time difference, the stronger the temporal correlation. Spatial trajectory overlap can be calculated based on the trajectory similarity of the occurrence locations of two early warning events. Business attribute similarity can be calculated based on the similarity of the business attribute fields of two early warning events. Finally, the edge weights are obtained by weighted fusion calculation of the scores of the above three dimensions, and the sum of the weights, after normalization, forms a comprehensive correlation score within the interval [0,1].

[0113] When a finite state machine model drives an early warning event to a valid early warning state, a graph query operation of the dynamic spatiotemporal correlation graph is first triggered to identify a set of associated early warning events that meet a preset correlation threshold. Specifically, using the current early warning event node as the query center, a breadth-first or weighted nearest neighbor search is performed in the graph, traversing all neighboring nodes connected by edges, and filtering out associated events whose edge weights exceed the preset correlation threshold. The query operation supports configurable depth, such as querying only first-degree correlations or extending to second-degree correlations. The query result set contains all associated early warning events that meet the threshold and can be sorted in descending order of correlation score.

[0114] Secondly, based on the current state of each associated early warning event in the identified set of associated early warning events, the processing priority of the current early warning event and the state transition judgment rules in the finite state machine model are dynamically adjusted. The specific adjustment logic is as follows: If the set of related early warning events contains similar events in a "processing" or "closed-loop" state, then the current early warning event is determined to have a significant spatiotemporal or business correlation with one or more similar events that are being processed or have been closed. In this case, the priority of the current early warning event can be increased by one level based on the original automatic verification result (e.g., from "Medium" to "High," or from "High" to "Urgent"). If the highest level has already been reached, it should be maintained. After increasing the priority, the early warning event will be moved forward in the dispatch queue and trigger a more urgent combination of notification channels. The logic behind this adjustment is that the existence of similar events that are being processed or have been closed indicates that the anomaly is not an isolated case and may indicate a systemic problem or regional risk, requiring higher attention and priority handling. Simultaneously, the waiting time window in the status transition judgment rule for similar events can be shortened by a preset percentage. The logic behind this adjustment is that when similar anomalies occur multiple times, it indicates that the scenario is urgent or recurring, and the status transition speed should be accelerated to improve overall response efficiency.

[0115] If the set of associated early warning events contains homogeneous events in a false alarm invalidation state, then the current early warning event is determined to have highly similar spatiotemporal or business characteristics to one or more events that have already been determined to be false alarms. In this case, the confidence score of the early warning event can be lowered, and the priority prediction level in the automatic verification result can be downgraded by one level.

[0116] By constructing the aforementioned dynamic spatiotemporal correlation map and its linkage mechanism with the finite state machine model, this method achieves the following technical effects: First, it expands isolated event identification into chain-like cognition based on contextual correlation, enabling the system to perceive the relationships between events; Second, it dynamically adjusts the handling strategy of the current event based on the handling results of historical related events (in progress, closed loop, or false alarm invalidation), realizing the reuse of experience and the transfer of knowledge; Third, it effectively avoids the repeated waste of handling resources in known false alarm scenarios, while also ensuring rapid response and resource allocation in scenarios of group anomalies or regional risks, significantly improving the intelligent decision-making level of the early warning and monitoring system.

[0117] In some embodiments, a strategy pre-simulation and immune mechanism based on a road segment-level hybrid traffic flow twin is also constructed. Specifically, instead of using individual vehicles as twin units, road segments (such as chainage intervals) and service areas and toll station nodes are used as twin units to construct a road segment-level hybrid traffic flow twin. Three types of intelligent agents evolve simultaneously in this twin: logistics fleet intelligent agents (following the state transition rules of a real finite state machine), social vehicle intelligent agents (simulating the randomness of human driving based on a GAN generator), and roadside facility intelligent agents (simulating constraints such as RSU, weather stations, and service area capacity).

[0118] During strategy iteration, the new early warning strategy is rehearsed in a virtual twin, and a strategy contagion model is used to assess the strategy risk: when a new early warning strategy causes a vehicle to trigger an early warning and enter a handling state, that vehicle is considered a "source of infection." The system calculates the abnormal transmission coefficient R0 (i.e., the average number of subsequent abnormal vehicles triggered by one abnormal vehicle), and simultaneously monitors whether the number of similar abnormal vehicles in the service area exceeds the capacity threshold (e.g., the upper limit of hazardous chemical parking spaces) within a preset time. If R0 > 1, or the number of similar abnormal vehicles in the service area exceeds the limit, the strategy is determined to be a high-risk strategy for contagion, and the strategy is automatically rejected or a diversion correction rule is required.

[0119] For mandatory inspection strategies for cold chain or hazardous chemicals, the twin will also rehearse the spatiotemporal conflicts caused by mandatory parking: if the rehearsal shows that executing "mandatory unpacking inspection of all cold chain vehicles passing through service area A" at a certain time period will cause multiple cold chain vehicles to be unable to find compliant parking spaces within the specified time limit (for example, the refrigeration unit cannot be idled for a long time), the system will automatically delay the handling status of some vehicles to the next service area with cold chain qualifications, or trigger a collaborative work order for roadside mobile inspection points.

[0120] Furthermore, for specific road sections with historically recurring anomalies (such as brake overheating on long downhill sections), the system injects a "digital vaccine" into the road segment within the twin: simulating continuous braking under extreme heavy load conditions, testing whether the finite state machine model can correctly attribute the anomaly to road conditions rather than vehicle malfunction, and whether the handling path is "roadside cooling pool guidance" rather than "maintenance dispatch." Strategies that pass the vaccine test obtain an immunity certificate for the road segment, while strategies that fail are prohibited from being effective on that segment. Through the aforementioned twin pre-simulation and infectious disease immunity mechanism, this invention can proactively identify and block the risks of strategy-induced anomaly aggregation, phantom braking propagation, and service area clustering from a road segment-level macro-traffic flow perspective, significantly improving the safety of strategy implementation and the overall operational efficiency of the logistics network.

[0121] In some embodiments, the method further includes constructing a strategy pre-simulation and immune mechanism based on a road segment-level hybrid traffic flow twin. Unlike traditional single-vehicle-level digital twins, this application constructs a road segment-level hybrid traffic flow twin using highway segments (e.g., divided by station intervals) and service areas and toll station nodes as twin units. This twin simultaneously evolves three types of intelligent agents in the digital space: logistics fleet intelligent agents, social vehicle intelligent agents, and roadside facility intelligent agents.

[0122] Specifically, the logistics fleet intelligent agent strictly follows the state transition rules defined by the aforementioned finite state machine model in this application, simulating the transition behavior of logistics vehicles between seven states: pending verification, effective warning, in progress, and closed loop. The social vehicle intelligent agent simulates the randomness and uncertainty of human driving behavior based on a generator using a generative adversarial network (GAN), including non-deterministic behaviors such as random acceleration / deceleration, lane changing, and temporary stops, making the twin environment closer to real traffic flow. The roadside facility intelligent agent simulates the data collection and broadcasting functions of roadside units (RSUs), weather status output from weather stations, and constraints such as the remaining parking space capacity of service areas. The three types of intelligent agents evolve in parallel within the same digital space, forming a high-fidelity mapping of real logistics traffic flow.

[0123] In some embodiments, during strategy iteration, the new early warning strategy is rehearsed in the road segment-level mixed traffic flow twin, and the strategy risk is assessed using a strategy contagion model. Specifically, when the new early warning strategy causes any vehicle in the twin to trigger an early warning and enter a handling state, that vehicle is considered a "source of infection." The anomaly propagation coefficient R0 is calculated, which is defined as the average number of subsequent anomaly vehicles triggered by a single anomaly vehicle within a complete time window (e.g., 30 minutes) of twin operation. The formula for calculating R0 is: R0 = (total number of subsequent anomaly vehicles) / (initial number of anomaly vehicles), where subsequent anomaly vehicles refer to new early warning events directly or indirectly triggered by the early warning behavior of the initial anomaly vehicle.

[0124] Simultaneously, the system monitors whether the number of similar abnormal vehicles within the service area exceeds a preset capacity threshold within a given timeframe. For example, service areas typically have a dedicated maximum number of parking spaces (e.g., 5 spaces) for hazardous materials transport vehicles. When the twin simulation shows that the number of hazardous materials vehicles in a handling state within the same service area exceeds this capacity threshold within 15 minutes, a risk of service area congestion is identified.

[0125] If the abnormal propagation coefficient R0 > 1, or if the number of vehicles of the same type with abnormality in the service area exceeds the capacity threshold within a preset time, the new early warning strategy is determined to be a high-risk strategy for infectivity. The system automatically rejects the strategy and may optionally trigger diversion correction rules (e.g., suggesting that some vehicles be guided to adjacent service areas), and records the rejection result and risk analysis report in the strategy iteration log.

[0126] In some embodiments, for mandatory inspection strategies for cold chain or hazardous chemicals, parking conflicts caused by intervention of the mandatory inspection strategy are simulated in a virtual twin. For example, assuming that a strategy of "mandatory unpacking and inspection of all cold chain vehicles passing through service area A" is implemented during a certain period, all cold chain vehicles arriving at service area A within the next hour are simulated in the virtual twin. The estimated arrival time, estimated dwell time (including queuing time and inspection execution time) of each vehicle are calculated one by one, as well as whether subsequent cold chain vehicles can find compliant parking spaces within the specified temperature maintenance time limit.

[0127] If the simulation results show that implementing this mandatory inspection strategy will cause the subsequent three refrigerated trucks to be unable to find compliant parking spaces within the specified time limit (e.g., the maximum continuous operation time of the refrigeration compressor is 45 minutes) due to excessive waiting time, the conflict mitigation mechanism will be automatically triggered: the status of some vehicles will be delayed until the next service area with refrigeration qualifications (e.g., guiding the third and subsequent refrigerated trucks to service area B, which is 20 kilometers away from service area A), or a roadside mobile checkpoint coordination work order will be triggered (e.g., dispatching a mobile inspection vehicle to perform a rapid inspection at the service area exit). The execution result of this mitigation strategy will also be recorded and fed back to the strategy optimization module.

[0128] In some embodiments, for a specific road segment that has historically experienced a recurring anomaly, a "digital vaccine" is injected into the twin for that road segment. Specifically, a digital vaccine is a simulation injection mechanism for extreme conditions, used to test the attribution correctness and the effectiveness of the handling path of the finite state machine model under extreme conditions. For example, for a long downhill road segment that has historically experienced a recurring brake overheating anomaly, the system injects a digital vaccine into the twin for that road segment: simulating a continuous braking scenario under extreme heavy load conditions (vehicle total weight exceeding the rated load by 20%), setting the ambient temperature to high temperature (35°C), and simulating a continuous 5-kilometer downhill road segment.

[0129] After the vaccine is injected, the twin is run and the attribution results of the finite state machine model are monitored. If the model correctly attributes the anomaly to road conditions (thermal management stress caused by a long downhill slope) rather than vehicle malfunction, and the response path is "guided to the roadside cooling pool" rather than "dispatched to a repair shop," then the strategy passes the vaccine test and obtains an "immunity certificate" for that road segment. The immunity certificate is a data-structured authentication identifier that records fields such as strategy number, road segment identifier, test time, test result (pass / fail), attribution accuracy, and compliance of the response path.

[0130] If a strategy fails the vaccine test (e.g., the model incorrectly attributes brake overheating to a vehicle braking system malfunction and generates an incorrect repair order), the strategy is prohibited from being used on that road segment. The system automatically generates strategy optimization suggestions and stores the failed test records in the immunization certificate database for reference during subsequent strategy iterations. Through the aforementioned twin simulation and infectious disease immunization mechanism, this application can identify and block the risks of abnormal aggregation, phantom braking, and service area clustering caused by strategies from the perspective of macro-traffic flow at the road segment level, significantly improving the safety of new early warning strategies and the overall operational efficiency of the logistics network.

[0131] Example 2 Corresponding to Embodiment 1 above, this application also provides a smart logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning system. This system is used to implement the smart logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method provided in any of Embodiment 1. In this embodiment, content that is the same as or similar to that in Embodiment 1 above can be referred to the above description and will not be repeated hereafter. (Refer to...) Figure 2 As shown, the system includes: Multi-source data acquisition module 10 is used to collect multi-source operational data during the transportation process of logistics vehicles; The early warning strategy configuration module 20 is used to build and configure a configurable early warning strategy library; The abnormal event identification module 30 is connected to the multi-source data acquisition module and the early warning strategy configuration module. It is used to identify abnormal vehicle events based on the multi-source operating data and in combination with a preset configurable early warning strategy library, and generate early warning events. Automatic verification module 40 is connected to the abnormal event identification module and is used to automatically verify the authenticity of the warning event, record the automatic verification result, and initialize the warning event to a state to be verified. The causal inference module 50 is used to perform causal inference based on roadside units, weather radar, traffic flow detectors and anonymous data of social vehicles on the same road segment as external intervention variables, to perform root cause attribution and obtain causal inference results. The causal inference includes: performing causal inference based on a road condition causal graph with road segment as the granularity, to determine the posterior probability that the anomaly is attributed to a sudden change in road condition or a mechanical failure of the vehicle, and introducing a cargo vulnerability factor as a moderating variable in the road condition causal graph according to the type of transported goods. Finite state machine model module 60 is used to store pre-built finite state machine models; The state transition control module 70 is connected to the finite state machine model module, the automatic verification module, and the causal inference module. It is used to drive the early warning event to flow between predefined discrete states based on the pre-built finite state machine model and the automatic verification result and the causal inference result. The discrete states are the state to be verified, the effective early warning state, the state in progress, the closed loop state, the false alarm invalidation state, the missed alarm supplementation state, or the path cause waiting to be coordinated state. The collaborative handling module 80 is connected to the state transition control module and is used to match the corresponding digital collaborative handling strategy and data feedback mechanism for the early warning event that has transitioned to different discrete states, and sequentially complete one or more of the following operations: early warning handling, result feedback, report generation, and strategy iteration.

[0132] In some implementations of this application, the intelligent logistics vehicle early warning digital collaborative analysis and abnormal monitoring early warning system can also implement other steps corresponding to the method described in Embodiment 1. For details, please refer to the detailed description in Embodiment 1, which will not be repeated here.

[0133] Example 3 Corresponding to Embodiment 1 above, this application also provides a computer device, including: a processor and a memory, wherein the memory stores a computer program that can run on the processor, and when the computer program is executed by the processor, it executes the intelligent logistics vehicle early warning digital collaborative analysis and abnormal monitoring early warning method provided in any of the above embodiments.

[0134] in, Figure 3An exemplary computer device 1500 is shown, which may specifically include a processor 1510, a video display adapter 1511, a disk drive 1512, an input / output interface 1513, a network interface 1514, and a memory 1520. The processor 1510, video display adapter 1511, disk drive 1512, input / output interface 1513, network interface 1514, and memory 1520 can communicate with each other via a communication bus 1530.

[0135] The processor 1510 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided by the present invention.

[0136] The memory 1520 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1520 can store the operating system 1521 for controlling the operation of the electronic device, and the basic input / output system (BIOS) 1522 for controlling the low-level operations of the electronic device. Additionally, it can store a web browser 1523, a data storage management system 1524, and an icon font processing system 1525, etc. The aforementioned icon font processing system 1525 can be the application program that specifically implements the aforementioned steps in this embodiment of the invention. In summary, when implementing the technical solution provided by this invention through software or firmware, the relevant program code is stored in the memory 1520 and is called and executed by the processor 1510.

[0137] Input / output interface 1513 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0138] Network interface 1514 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0139] The bus includes a pathway for transmitting information between various components of the device, such as processor 1510, video display adapter 1511, disk drive 1512, input / output interface 1513, network interface 1514, and memory 1520.

[0140] In addition, the electronic device can also obtain information on specific claim conditions from the virtual resource object claim condition information database for condition judgment, and so on.

[0141] It should be noted that although the above-described device only shows the processor 1510, video display adapter 1511, disk drive 1512, input / output interface 1513, network interface 1514, memory 1520, bus, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the present invention, and not necessarily all the components shown in the figures.

[0142] Example 4 Corresponding to Embodiment 1 above, this application also provides a computer-readable storage medium. In this embodiment, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description and will not be repeated hereafter.

[0143] The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method as described above.

[0144] In some implementations of this application, when the computer program is executed by the processor, it can also implement the steps corresponding to the method described in Embodiment 1. Please refer to the detailed description in Embodiment 1, which will not be repeated here.

[0145] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware systems. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0146] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0147] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0148] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0149] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0150] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning, characterized in that, The method includes: Based on the multi-source operational data collected during the transportation process of logistics vehicles, combined with a pre-set configurable early warning strategy library, abnormal vehicle events are identified and early warning events are generated. The warning event is automatically verified for authenticity, the automatic verification result is recorded, and the warning event is initialized to a state to be verified. Using roadside units, weather radar, traffic flow detectors, and anonymous data of social vehicles on the same road segment as external intervention variables, causal inference is performed to perform root cause attribution and obtain causal inference results. The causal inference includes: performing causal inference based on road condition causal graphs at the road segment level to determine the posterior probability that the anomaly is attributed to sudden changes in road conditions or vehicle mechanical failures, and introducing a cargo vulnerability factor as a moderating variable in the road condition causal graph according to the type of transported goods. Based on a pre-built finite state machine model, and combined with the automatic verification results and the causal inference results, the early warning event is driven to flow between predefined discrete states, which are the state to be verified, the effective early warning state, the state in progress, the closed-loop state, the false alarm invalidation state, the missed alarm supplementation state, or the path cause waiting to be coordinated state. For the early warning events that have flowed to different discrete states, a corresponding digital collaborative handling strategy and data feedback mechanism are matched to complete one or more of the following operations in sequence: early warning handling, result feedback, report generation, and strategy iteration.

2. The intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method according to claim 1, characterized in that, The process of using roadside units, weather radar, traffic flow detectors, and anonymous data from other vehicles on the same road segment as external intervention variables to perform causal inference for root cause attribution and obtain causal inference results includes: Using the road segment where the logistics vehicle associated with the warning event is located as the analysis granularity, a Bayesian network structure is constructed for the road condition cause-effect graph; the road condition cause-effect graph contains various nodes used for anomaly attribution and directed edges between nodes; When the warning event is triggered, the current values ​​of the roadside unit, weather radar, traffic flow detector and anonymous data of social vehicles on the same road segment are used as evidence to input the road condition causal graph. Bayesian inference is used to calculate the first prediction probability that the latent variable node of road condition change is true under the given evidence condition, and the second prediction probability that the latent variable node of vehicle mechanical failure is true. Based on the type of transported goods, the corresponding vulnerability factor coefficient α is read from the preset cargo parameter table, where 0 < α < 1; the first predicted probability is weighted and corrected using the vulnerability factor coefficient α to obtain the corrected road condition attribution probability P_road, and the second predicted probability is weighted and corrected to obtain the corrected vehicle attribution probability P_vehicle. If P_road is greater than or equal to the first attribution threshold, the anomaly is determined to be attributed to a sudden change in road conditions, and P_road is output as the posterior probability of the anomaly being attributed to the sudden change in road conditions. If P_vehicle is greater than or equal to the second attribution threshold, the anomaly is determined to be attributable to vehicle mechanical failure, and P_vehicle is output as the posterior probability that is usually attributable to vehicle mechanical failure. If P_road is less than the first attribution threshold and P_vehicle is less than the second attribution threshold, then it is determined that the attribution cannot be clearly defined.

3. The intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method according to claim 2, characterized in that, The pre-built finite state machine model, combined with the automatic verification results and the causal inference results, drives the early warning event to flow between predefined discrete states, including: When the automatic verification result shows that the abnormality is real and the posterior probability of the road condition change in the causal inference result is greater than the first preset threshold, the warning event jumps from the pending verification state to the road cause pending coordination state. When the automatic verification result shows that the anomaly is real and the posterior probability of the vehicle mechanical fault in the causal inference result is greater than the second preset threshold, the warning event jumps from the pending verification state or the effective warning state to the handling state. When the automatic verification result indicates that the anomaly is real but the causal inference result indicates that the cause cannot be clearly attributed, the warning event will switch from the pending verification state to the effective warning state. When the automatic verification result is a false alarm, the warning event changes from the pending verification state to the false alarm invalidation state; When an automatic or manual trigger command is received, the warning event transitions from a valid warning state to a pending state. When the warning response result is received from the external system and the vehicle abnormality has been eliminated, the warning event transitions from the handling state to the closed-loop state. When a manually recorded abnormal event is confirmed and enters the handling process, the warning event will change from the missed reporting status to the handling status. The finite state machine model restricts closed-loop states and false alarm invalid states from transitioning to other states.

4. The intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method according to claim 1 or 2, characterized in that, The automatic authenticity verification also includes: The preset spatiotemporal consistency verification model is invoked to align the vehicle positioning data, on-board diagnostic timestamp data, and vehicle passage time data collected by the roadside unit in the multi-source operation data, and to construct the vehicle operation sequence feature vector. Based on the vehicle runtime sequence feature vector, the dynamic time warping distance between the actual observed sequence and the expected sequence in the preset normal driving mode library is calculated. When the dynamic time warping distance exceeds the preset threshold, it is determined that there is a time-space inconsistency anomaly. The method further includes: when the dynamic time warping distance output by the spatiotemporal consistency verification model shows a monotonically increasing trend within three consecutive verification cycles, automatically triggering the switching of the on-board device clock drift warning strategy of the corresponding vehicle in the configurable warning strategy library, and recording the switching operation in the log.

5. The intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method according to claim 1 or 2, characterized in that, The method also includes performing permission verification, condition verification, and log persistence for each state transition. The permission verification includes verifying whether the current operating role has the permission to trigger the state transition. And / or, The condition verification includes checking whether the current state meets the preset jump trigger conditions; And / or, The log solidification process involves storing the event ID, timestamp, source state, target state, triggering condition, operating role, and executed action for each state transition into the database, forming a state transition audit log.

6. The intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method according to claim 1 or 2, characterized in that, The digital collaborative processing strategy and data feedback mechanism includes at least one of the following: For early warning events that have progressed to the "in progress" stage, the notification service is automatically invoked to send early warning information to relevant personnel, and a collaborative handling work order is automatically generated. For early warning events that have reached the closed-loop state, the front-end page is automatically refreshed in real time and the early warning report data is updated synchronously. Each early warning message is assigned a unique identifier, which is then linked to the entire process of anomaly occurrence, early warning push, manual handling, anomaly elimination, and result verification, forming a closed-loop management system that integrates anomaly identification, status verification, early warning triggering, collaborative handling, result feedback, and data accumulation.

7. The intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method according to claim 1 or 2, characterized in that, The strategy iteration operation includes: Regularly analyze the accuracy, false alarm rate, and missed alarm rate of early warnings under different business scenarios; The warning threshold is adjusted based on the warning accuracy, and the warning judgment rule is updated based on the false alarm rate and the missed alarm rate.

8. The intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method according to claim 1 or 2, characterized in that, The method further includes: A dynamic spatiotemporal correlation graph of early warning events is constructed, wherein the dynamic spatiotemporal correlation graph uses early warning events as nodes and uses the temporal correlation, spatial trajectory overlap and business attribute similarity between events as edge weights to form a traceable chain of early warning events; When the finite state machine model drives the early warning event to the effective early warning state, the graph query operation of the dynamic spatiotemporal correlation graph is automatically triggered to identify the set of related early warning events that meet the preset correlation threshold with the early warning event; Based on the current state of each associated early warning event in the set of associated early warning events, the processing priority of the early warning events and the state transition determination rules in the finite state machine model are dynamically adjusted, wherein: If there are homogeneous events in the set of associated early warning events that are in the process of being handled or in the closed-loop state, then the handling priority of the early warning event is increased and the waiting time window in the state transition determination rule of the homogeneous event is shortened by a preset ratio. If there is a homogeneous event in the set of associated early warning events that is in the false alarm invalidation state, then the confidence score of the early warning event is lowered, and the priority prediction level in the automatic verification result is downgraded by one level.

9. The intelligent logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning method according to claim 1 or 2, characterized in that, The method further includes: Construct a road segment-level hybrid traffic flow twin with highway segments and service areas or toll station nodes as twin units, and simultaneously evolve logistics vehicle intelligent agents, social vehicle intelligent agents and roadside facility intelligent agents in the twin; When performing the strategy iteration operation, the new early warning strategy is rehearsed in the twin, and the strategy risk is assessed using a strategy infectious disease model: when the new early warning strategy causes any vehicle to trigger an early warning and enter the handling state, the vehicle is regarded as the source of infection, the abnormal transmission coefficient R0 is calculated, and whether the number of abnormal vehicles of the same type in the service area exceeds the capacity threshold is monitored. If the abnormal propagation coefficient R0>1, or if the number of abnormal vehicles of the same type in the service area exceeds the capacity threshold within a preset time, then the new early warning strategy is determined to be a high-risk strategy for infectiousness, and the new early warning strategy is automatically rejected. In response to mandatory inspection strategies for cold chain or hazardous chemicals, the twin simulates the parking conflicts caused by the intervention of the mandatory inspection strategy. If the simulation shows that subsequent vehicles will not be able to find compliant parking spaces within the specified time limit, the status of some vehicles in the process of being processed will be automatically delayed to the next qualified service area. For specific road sections, digital vaccines are injected into the twin to simulate the attribution correctness and treatment path effectiveness of the finite state machine model under extreme conditions. An immunization certificate for the road section is obtained through a vaccine testing strategy, and strategies that fail to pass are prohibited from being effective on the road section.

10. A smart logistics vehicle early warning digital collaborative analysis and anomaly monitoring and early warning system, characterized in that, The system is used to implement the method as described in any one of claims 1 to 9, the system comprising: The multi-source data acquisition module is used to collect multi-source operational data during the transportation process of logistics vehicles; The early warning strategy configuration module is used to build and configure a configurable early warning strategy library; An abnormal event identification module, connected to the multi-source data acquisition module and the early warning strategy configuration module, is used to identify abnormal vehicle events based on the multi-source operational data and in combination with a preset configurable early warning strategy library, and generate early warning events. An automatic verification module, connected to the abnormal event identification module, is used to automatically verify the authenticity of the warning event, record the automatic verification result, and initialize the warning event to a state to be verified. The causal inference module is used to perform causal inference based on roadside units, weather radar, traffic flow detectors, and anonymous data of social vehicles on the same road segment as external intervention variables to perform root cause attribution and obtain causal inference results. The causal inference includes: performing causal inference based on a road condition causal graph with road segment as the granularity to determine the posterior probability that the anomaly is attributed to a sudden change in road conditions or a mechanical failure of the vehicle, and introducing a cargo vulnerability factor as a moderating variable in the road condition causal graph according to the type of transported goods. The finite state machine model module is used to store pre-built finite state machine models; The state transition control module, connected to the finite state machine model module, the automatic verification module, and the causal inference module, is used to drive the early warning event to flow between predefined discrete states based on the pre-built finite state machine model and the automatic verification result and the causal inference result. The discrete states are the state to be verified, the effective early warning state, the state in progress, the closed-loop state, the false alarm invalidation state, the missed alarm supplementation state, or the path cause waiting to be coordinated state. The collaborative handling module, connected to the state transition control module, is used to match the corresponding digital collaborative handling strategy and data feedback mechanism for the early warning event that has transitioned to different discrete states, and sequentially complete one or more of the following operations: early warning handling, result feedback, report generation, and strategy iteration.

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