An AI-based method for automatically constructing multi-dimensional data chains for logistics waybills

Abstract

This invention discloses an AI-based method for automatically constructing a multi-dimensional data chain for logistics waybills, belonging to the field of big data processing technology. This AI-based method includes: S1, completing multi-source collection and unified indexing of waybill entities, trajectory nodes, and historical samples, and extracting them into standardized feature vectors; S2, performing multi-dimensional consistency judgment on trajectory segments based on windowed spatiotemporal similarity measurement and structural coupling feature analysis; S3, constructing a segment trajectory confidence model by integrating multi-source heterogeneous behavior, business constraints, historical associations, and sequence patterns; S4, performing robust end-to-end adjudication guided by multi-granularity confidence markers and fluctuation convergence; and S5, performing candidate version retention, local review, and controlled recalculation to achieve continuous adaptive optimization and stability assurance. This method solves the problems of difficulty in locating conflicts and ensuring stable link consistency under cross-source semi-consistent conditions.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, specifically to an automatic construction method for multi-dimensional data chains of logistics waybills based on AI verification. Background Technology

[0002] The logistics fulfillment chain spans all stages, including pickup, loading and unloading, trunk transportation, terminal transfer, delivery, and receipt confirmation. The trajectory and node records associated with waybills serve not only for in-transit visibility and timeliness verification, but also for billing settlement, anomaly tracing, and compliance auditing. With the parallel access of data sources such as vehicle positioning, driver terminals, terminal scanning devices, road passage records, energy replenishment records, and receipt confirmations, the same waybill forms a multi-source heterogeneous record set at the timestamp, geographical location, node type, and source identifier levels. To support end-to-end verification and traceability, the industry generally needs to uniformly index and structurally archive waybill entity information, business constraint information, trajectory node information, source profile information, and historical sample information. It also needs to organize time statistics, spatial statistics, and business elements into computable feature expressions, thereby connecting multi-source trajectory fragments into a queryable, verifiable, and evolving waybill data chain.

[0003] For example, the invention patent with publication number CN111400620A discloses a user trajectory location prediction method based on spatiotemporal embedding Self-Attention. This method first reads all user identifiers, POI identifiers, access times, and POI geographic location information from the user's historical trajectory in the original check-in records; it calculates the spatial distance cost between all POIs and the time interval and timestamp information between all trajectory points for each user; then, it establishes a spatiotemporal embedding Self-Attention model based on the user's historical trajectory to obtain the probability distribution of the user's next access point; the established spatiotemporal embedding Self-Attention model simultaneously considers the influence of POI geographic location information and the user's historical POI access time information on the user's next access behavior. This invention significantly improves the training speed of the trajectory prediction model, greatly shortens the trajectory model training time, and also improves the model's prediction accuracy.

[0004] For example, the invention patent with announcement number CN113837703B discloses a real-time automatic verification method for quantifying and preventing duplicate shipments of logistics waybills. This verification method includes a dynamic information monitoring system for shipments based on historical waybills; a probe service cluster built using a data probe mechanism; a data analysis service cluster established by extracting overlapping waybill data based on the shipment information; and a verification service cluster presented to users for decision-making. This invention provides real-time, efficient, automated, and accurate verification of duplicate waybills without affecting existing system business logic. It accurately and in real-time detects data changes by using a probe method disguised as a database slave. The analysis service cluster is decoupled from the probe service via Kafka to avoid affecting the real-time performance of probe data. The analysis service, deployed in a cluster, quickly integrates historical big data and probe data for comprehensive analysis, generating decision-making data. It can efficiently and automatically process large amounts of business data, significantly improving accuracy.

[0005] For the full lifecycle management of waybills, existing technologies often only focus on the results at a specific point in time, lacking a unified characterization of the impact of incremental data arrival. When new trajectory records, delayed scan records, fee and re-addition records, and customer complaint supplementary records arrive, the common practice is to re-trigger the trajectory stitching process, directly overwriting the original results. This lacks quantitative measurement of the stability differences and scope of change impact between old and new versions, and also lacks a dedicated management mechanism for version evolution. As a result, waybill trajectory versions exported at different times may have significant discrepancies. When settlement verification, customer complaint tracing, and compliance audits use multiple batches of exported results, they face the risks of inconsistent links and unclear responsibility definitions. Summary of the Invention

[0006] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an AI-based method for automatically constructing multi-dimensional data chains for logistics waybills, which solves the problems of difficulty in locating conflicts and ensuring stable consistency of the data chain under semi-consistent conditions across different sources.

[0007] Technical solution To achieve the above objectives, this invention employs the following technical solution: an AI-verified method for automatically constructing a multi-dimensional data chain for logistics waybills, comprising: S1, completing multi-source collection and unified indexing of waybill entities, trajectory nodes, source profiles, and historical samples, and extracting time statistics, spatial statistics, and business elements into standardized feature vectors; S2, performing multi-dimensional consistency discrimination on trajectory segments based on windowed spatiotemporal similarity measurement and structural coupling feature analysis, realizing dynamic interval division and conflict feature localization, and driving the screening and classification of conflict intervals; S3, constructing a segment trajectory confidence model by integrating multi-source heterogeneous behavior, business constraints, historical associations, and sequence patterns, and outputting multi-granularity confidence identifiers; S4, performing robust adjudication across the entire chain guided by multi-granularity confidence identifiers and fluctuation convergence, executing candidate shrinkage, replacement repair, and reorganization search, completing main chain locking, and retaining local backups; S5, conducting version robustness review guided by version continuity and change impact, executing candidate version retention, local review, and controlled recalculation, achieving continuous adaptive optimization and stability assurance.

[0008] Furthermore, the specific measures for completing the multi-source collection and unified indexing of waybill entities, trajectory nodes, source profiles, and historical samples are as follows: Collect waybill entity and business constraint data: Read the waybill creation time, task completion time, origin and destination network codes, geofence, product type, service time limit, carrier identifier, and vehicle license plate number through the waybill management platform, and bind these fields with the unique waybill identifier to directly construct a business constraint feature vector; Collect trajectory and node data: Obtain vehicle location time sequence, driver check-in and loading / unloading site operation time sequence, and road access time sequence through the vehicle monitoring platform, driver terminal, station scanning device, road access platform, and energy replenishment platform. For each refueling station time node, bind each node with the waybill identifier, source channel identifier, node type and timestamp, and write it into the trajectory node record in the order of waybill and time to construct the trajectory node time sequence; collect source behavior and quality data: statistically analyze the missing field ratio, data delay times, historical supplementation times and dispute node records of each data source to generate source profile features and bind source identifiers; collect historical trajectory samples and version evolution data: extract the node time interval distribution and road network shortest distance distribution from undisputed historical waybills, establish a time scale and spatial scale reference table, and record the characteristics of historical trajectory segments and changes in the stability of each main chain according to the waybill number.

[0009] Furthermore, the specific measures for extracting time statistics, spatial statistics, and business elements into standardized feature vectors are as follows: The time series of trajectory nodes is divided into time intervals with a fixed time length and sliding step size; the difference between the start and end times of each time interval is calculated to obtain the candidate window length. Within the same candidate window, the timestamps of the source nodes are compared with the corresponding reference node timestamps in historical samples, and the average time difference is calculated to obtain the average time offset. Representative values ​​are extracted from the distribution of time intervals between adjacent nodes in undisputed historical waybills to obtain the time scale. Within the same candidate window, the latitude and longitude of nodes are mapped to the road network, and the geodetic distance from the source node to the reference path is calculated; the average geodetic distance within the window is calculated to obtain the average spatial offset. Representative values ​​are extracted from the distribution of the shortest road network distances between adjacent nodes in undisputed historical waybills to obtain the spatial scale. Statistical location matching is performed within the same candidate window. The node overlap ratio is obtained by comparing the number of successful nodes with the total number of nodes within the window. Business constraint feature vectors are obtained by encoding and concatenating the waybill main field and service time limit field. Historical similar segment matching data is obtained by performing similarity searches in historical trajectory samples using time interval sequences and spatial path codes as search keys and extracting key statistics of similar segments. After completing the above data acquisition, linear scaling and zero-mean standardization are performed on each dimension to generate standardized feature vectors, which are then written to the feature cache table. A waybill identifier is generated for each waybill, a node identifier is generated for each trajectory node, and a source identifier and batch identifier are assigned to each source. All timestamps are uniformly converted to a unified time format, and all location data are uniformly converted to a unified coordinate expression method. The waybill master record, trajectory node record, source profile record, and version evolution record are associated and stored through the waybill identifier to construct a logistics operation and maintenance database.

[0010] Furthermore, based on the spatiotemporal similarity measurement and structural coupling feature analysis of windowed segments, the specific measures for multidimensional consistency discrimination of trajectory segments are as follows: All candidate windows covered by the candidate segments are obtained; within each candidate window, the average time offset, average spatial offset, time scale, spatial scale, and node overlap ratio are read; for each candidate window, the average time offset is divided by the time scale to obtain the time offset ratio, and the average spatial offset is divided by the spatial scale to obtain the spatial offset ratio; the time offset ratio and spatial offset ratio are squared and summed, and the square root of the sum is taken to obtain the comprehensive spatiotemporal offset of the candidate window; the comprehensive spatiotemporal offset is divided by the sum of the node overlap ratio and the smallest positive number to obtain the consistency conflict local intensity of this candidate window; the consistency conflict local intensities of all candidate windows are compared, and the largest value is selected as the consistency conflict intensity value of this candidate segment.

[0011] Furthermore, the specific measures to achieve dynamic interval division and conflict feature localization, and drive the screening and classification of conflict intervals, are as follows: By comparing the consistent conflict intensity value and the conflict threshold in real time, when the consistent conflict intensity value is less than the conflict threshold, the corresponding trajectory segment is determined as a stable trajectory segment and connected in chronological order to form a stable segment linked list without changing the existing splicing results; the node matching relationship confirmed in the current time interval is solidified as a benchmark matching pair, and a consistent conflict intensity value sequence is constructed, synchronously outputting the time behavior feature vector, spatial path feature vector, and source behavior feature vector; when the consistent conflict intensity value is greater than or equal to the conflict threshold, the trajectory interval is marked as a conflict area and a local optimization process is triggered: shortening the duration of the conflict center window and reducing the time interval between adjacent windows to increase the phase transition time. The overlapping parts between adjacent windows; if the time offset of a candidate trajectory segment exceeds the P25-P75 quantile range of the historical trajectory node interval, or the spatial distance offset exceeds k times the shortest road network distance of the historical samples, it is judged as abnormal and removed to achieve tight matching of nodes; a settlement freeze mark is generated for the waybill in the conflict section, and the corresponding automatic settlement interface is blocked from being called. The route planning side abandons the path segment including the conflict area; the trajectory segments and scan records of all data sources in the conflict section are collected synchronously, the candidate segment set is regenerated, and supplementary collection is triggered for the part of the trajectory segment and scan record with missing data. The conflict intensity is recalculated. If the conflict intensity value is still greater than or equal to the conflict threshold, the trajectory segment and processing process of the conflict section will be completely written into the conflict sample table.

[0012] Furthermore, the specific measures for constructing a segment trajectory confidence model and generating multi-granularity confidence labels by integrating multi-source heterogeneous behavior, business constraints, historical correlations, and sequence patterns are as follows: Input the time behavior feature vector, spatial path feature vector, business constraint feature vector, source behavior feature vector, historical similar segment matching data, and consistent conflict intensity value sequence within the conflict time period of the waybill. Construct a multi-dimensional trajectory evaluation feature sequence covering the same observation time window. Through sliding window feature extraction, multi-source feature fusion, and dynamic feature filtering, first calculate the time offset score, spatial detour score, business violation score, and source behavior feature vector based on the road network topology, capacity operation rules, and service timeliness rules. The basic evaluation results, composed of deviation scores, are then nonlinearly mapped using a data-driven framework centered on a tree model and a deep network superposition structure. This results in a fragment trajectory confidence model. The fragment trajectory confidence model adjusts the feature sensitivity and internal parameters based on changes in consistent conflict intensity values ​​and the distribution of historical conflict samples. It calculates and outputs the confidence value of a single fragment trajectory and its corresponding risk label. Fragments with a confidence value greater than the confidence threshold are written into a stable fragment queue and concatenated into a candidate trajectory chain in chronological order. Fragments with a confidence value less than or equal to the confidence threshold are written into a conflict fragment queue and accompanied by the original features and conflict labels to form training samples.

[0013] Furthermore, the specific measures for end-to-end robust adjudication guided by multi-granularity confidence indicators and fluctuation convergence are as follows: During the candidate trajectory chain generation process, the number of segments contained in each candidate trajectory chain is counted to obtain the number of candidate trajectory chain segments; the trajectory confidence value of each segment in the candidate trajectory chain and the total number of segments are obtained; the reciprocal of the trajectory confidence value of each segment is taken to obtain the confidence reciprocal term; the natural logarithm of the confidence reciprocal term is taken to obtain the logarithmic reciprocal term; the square of the logarithmic reciprocal term is taken to obtain the logarithmic square term; the sum of the logarithmic square terms of all segments is obtained to obtain the sum of squares term; the sum of squares term is divided by the number of candidate trajectory chain segments to obtain the mean term; the mean term is negative to obtain the negative mean term; and the link stability value is obtained by performing an exponential operation on the negative mean term.

[0014] Further, the specific measures for performing candidate shrinkage, replacement repair, and reorganization searches to complete main chain locking and retain local backup are as follows: By comparing the link stability value and stability threshold in real time, when the link stability value is less than the stability threshold, the corresponding candidate trajectory chain is determined to be an unstable link, and the unique main chain is no longer directly extracted from this chain. The candidate segment set of the same waybill is updated from the segment set within the current conflict interval to the segment set within the candidate windows covering the conflict interval and before and after the conflict interval. Within the conflict interval, the segment trajectory confidence value is sorted in descending order to generate a segment candidate queue. The front end of the segment candidate queue is selected to perform segment replacement and boundary repair. The candidate chain is split into sub-chains according to the low-confidence segment neighborhood, respectively, based on mutual exclusion and connectivity. Under relational constraints, a depth-first approach is performed before reorganization; the link stability value is recalculated, and if it is still less than the stability threshold, the candidate chain is discarded, a check mark is output, the main chain of the previous version remains unchanged, the waybill is marked as check, and the candidate chain, the reason for discard, and the processing procedure are written into the conflict sample table; when the link stability value is greater than or equal to the stability threshold, the corresponding candidate trajectory chain is determined as an acceptable link, the segment sequence of this chain is locked as the main chain skeleton, segments that have time conflicts, spatial conflicts, and business stage conflicts with the main chain are cleaned up, and only a preset number m adjacent replacement segments of the main chain are retained as local candidates, a candidate chain list is generated simultaneously, and the main chain structure and link stability value are written into the robustness review process of subsequent versions.

[0015] Furthermore, the specific measures for conducting version robustness reviews guided by version continuity and the impact of changes are as follows: After the incremental update, the new main chain is subjected to the full-link robustness adjudication process to obtain the stability of the newly added main chain; the number of replaced segments and the total number of main chain segments are obtained by comparing and statistically analyzing the segment indices of the new and old main chains, and the updated main chain ratio is calculated accordingly; the stability of the original version's main chain is obtained by retrieving the link stability record of the previous main version in the version evolution record by waybill number; the stability difference term is obtained by subtracting the stability of the original version's main chain from the stability of the newly added main chain; the stability difference term is squared to obtain the stability difference squared term; the updated main chain ratio is squared to obtain the update ratio squared term; the square root of the sum of the stability difference squared term and the update ratio squared term is obtained by performing a square root operation to obtain the comprehensive deviation term; and the version robustness value is obtained by subtracting the comprehensive deviation term from one.

[0016] Furthermore, to achieve continuous adaptive optimization and stability assurance, specific measures are implemented for candidate version retention, partial review, and controlled recalculation: By comparing the version robustness value and robustness threshold in real time, when the version robustness value is less than the robustness threshold, the newly constructed data chain is registered as a candidate version without replacing the current main version. A version risk marker is applied to all involved waybills, recording the time interval, number of segments, and link stability changes involved in this increment, and forming a candidate version list by waybill number. Within conflict intervals, the output stable segment list and candidate chain list are reloaded, and the segment splicing results are re-examined in chronological and spatial order. For disputed intervals, supplementary recording tasks are issued, and after supplementing missing scan records, link stability is only partially recalculated in the relevant intervals. The version robustness value is recalculated, and settlement freeze flags and performance confirmation freeze flags for disputed versions are generated. The frozen state is maintained by controlling settlement interface calls and process engine state transitions. The state is suspended as long as the number of recalculations does not exceed the upper limit N and the version robustness value is still less than the robustness threshold. The suspension flag is lifted when the version robustness value rises to greater than or equal to the robustness threshold after recalculation. When the version robustness value is greater than or equal to the robustness threshold, the newly constructed data chain is identified as a robust version and promoted to the new master version. The version reference is updated synchronously so that subsequent queries and statistics uniformly use the latest master version data chain. At the same time, the original master version, along with the version number and construction time, the list of difference fragments involved in this version switch, and the corresponding version robustness value, are archived in the version evolution record table.

[0017] Beneficial effects The present invention has the following beneficial effects: (1) This invention collects data from multiple sources, including waybill subjects, trajectory nodes, source profiles and historical samples, and completes unified indexing with waybill identifiers, node identifiers and source identifiers. Combined with the unified conversion of time format and coordinate expression method, it achieves the effect of cross-source data traceability and consistent alignment, effectively solving the problem of difficulty in correlation and verification caused by inconsistent data calibers from multiple sources in the prior art.

[0018] (2) This invention achieves the effect of quantification of spatiotemporal offset and comparable evaluation by uniformly calculating the candidate window length, average time offset, average spatial offset, time scale, spatial scale and node overlap ratio, and forming a standardized feature vector, thereby effectively solving the problem that semi-consistent trajectories are difficult to measure with a unified scale in the prior art.

[0019] (3) This invention constructs a segment trajectory confidence model by integrating time behavior feature vectors, spatial path feature vectors, business constraint feature vectors, source behavior feature vectors and historical similar segment matching data, and outputs single segment trajectory confidence values ​​and risk labels, thereby achieving the effect of candidate segment hierarchical screening and sample accumulation, effectively solving the problem of difficulty in convergence caused by similar candidate segment scores in the prior art.

[0020] (4) This invention calculates the version robustness value by comparing the stability of the newly added main chain with the stability of the original version main chain and combining the main chain update ratio. When the version robustness value does not reach the robustness threshold, the candidate version is retained and a partial review is triggered. Within the upper limit N of the number of recalculations, a partial recalculation is performed and a settlement freeze mark and a performance confirmation freeze mark are generated to control the settlement interface call and the process engine state migration. When the version robustness value reaches the robustness threshold, the main version switch and version evolution archive are completed, thereby achieving the effect of controlled version continuation and stable incremental maintenance. This effectively solves the problem of repeated version switching and unstable traceability links caused by incremental writing in the prior art.

[0021] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification, as described in this invention. Figure 2 This is a diagram illustrating the evolution of the conflict intensity situation of waybills in this invention. Figure 3 This is a flowchart of the stability assessment and decision-making process for the waybill trajectory chain in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figures 1-3This invention provides a technical solution: an AI-verified method for automatically constructing a multi-dimensional data chain for logistics waybills, comprising: S1, completing multi-source collection and unified indexing of waybill subjects, trajectory nodes, source profiles, and historical samples, and extracting time statistics, spatial statistics, and business elements into standardized feature vectors; S2, performing multi-dimensional consistency discrimination on trajectory segments based on windowed spatiotemporal similarity measurement and structural coupling feature analysis, realizing dynamic interval division and conflict feature localization, and driving the screening and classification of conflict intervals; S3, constructing a segment trajectory confidence model by integrating multi-source heterogeneous behavior, business constraints, historical associations, and sequence patterns, and outputting multi-granularity confidence identifiers; S4, performing robust adjudication across the entire link guided by multi-granularity confidence identifiers and fluctuation convergence, performing candidate shrinkage, replacement repair, and reorganization search, completing main chain locking, and retaining local backups; S5, conducting version robustness review guided by version continuity and change impact, performing candidate version retention, local review, and controlled recalculation, and achieving continuous adaptive optimization and stability assurance.

[0025] Specifically, the measures for completing the multi-source collection and unified indexing of waybill entities, trajectory nodes, source profiles, and historical samples are as follows: The waybill management platform reads the waybill creation time, task completion time, origin and destination network codes, geofence, product type, service time limit, carrier identifier, and vehicle license plate number. These fields are then bound to the waybill's unique identifier to directly construct a business constraint feature vector. Trajectory and node data are collected: Vehicle location time sequence, driver check-in and loading / unloading site operation time sequence, and road passage and refueling station time nodes are obtained through vehicle monitoring platforms, driver terminals, station scanning devices, road access platforms, and energy replenishment platforms. Each node is bound to the waybill identifier, source channel identifier, node type, and timestamp, and then written into the trajectory node record according to the waybill and time sequence to construct a trajectory node time sequence. Source behavior and quality data are collected: The proportion of missing fields, number of data delays, number of historical supplements, and dispute node records for each data source are statistically analyzed to generate source profile features and bind them to the source identifier. Historical trajectory samples and version evolution data are collected: The node time interval distribution and road network shortest distance distribution are extracted from undisputed historical waybills. A time-scale and spatial-scale reference table is established, and the characteristics of historical trajectory segments and changes in main-chain stability are recorded according to the waybill number. In the above data collection process, disputed node records refer to nodes found to have data quality problems or obvious anomalies during data collection and processing, such as missing data, excessive timestamp deviations, or spatial locations that do not conform to the baseline trajectory. These nodes need to be marked as disputed nodes. Undisputed historical waybills are defined as those where all trajectory data conforms to historical standards and no anomalies are found during the collection process. Therefore, the time interval distribution and road network shortest distance distribution extracted from these undisputed historical waybills can serve as comparison standards for subsequent waybills, ensuring consistency between new data and historical patterns.

[0026] This implementation plan establishes a comprehensive database of waybill entities, trajectory nodes, source profiles, and historical samples through multi-source data collection and unified indexing. By comprehensively collecting data on waybill entities and business constraints, trajectory and node data, and source behavior and quality data, combined with the extraction of historical trajectory samples and version evolution data, the accuracy, completeness, and consistency of the data are ensured. Furthermore, by monitoring data quality and marking disputed nodes, effective handling of problematic nodes is ensured during subsequent analysis, thereby enhancing the reliability and stability of the data chain and laying a solid foundation for subsequent feature extraction and fragment trajectory confidence model construction.

[0027] Specifically, the measures for extracting time statistics, spatial statistics, and business elements into standardized feature vectors are as follows: The time series of trajectory nodes is divided into time intervals with a fixed time length and sliding step size; the difference between the start and end times of each time interval is calculated to obtain the candidate window length. Within the same candidate window, the timestamps of source nodes are compared with the corresponding reference node timestamps in historical samples, and the average time difference is taken to obtain the average time offset. Representative values ​​are extracted from the distribution of time intervals between adjacent nodes in undisputed historical waybills to obtain the time scale. Within the same candidate window, the latitude and longitude of nodes are mapped to the road network, and the geodetic distance from the source node to the reference path is calculated using the spherical distance formula. The calculated distance values ​​are judged according to the corresponding standards, and the average geodetic distance of all nodes within the window is taken as the average spatial offset. The spatial scale is obtained by extracting representative values ​​from the distribution of the shortest road network distance between adjacent nodes in undisputed historical waybills. The judgment rule for the distribution of the shortest road network distance is based on the distribution of the shortest path in historical data, using the P25-P75 quantile range or median as the standard for extraction. The node overlap ratio is obtained by statistically analyzing the ratio of the number of nodes that successfully match the location within the same candidate window to the total number of nodes in the window. The judgment rule for location matching is: within the same time window, if the location error between the latitude and longitude of a node and the corresponding node on the reference path is less than 2 meters, it is considered a successful match; otherwise, it is considered a failed match. Business constraint feature vectors are obtained by encoding and concatenating the waybill main field and service time limit field. Historical similar segment matching data are obtained by performing similarity retrieval in historical trajectory samples using time interval sequence and spatial path encoding as retrieval keys and extracting key statistics of similar segments. After completing the above data acquisition, linear scaling and zero-mean standardization are performed on each dimension to generate standardized feature vectors and write them to the feature cache table. A waybill identifier is generated for each waybill, a node identifier is generated for each trajectory node, and a source identifier and batch identifier are assigned to each source. All timestamps are uniformly converted to a unified time format, and all location data are uniformly converted to a unified coordinate expression method. The waybill master record, trajectory node record, source profile record, and version evolution record are associated and stored through the waybill identifier to build a logistics operation and maintenance database.

[0028] In this implementation plan, multi-dimensional temporal and spatial features are extracted by segmenting and analyzing the time-series data of trajectory nodes. These features are then combined with business constraints to generate standardized feature vectors, providing a foundation for subsequent trajectory analysis and data chain construction. Specifically, features such as time offset, spatial offset, and node overlap within the candidate window are calculated to extract data reflecting spatiotemporal consistency. These features are then standardized based on statistical information from historical samples. Simultaneously, waybill information and service time limits are encoded and combined with other relevant data to form a comprehensive feature vector. Finally, data is stored using a unified time format and coordinate method, providing accurate and unified foundational data for subsequent dynamic analysis, conflict detection, and version updates.

[0029] Specifically, based on the spatiotemporal similarity measurement and structural coupling feature analysis of windowed segments, the specific measures for multidimensional consistency discrimination of trajectory segments are as follows: All candidate windows covered by the candidate segments are obtained, and within each candidate window, the average time offset, average spatial offset, time scale, spatial scale, and node overlap ratio are read. The average time offset characterizes the time alignment difference between nodes recorded by the vehicle monitoring platform, driver terminal, station scanning device, road passage platform, and energy supply platform; the average spatial offset characterizes the alignment difference between the positions of nodes from multiple sources in the road network trajectory and the electronic fence; and the node overlap ratio characterizes the percentage of successful matching of nodes from multiple sources, which can correspond to the waybill matching in audit scenarios. The verification requirement is to ensure consistency between start and end times and node locations. For each candidate window, the average time offset is divided by the time scale to obtain the time offset ratio, and the average spatial offset is divided by the spatial scale to obtain the spatial offset ratio, ensuring comparability of offsets across different line mileages and node intervals. The time offset ratio and spatial offset ratio are squared and summed, and the square root of the sum is taken to obtain the comprehensive spatiotemporal offset of the candidate window. An increase in the comprehensive spatiotemporal offset indicates a decrease in the synchronicity of records from multiple sources on the time axis and spatial path. The comprehensive spatiotemporal offset is divided by the sum of the node overlap ratio and the minimum positive number to obtain the local consistency conflict intensity of this candidate window. The minimum positive number is set as... This value is used to avoid division by zero during calculation. When the node overlap ratio is close to zero, it ensures that the denominator is not too small, thus guaranteeing the stability of the calculation results; it increases the consistency conflict local intensity value when the node overlap ratio decreases, thus reflecting the abnormal behavior when the document flow, fund flow, trajectory flow and node records are difficult to corroborate each other; it compares the magnitudes of the consistency conflict local intensity values ​​among all candidate windows, selects the one with the largest value as the consistency conflict intensity value of this candidate segment, explicitly locates the conflict center position at the window granularity, and provides input for subsequent conflict interval delineation.

[0030] The specific calculation method for the consistency conflict intensity value is as follows: ; In the formula, The consistency conflict intensity value represents the candidate segment, reflecting the degree of conflict at the segment under the combined effects of spatiotemporal offset and node overlap. The candidate window length represents the set of indices formed by dividing the entire segment into fixed-length windows along the time axis; w represents the time window index, indicating that in... The specific window that is selected; This represents the average time offset of the source trajectory within window w relative to the reference trajectory, reflecting the overall forward or backward shift of the scan timestamps within the window on the time axis. It indicates the time scale, reflecting the typical level of time intervals between adjacent nodes in a historical sample of normal waybills; It represents the average spatial offset of the source trajectory relative to the reference trajectory within window w, reflecting the overall geographical offset of the latitude and longitude of nodes within the same window. It indicates the spatial scale and reflects the typical level of road network distance between adjacent nodes in the historical normal trajectory; ε represents the percentage of nodes overlapping within window w, reflecting the proportion of successful position matching between source trajectory nodes and reference trajectory nodes within the same window; ε represents a very small positive number, reflecting the protective effect on the denominator when the node overlap percentage is close to zero.

[0031] In this embodiment, the average time offset of group 1 is 12.5, the time scale is 15, the average spatial offset is 5.2, the spatial scale is 8, the node overlap ratio is 0.85, and the calculated consistency conflict intensity value is 0.571; the average time offset of group 2 is 45.3, the time scale is 30, the average spatial offset is 12.8, the spatial scale is 15, the node overlap ratio is 0.42, and the calculated consistency conflict intensity value is 2.134; the average time offset of group 3 is 8.2, the time scale is 12, and the average spatial offset is 2. Group 1 has a spatial scale of 5, a node overlap ratio of 0.92, and a calculated consistency conflict intensity value of 0.238. Group 4 has an average time offset of 78.6, a time scale of 25, an average spatial offset of 18.5, a spatial scale of 12, a node overlap ratio of 0.31, and a calculated consistency conflict intensity value of 4.809. Group 5 has an average time offset of 23.7, a time scale of 18, an average spatial offset of 7.3, a spatial scale of 10, a node overlap ratio of 0.67, and a calculated consistency conflict intensity value of 1.302.

[0032] Table 1. Data Table for Waybill Conflict Intensity Assessment Based on Spatiotemporal Offset like Figure 2The figure shown is a diagram illustrating the evolution of the conflict intensity of waybills provided in this application embodiment. Combined with the data in Table 1... Figure 2 It is evident that the conflict intensity varies significantly across different waybill types and time windows, with spatiotemporal offset and node overlap characteristics jointly influencing the conflict intensity value. For example, in large-item transportation, when the time offset reaches as high as 78.6 minutes and the node overlap ratio is as low as 0.31, the consistent conflict intensity value surges to 4.809, exhibiting an abnormal spike and triggering the system to perform emergency processing and comprehensive verification. In the stable phase of standard express delivery, all parameters are at low levels, with a consistent conflict intensity value of only 0.571, exhibiting a stable waveform below the conflict threshold, indicating that transportation continues normally and demonstrating good stability in daily transportation. In cold chain transportation, when encountering traffic congestion, the average time offset reaches 45.3 minutes, and the consistent conflict intensity value exceeds 2.134, exhibiting a conflict range that continuously exceeds the conflict threshold. The system then initiates a composite control mechanism of suspending settlement and re-verifying, reflecting a strong response capability to abnormal transportation. This indicates that time series analysis can effectively capture dynamic changes in the transportation process, and the fluctuation of the conflict intensity value is highly correlated with the actual transportation status. The intensity of conflicts increases significantly during morning and evening rush hours, consistent with actual traffic congestion patterns. While there are some fluctuations during the midday period, the intensity of conflicts remains within a controllable range due to the high overlap of time points. This time series pattern provides a reliable basis for real-time monitoring and early warning of logistics waybills, allowing for dynamic adjustments to monitoring strategies and actions based on different time periods and transportation statuses. It also provides effective data support for transportation quality trend analysis, anomaly warnings, and intelligent decision-making, contributing to a comprehensive improvement in transportation consistency, customer satisfaction, and overall logistics stability.

[0033] In this implementation scheme, candidate segments are divided into multiple candidate windows. The average time offset, average spatial offset, time scale, spatial scale, and node overlap ratio are read. The offset is scaled using the time and spatial scales and a comprehensive spatiotemporal deviation index is synthesized. Then, combined with the node overlap ratio, a window-level local intensity quantification result is formed. The window with the largest local intensity is selected as the segment conflict center from all candidate windows. This transforms the inconsistent performance of multi-source trajectories on the time axis and road network location into a comparable numerical indication, providing a consistent input basis for conflict interval location, segment selection, and subsequent link adjudication.

[0034] Specifically, the measures to achieve dynamic interval division and conflict feature localization, and drive the screening and classification of conflict intervals are as follows: By comparing the consistent conflict intensity value and the conflict threshold in real time, when the consistent conflict intensity value is less than the conflict threshold, the system determines the trajectory segment as a stable trajectory segment and connects them in time order to form a stable segment linked list without adjusting the existing splicing structure. At the same time, the node matching relationship confirmed in the current time interval is solidified as a benchmark matching pair, a consistent conflict intensity value sequence is established, and the time behavior feature vector, spatial path feature vector and source behavior feature vector are output synchronously. If the consistency conflict intensity value is greater than or equal to the conflict threshold, the corresponding trajectory interval is marked as a conflict zone, and a local optimization process is initiated. This includes shortening the duration of the conflict center window, reducing the time interval between adjacent windows, and increasing the overlap between adjacent windows, thereby refining the temporal granularity of conflict detection. Tightening matching is performed on nodes; if the time offset of a candidate trajectory segment exceeds the P25-P75 quantile range of historical trajectory node intervals, or the spatial distance offset exceeds k times the shortest road network distance of historical samples, it is judged as abnormal and removed, improving the accuracy and effectiveness of matching. Here, k is the spatial offset threshold, with a value of 1.5, which is sufficient to effectively identify trajectory segments with abnormal spatial distribution while ensuring the tolerance range of normal trajectories. This is used to measure whether the spatial offset of a candidate trajectory segment is abnormal. For waybills involved in conflict zones, a settlement freeze flag is generated, and calls to relevant automatic settlement interfaces are blocked. Simultaneously, during route planning, path segments containing conflict zones are proactively avoided to prevent abnormal data from impacting business decisions. The system also synchronously collects trajectory segments and scan records from all data sources within the conflict zone, regenerates a candidate segment set, triggers supplementary collection for missing data, and recalculates the conflict intensity after completion. If the conflict intensity value is still greater than or equal to the conflict threshold, the trajectory segments and processing procedures of the conflict zone are completely written into the conflict sample table, achieving full-process archiving and traceability of abnormal situations.

[0035] This implementation scheme enables automatic identification and refined routing of consistency conflicts in multi-source trajectory segments, effectively distinguishing stable and conflicting intervals in the data chain. When the consistency conflict intensity value is below a threshold, the continuity and reliability of the trajectory splicing results are guaranteed, and trusted node relationships are quickly solidified, laying the foundation for subsequent data chain generation. When a conflict interval is detected, by dynamically adjusting the analysis window, strictly eliminating abnormal nodes, and freezing the key business processes of the corresponding waybills, the system can significantly reduce the interference of abnormal data on the settlement and fulfillment processes, and promptly supplement missing information, achieving full-process control and archiving of conflict data, providing a traceable basis for subsequent risk review and strategy optimization.

[0036] Specifically, the method for constructing a segment trajectory confidence model by integrating multi-source heterogeneous behavior, business constraints, historical correlations, and sequence patterns, and outputting multi-granularity confidence labels, involves the following steps: Inputting time behavior feature vectors, spatial path feature vectors, business constraint feature vectors, source behavior feature vectors, historical similar segment matching data, and consistent conflict intensity value sequences within the conflict period of the waybill, a multi-dimensional trajectory evaluation feature sequence covering the same observation time window is constructed. Through sliding window feature extraction, multi-source feature fusion, and dynamic feature filtering, a basic evaluation result consisting of time offset score, spatial detour score, business violation score, and source deviation score is first calculated based on the road network topology, capacity operation rules, and service timeliness rules. Then, a data-driven framework with a tree model and deep network overlay structure as its core is used to perform nonlinear mapping on the basic evaluation result to form a segment trajectory confidence model. During the training phase, the model is developed from historical undisputed waybills. Confirmed trajectory segments are extracted as positive samples, and segments with conflict markers are extracted as negative samples from the conflict sample table. Supervision labels are configured for the confidence values ​​of each trajectory segment, and a target loss based on cross-entropy is constructed. The internal parameters of the segment trajectory confidence model are iteratively adjusted through a batch gradient update strategy, and new samples are introduced in a rolling manner over time to complete incremental training, so that the segment trajectory confidence model's ability to identify conflict patterns is continuously strengthened. The segment trajectory confidence model adjusts the feature sensitivity and internal parameters of the segment trajectory confidence model according to the change of consistent conflict intensity value and the distribution of historical conflict samples. The model calculates and outputs the confidence value of a single trajectory segment and the corresponding risk marker. Segments with a confidence value greater than the confidence threshold are written into the stable segment queue and spliced ​​into candidate trajectory chains in chronological order. Segments with a confidence value less than or equal to the confidence threshold are written into the conflict segment queue and accompanied by the original features and conflict markers to form training samples.

[0037] In this implementation plan, a multi-dimensional trajectory evaluation feature sequence covering the conflict time period is constructed by integrating time behavior feature vectors, spatial path feature vectors, business constraint feature vectors, source behavior feature vectors, historical similar segment matching data, and consistent conflict intensity value sequences. Then, a segment trajectory confidence model is used to complete the comprehensive scoring and risk labeling of each trajectory segment, so that time regularity, spatial form, business constraints, and source credibility are transformed into segment trajectory confidence values ​​on a unified scale. This allows for the simultaneous generation of a stable segment queue for splicing candidate trajectory chains and a conflict segment queue for continuous learning within the same process, providing a structured and quantifiable input basis for subsequent joint optimization of candidate chains and version robustness review.

[0038] Specifically, the robust end-to-end decision-making measures guided by multi-granularity confidence markers and fluctuation convergence are as follows: The number of candidate trajectory chain segments is obtained by counting the number of segments in each candidate trajectory chain during the generation process. This number is used as a length normalization benchmark to ensure comparability of different candidate trajectory chains under the same criteria. The trajectory confidence value of each segment in the candidate trajectory chain and the total number of segments are obtained. The trajectory confidence value is used as the segment reliability input, and the total number of segments is kept consistent with the number of candidate trajectory chain segments to constrain the subsequent accumulation range. The reciprocal of the trajectory confidence value of each segment is taken to obtain the reciprocal confidence term, increasing the proportion of segments with low trajectory confidence values ​​in subsequent accumulation, thereby explicitly amplifying uncertain segments as the main source of influence. The natural logarithm of the reciprocal confidence term is taken to obtain the logarithmic reciprocal term, using logarithmic transformation to compress the scale and widen the tail. To mitigate the influence of a few extreme reciprocal terms on the cumulative results, the following steps are taken: First, the logarithmic reciprocal term is squared to obtain a logarithmic square term. This square operation transforms the deviation into an energy-based cumulative term, resulting in a more significant penalty for candidate trajectory chains with unevenly distributed confidence values ​​during the cumulative process. Second, the logarithmic square terms of all segments are summed to obtain a sum of squares, converging the unstable contributions of each segment into a link-level cumulative quantity. Third, the sum of squares is divided by the number of candidate trajectory chain segments to obtain a mean term, eliminating the bias caused by differences in the number of candidate trajectory chain segments. Fourth, the mean term is negative, converting the unstable cumulative quantity into an exponential input in the same direction as stability. Fifth, an exponential operation is performed on the negative mean term to obtain the link stability value, causing the link stability value to monotonically decay as the mean term increases and remain within the range of zero to one. This provides a consistent quantitative basis for candidate trajectory chain selection, segment replacement, boundary repair, and main chain locking.

[0039] The specific calculation method for the link stability value is as follows: ; In the formula, The link stability value of the candidate trajectory chain reflects the overall stability of the entire waybill trajectory in terms of the spatiotemporal confidence distribution of the segments; L represents the number of segments of the candidate trajectory chain, reflecting how many consecutive segments the chain is divided into under the constraints of temporal order and spatial connectivity. This represents the confidence value of the segment trajectory with index i in the candidate trajectory chain.

[0040] In this implementation scheme, the trajectory confidence values ​​of each segment within the candidate trajectory chain are uniformly aggregated into a stability characterization at the link level. This makes the candidate trajectory chain exhibit a more significant decrease in stability when there are low-confidence segments, uneven confidence distribution, or local amplification. Thus, segment-level uncertainty is transformed into a link-level criterion that can be directly used for screening and adjudication. This facilitates the rapid differentiation between acceptable links and links to be dealt with when multiple candidate trajectory chains coexist. It also provides a unified quantitative basis and triggering conditions for subsequent candidate shrinkage, segment replacement, boundary repair, sub-chain reorganization, and main chain locking.

[0041] Specifically, the process involves candidate shrinking, replacement and repair, and reorganization searches to lock the main chain and retain local backup measures, such as: Figure 3 This is a flowchart of the stability assessment and decision-making process for waybill trajectory chains in this embodiment. By comparing the link stability value and the stability threshold in real time, if the link stability value is less than the stability threshold, the corresponding candidate trajectory chain is determined to be an unstable link. In this case, instead of directly extracting a unique main chain, the set of candidate segments for the same waybill is expanded from the set of segments within the current conflict interval to the set of segments covering the conflict interval and n candidate windows before and after it. The system generates a segment candidate queue by sorting the segment trajectory confidence values ​​in descending order within the conflict interval, and selects the front end of the segment candidate queue to perform segment replacement and boundary repair. During this process, the candidate chain is split into several sub-chains based on the neighborhood of low-confidence segments. Each sub-chain must satisfy mutual exclusion constraints and connectivity constraints. The former is defined as the segments of each sub-chain in the same time period not overlapping in the time interval and not intersecting in the spatial path. The latter is defined as the segments in the sub-chain being continuous in time and having no breaks in the spatial path. After the splitting is completed, a depth-first traversal is performed on each sub-chain, prioritizing the combination of segments with higher confidence values ​​and satisfying the above constraints, and then global reorganization is performed. Afterwards, the link stability value is recalculated. If it is still less than the stability threshold, the candidate chain is discarded, the waybill is marked as pending verification, and the pending verification mark is output. At the same time, the main chain structure of the previous version remains unchanged, and the candidate chain, the reason for rejection, and the processing procedure are completely written into the conflict sample table. If the link stability value is greater than or equal to the stability threshold, the candidate trajectory chain is determined as an acceptable link, the chain segment sequence is locked as the main chain skeleton, segments that have time conflicts, spatial conflicts, or business stage conflicts with the main chain are cleaned up, and only a preset number of m adjacent replacement segments of the main chain are retained as local candidates. A candidate chain list is generated simultaneously, and the main chain structure and link stability value are written into the robustness review process of subsequent versions.

[0042] This implementation scheme achieves dynamic judgment and structural optimization of the overall stability of candidate trajectory chains. By introducing a real-time comparison mechanism of link stability values, it can promptly identify link patterns with abnormal confidence distribution, discontinuous segment connections, or local conflicts. When insufficient link stability is detected, the system automatically expands the range of candidate segments and prioritizes segment replacement and boundary repair based on confidence values, effectively improving the coherence and usability of the trajectory chain. Using mutual exclusion and connectivity constraints, it ensures spatiotemporal consistency and structural closure during sub-chain splitting and reorganization, achieving multi-level segment optimization and conflict avoidance. If multiple rounds of optimization still fail to meet the standards, the system automatically retains the historical stable chain structure and generates a check mark, providing support for subsequent manual review and risk control. If the link stability reaches the stability threshold, it can promptly solidify the trusted main chain, streamline redundant segments, and promote the high-quality evolution of multi-dimensional data chains.

[0043] Specifically, the measures for conducting version robustness reviews guided by version continuity and the impact of changes are as follows: After the incremental update, the new main chain is subjected to a full-link robustness adjudication process to obtain the stability of the new main chain. When the main chain is confirmed, the stability of the new main chain, along with the waybill number, main chain version number, main chain fragment index list, and version build time, is written into the version evolution record table. The version evolution record table maintains the main chain evolution timeline and link stability history sequence for each waybill, providing a data foundation for subsequent stability comparison and version tracing; by comparing the old and new main chain fragment indexes... The number of replaced segments and the total number of main chain segments are statistically obtained, and the update ratio of the main chain is calculated accordingly. The stability of the original main chain is obtained by retrieving the link stability record of the previous main version in the version evolution record by waybill number. The stability difference term is obtained by subtracting the stability of the original main chain from the stability of the newly added main chain. The stability difference term is squared to obtain the stability difference square term. The update ratio is squared to obtain the update ratio square term. The square root of the sum of the stability difference square term and the update ratio square term is obtained to obtain the comprehensive deviation term. The version robustness value is obtained by subtracting the comprehensive deviation term from one.

[0044] The specific calculation method for version robustness is as follows: ; In the formula, V represents the version robustness value of the multi-dimensional data chain of the waybill, which reflects whether the version structure remains stable under the combined effect of the change in the stability of the old and new main chains after the incremental data is written and the coverage of this modification. This indicates the overall stability of the main chain after the introduction of new main chain stability, reflecting the overall stability of the main chain in the fragment confidence distribution after the incremental update; R represents the stability of the original main chain, reflecting the overall stability of the main chain in terms of fragment confidence distribution before the version switch; R represents the updated main chain proportion, reflecting the coverage of this update in terms of timeline and link structure.

[0045] In this implementation plan, by writing the stability of the newly added main chain, the changes in the old and new main chain segments, and the waybill number after each incremental update of the main chain into the version evolution record table, and calculating the updated main chain ratio and version robustness value based on this, this step achieves a quantitative characterization and continuous recording of the impact range and stability changes of the main chain structure adjustment. This provides a unified measurement standard for subsequent version retention, freezing, and review, thereby achieving the effect of keeping the evolution process of the multi-dimensional data chain of waybills controllable and traceable under multiple rounds of incremental updates. It effectively solves the problems of lack of quantitative evaluation basis for main chain version changes and difficulty in tracing responsibility due to arbitrary version switching in the existing technology.

[0046] Specifically, the implementation of candidate version retention, partial review, and controlled recalculation to achieve continuous adaptive optimization and stability assurance involves the following measures: Real-time comparison of version robustness values ​​and robustness thresholds; when a version robustness value is less than the robustness threshold, the newly constructed data chain is registered as a candidate version without replacing the current master version; a version risk marker is applied to all involved waybills; the time interval, number of segments, and link stability changes involved in this increment are recorded; a candidate version list is formed according to the waybill number; and the candidate version list is written into the controlled recalculation task queue based on the version risk marker, time interval, number of segments, and link stability changes. Stability change information is sorted and assigned, with limits on queue capacity, number of concurrent tasks, number of candidate windows covered by a single operation and single-round recalculation, and calculation duration per round of recalculation. When resource limits are reached, the current round of recalculation is terminated while retaining the candidate version status. Within conflict intervals, the output stable fragment list and candidate list are reloaded, and the fragment splicing results are re-examined in chronological and spatial order. For disputed intervals, supplementary recording tasks are issued to fill in missing scan records and mark the supplementary recording completion. This triggers a local recalculation only for disputed intervals. The recalculation process reuses confirmed fragments and baseline matching pairs in the stable fragment list, only applying... The splicing boundary associated with the replaced segment and adjacent segments are recalculated for link stability and version robustness value is recalculated. Settlement freeze flags and performance confirmation freeze flags for disputed versions are generated. The frozen state is maintained by controlling settlement interface calls and process engine state transitions. A suspended state is maintained as long as the number of recalculations does not exceed the upper limit N and the version robustness value is still less than the robustness threshold. The suspended flag is lifted when the version robustness value rises to greater than or equal to the robustness threshold after recalculation. Here, the upper limit N represents the maximum cumulative number of recalculation cycles allowed to be triggered under the same waybill number and disputed interval in the candidate version state. Values ​​range from 1 to 5; generally, 3 is used; 2 is used if the number of candidate windows covered by the disputed interval does not exceed one-third, 3 is used if it exceeds one-third but does not exceed two-thirds, and 4 to 5 is used if it exceeds two-thirds; 1 to 2 is used when the concurrency limit is limited; when the version robustness value is greater than or equal to the robustness threshold, the newly built data chain is identified as a robust version, promoted to the new main version, and the version reference is updated synchronously so that subsequent queries and statistics uniformly use the latest main version data chain. At the same time, the original main version, along with the version number and construction time, the list of difference fragments involved in this version switch, and the corresponding version robustness value are archived in the version evolution record table.

[0047] In this implementation plan, by introducing a comparison between version robustness values ​​and robustness thresholds, the version switching after incremental updates is transformed from direct overwrite to a controllable migration that can be rolled back. When version robustness is insufficient, the new link is locked as a candidate version and a risk marker and difference list are attached. This drives the stable segment list and the candidate chain list to perform supplementary recording and partial recalculation within the disputed interval. At the same time, the upper limit of the number of recalculations and the concurrency constraints of the task queue achieve a resource-controlled recalculation closed loop. Furthermore, the settlement freeze marker and the performance confirmation freeze marker form a verifiable technical block for external interface calls and process state migration, preventing unstable versions from entering the subsequent query statistics and settlement links. When version robustness meets the requirements, the main version is upgraded and the version reference is switched, and the version evolution information is archived. This achieves the stability guarantee of the version evolution process, the traceable repair of the disputed interval, and the continuous maintenance of the consistency of the main version.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0049] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification, characterized in that, Includes the following steps: S1 completes the multi-source collection and unified indexing of waybill entities, trajectory nodes, source profiles and historical samples, and extracts time statistics, spatial statistics and business elements into standardized feature vectors; S2, based on the spatiotemporal similarity measurement and structural coupling feature analysis of windowed segmentation, performs multidimensional consistency discrimination on trajectory segments, realizes dynamic interval division and conflict feature localization, and drives the screening and classification of conflict intervals; S3 integrates multi-source heterogeneous behavior, business constraints, historical associations and sequence patterns to construct a fragment trajectory confidence model and outputs multi-granularity confidence labels; S4 performs robust decision-making across the entire chain, guided by multi-granularity confidence markers and volatility convergence, and performs candidate shrinkage, replacement repair and reorganization search to complete the main chain locking and retain local backup. S5 conducts version robustness reviews based on version continuity and the impact of changes, performs candidate version retention, partial review, and controlled recalculation, and achieves continuous adaptive optimization and stability assurance.

2. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for completing the multi-source collection and unified indexing of waybill entities, trajectory nodes, source profiles, and historical samples are as follows: Collect waybill entity and business constraint data: Read the waybill creation time, task completion time, origin and destination network codes, geofence, product type, service time limit, carrier identifier, and vehicle license plate number through the waybill management platform. Bind these fields to the waybill's unique identifier to directly construct a business constraint feature vector. Collect trajectory and node data: Obtain vehicle location time sequence, driver check-in and loading / unloading site operation time sequence, and road passage and refueling station time nodes through the vehicle monitoring platform, driver terminal, station scanning device, road passage platform, and energy supply platform. Link each node to the waybill identifier and origin. After binding the channel identifier, node type, and timestamp, the data is written into the trajectory node record according to the waybill and time sequence to construct the trajectory node time sequence; collect source behavior and quality data: statistically analyze the missing field ratio, data delay times, historical supplementation times, and disputed node records of each data source to generate source profile features and bind source identifiers; collect historical trajectory samples and version evolution data: extract the node time interval distribution and road network shortest distance distribution from undisputed historical waybills, establish a time scale and spatial scale reference table, and record the characteristics of historical trajectory segments and changes in main chain stability according to waybill number.

3. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for extracting time statistics, spatial statistics, and business elements into standardized feature vectors are as follows: The candidate window length is obtained by dividing the time sequence of trajectory nodes into time intervals with a fixed time length and a sliding step size, and calculating the difference between the start time and the end time of each time interval. The average time offset is obtained by comparing the timestamp of the source node with the time of the corresponding reference node in the historical sample within the same candidate window and averaging the time difference. The time scale is obtained by extracting representative values ​​from the distribution of time intervals between adjacent nodes in undisputed historical waybills. The system obtains the average spatial offset by mapping the latitude and longitude of nodes to the road network within the same candidate window and calculating the geodetic distance from the source node to the reference path. It also obtains the spatial scale by extracting representative values ​​from the distribution of the shortest road network distances between adjacent nodes in undisputed historical waybills, and obtains the node overlap ratio by calculating the ratio of the number of successfully matched nodes to the total number of nodes within the same candidate window. Furthermore, it obtains the business constraint feature vector by encoding and concatenating the waybill main field and service time limit field, and obtains historical similar segment matching data by performing similarity searches in historical trajectory samples using time interval sequences and spatial path codes as search keys and extracting key statistics of similar segments. After completing the above data acquisition, it performs linear scaling and zero-mean standardization on each dimension to generate standardized feature vectors, which are then written to the feature cache table. A waybill identifier is generated for each waybill, a node identifier is generated for each trajectory node, and a source identifier and batch identifier are assigned to each source. All timestamps are uniformly converted to a unified time format, and all location data is uniformly converted to a unified coordinate expression. The waybill master record, trajectory node record, source profile record, and version evolution record are associated and stored through the waybill identifier to construct a logistics operation and maintenance database.

4. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for multidimensional consistency judgment of trajectory segments based on the spatiotemporal similarity measurement and structural coupling feature analysis using window-based methods are as follows: Obtain all candidate windows covered by the candidate fragments, and read the average time offset, average spatial offset, time scale, spatial scale and node overlap ratio in each candidate window; For each candidate window, the average time offset is divided by the time scale to obtain the time offset ratio, and the average spatial offset is divided by the spatial scale to obtain the spatial offset ratio. The temporal offset ratio and spatial offset ratio are squared and summed, and the square root of the sum is taken to obtain the comprehensive spatiotemporal offset of the candidate window. The comprehensive spatiotemporal offset is divided by the sum of the node overlap ratio and the smallest positive number to obtain the consistent conflict local intensity of the candidate window. The consistent conflict local intensities of all candidate windows are compared, and the one with the largest value is selected as the consistent conflict intensity value of this candidate segment.

5. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for achieving dynamic interval division and conflict feature localization, and driving the screening and classification of conflict intervals are as follows: By comparing the consistency conflict intensity value and the conflict threshold in real time, when the consistency conflict intensity value is less than the conflict threshold, the corresponding trajectory segment is determined to be a stable trajectory segment and is connected in time order to form a stable segment linked list without changing the existing splicing result; the node matching relationship confirmed in the current time interval is solidified as the benchmark matching pair, and a consistency conflict intensity value sequence is constructed, and the time behavior feature vector, spatial path feature vector and source behavior feature vector are output synchronously. When the consistency conflict intensity value is greater than or equal to the conflict threshold, the trajectory interval is marked as a conflict zone and a local optimization process is triggered: shorten the duration of the conflict center window and reduce the time interval between adjacent windows to increase the overlap between adjacent windows; if the time offset of a candidate trajectory segment exceeds the P25-P75 quantile range of the historical trajectory node interval, or the spatial distance offset exceeds k times the shortest road network distance of the historical samples, it is judged as abnormal and removed to achieve tight matching of nodes; generate a settlement freeze mark for the waybill in the conflict interval and prevent the corresponding automatic settlement interface from being called, and the route planning side abandons the path segment including the conflict zone; synchronously collect trajectory segments and scan records from all data sources in the conflict interval, regenerate the candidate segment set, trigger supplementary collection for the parts of the trajectory segments and scan records with missing data, and recalculate the conflict intensity; if the conflict intensity value is still greater than or equal to the conflict threshold, the trajectory segments and processing process of the conflict interval will be completely written into the conflict sample table.

6. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for constructing a segment trajectory confidence model by integrating multi-source heterogeneous behavior, business constraints, historical correlations, and sequence patterns, and outputting multi-granularity confidence identifiers are as follows: The system inputs time behavior feature vectors, spatial path feature vectors, business constraint feature vectors, source behavior feature vectors, historical similar segment matching data, and consistent conflict intensity value sequences within the conflict period of the waybill. It then constructs a multi-dimensional trajectory evaluation feature sequence covering the same observation time window. Through sliding window feature extraction, multi-source feature fusion, and dynamic feature filtering, it first calculates basic evaluation results composed of time offset scores, spatial detour scores, business violation scores, and source deviation scores based on the road network topology, capacity operation rules, and service timeliness rules. Then, it employs a data-driven framework centered on a tree model and a deep network overlay structure to perform nonlinear mapping on the basic evaluation results, forming a segment trajectory confidence model. This model adjusts feature sensitivity and internal parameters based on changes in consistent conflict intensity values ​​and the distribution of historical conflict samples, calculating and outputting single-segment trajectory confidence values ​​and corresponding risk labels. Segments with confidence values ​​greater than the confidence threshold are written into a stable segment queue and concatenated into candidate trajectory chains in chronological order. Segments with confidence values ​​less than or equal to the confidence threshold are written into a conflict segment queue, along with their original features and conflict labels, forming training samples.

7. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for end-to-end robust adjudication guided by multi-granularity confidence markers and volatility convergence are as follows: The number of candidate trajectory chain segments is obtained by counting the number of segments contained in each candidate trajectory chain during the candidate trajectory chain generation process; the trajectory confidence value of each segment in the candidate trajectory chain and the total number of segments are obtained; the reciprocal of the trajectory confidence value of each segment is taken to obtain the reciprocal confidence term; the natural logarithm of the reciprocal confidence term is taken to obtain the logarithmic reciprocal term. Squaring the reciprocal of the logarithmic term yields the logarithmic square term; The sum of the logarithmic squares of all segments is used to obtain the sum of squares; the sum of squares is divided by the number of candidate trajectory chain segments to obtain the mean; the mean is negative to obtain the negative mean. The link stability value is obtained by performing an exponential operation on the negative mean term.

8. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for performing candidate shrinkage, replacement repair, and recombination search to complete main chain locking and retain local backup are as follows: By comparing the link stability value and the stability threshold in real time, when the link stability value is less than the stability threshold, the corresponding candidate trajectory chain is determined to be an unstable link. The unique main chain is no longer directly extracted from this chain. The candidate segment set of the same waybill is updated from the segment set within the current conflict interval to the segment set within the candidate windows covering the conflict interval and before and after the conflict interval. Within the conflict interval, the segment trajectory confidence value is sorted in descending order to generate a segment candidate queue. The front end of the segment candidate queue is selected to perform segment replacement and boundary repair. The candidate chain is split into sub-chains according to the neighborhood of low-confidence segments. Depth-first search is performed under mutual exclusion and connectivity constraints, and then the sub-chains are reassembled. The link stability value is recalculated. If it is still less than the stability threshold, the candidate chain is discarded, a check mark is output, the previous version of the main chain is retained, the waybill is marked as checkable, and the candidate chain, the reason for discarding, and the processing procedure are written into the conflict sample table. When the link stability value is greater than or equal to the stability threshold, the corresponding candidate trajectory chain is determined as an acceptable link, the segment sequence of this chain is locked as the main chain skeleton, segments that have time conflicts, spatial conflicts, or business stage conflicts with the main chain are cleaned up, and only a preset number m adjacent replacement segments of the main chain are retained as local candidates. A candidate chain list is generated simultaneously, and the main chain structure and link stability value are written into the robustness review process of subsequent versions.

9. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for conducting version robustness reviews based on version continuity and the impact of changes are as follows: After the incremental update, the stability of the new main chain is obtained by applying the full-link robust adjudication process to the new main chain. The number of replaced segments and the total number of main chain segments are obtained by comparing and statistically analyzing the segment indexes of the new and old main chains, and the updated main chain ratio is calculated accordingly. The stability of the original version main chain is obtained by retrieving the link stability record of the previous main version by waybill number in the version evolution record. The stability difference term is obtained by subtracting the stability of the original version main chain from the stability of the new main chain. The stability difference term is squared to obtain the stability difference square term. The squared update ratio term is obtained by square the update main chain ratio. The sum of the squared stability difference term and the squared update ratio term is taken by the square root to obtain the comprehensive deviation term; the version robustness value is obtained by subtracting the comprehensive deviation term from one.

10. The method for automatically constructing a multi-dimensional data chain for logistics waybills based on AI verification according to claim 1, characterized in that: The specific measures for retaining candidate versions, partial review, and controlled recalculation to achieve continuous adaptive optimization and stability assurance are as follows: By comparing the version robustness value and robustness threshold in real time, when the version robustness value is less than the robustness threshold, the newly constructed data chain is registered as a candidate version without replacing the current main version. The entire waybill involved is marked with a version risk mark, and the time interval, number of segments and link stability change information involved in this increment are recorded. A candidate version list is formed according to the waybill number. Within the conflict interval, the output stable segment list and candidate chain list are reloaded, and the segment splicing results are re-examined in chronological and spatial order. For the disputed interval, a supplementary recording task is issued. After the missing scan records are supplemented, the link stability is recalculated locally in the relevant interval and the version robustness value is recalculated. Settlement freeze mark and performance confirmation freeze mark for disputed versions are generated. The frozen state is maintained by controlling the settlement interface call and process engine state migration. The suspension state is maintained when the number of recalculations does not exceed the upper limit N and the version robustness value is still less than the robustness threshold until the version robustness value rises to greater than or equal to the robustness threshold after recalculation. When the version robustness value is greater than or equal to the robustness threshold, the newly built data chain is identified as a robust version and promoted to the new major version. The version reference is updated synchronously so that subsequent queries and statistics uniformly use the latest major version data chain. At the same time, the original major version, along with the version number and build time, the list of differences involved in this version switch, and the corresponding version robustness value, are archived in the version evolution record table.

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