Intelligent disposal method and system for ETC digital auditing under road network linkage

Abstract

The application discloses an ETC digital auditing intelligent disposal method and system under road network linkage, comprising the following steps: collecting multi-road network ETC passing, equipment operation and topological correlation data, screening an abnormal data set through cross-road network abnormal clustering discrimination, forming a task queue based on hierarchical auditing priority sequencing, constructing a clear feature label by using a fee evasion behavior portrait, integrating and checking a complete evidence chain through an auditing evidence chain intelligent management platform, and finally pushing a disposal instruction and synchronizing state data through a road network linkage mechanism. The abnormal discrimination, priority sequencing and portrait construction links all include implementation steps, realizing the full-process intelligentization of multi-road network data cooperation, abnormal accurate identification, ordered auditing disposal and complete evidence chain construction, effectively improving the accuracy and efficiency of cross-road network ETC auditing, and adapting to the auditing management needs of large-scale cross-regional ETC passing.

Description

Technical Field

[0001] This invention relates to the field of ETC digital auditing technology, and in particular to an intelligent processing method and system for ETC digital auditing under road network linkage. Background Technology

[0002] With the continuous expansion of the expressway network and the significant increase in cross-regional ETC traffic, the need for multi-network collaborative management is becoming increasingly urgent. Traditional auditing models, relying on single-network data collection and manual judgment, are ill-suited to the complexity and dynamism of cross-network traffic. In current ETC auditing scenarios, factors such as cross-correlation of vehicle travel paths, differences in equipment operating status, and complex road network topology result in scattered and weakly correlated abnormal traffic data. Auditing work faces challenges such as difficulty in data integration and delayed response. There is an urgent need to build a digital intelligent handling solution that takes into account cross-network data collaboration, accurate anomaly identification, priority ranking, and evidence chain integration to achieve efficient, accurate, and intelligent ETC auditing under multi-network linkage.

[0003] Existing technologies have two core drawbacks: First, they lack an anomaly detection mechanism that deeply correlates data across road networks. They rely solely on single road network data or simple thresholds to determine anomalies, failing to fully integrate the topological characteristics of multiple road networks, equipment operating status, and traffic trajectory patterns. This results in a high rate of missed and false positives for anomaly data, making it difficult to accurately locate cross-road network toll evasion. Second, the audit process lacks a systematic prioritization and evidence chain integration capability. It has not established a dynamic prioritization mechanism that matches the scope of road network impact and the allocation of disposal resources. Furthermore, evidence data is stored in a scattered manner with weak logical connections, making it impossible to form a complete and effective audit evidence chain. At the same time, there is a lack of efficient linkage and disposal channels between road networks, leading to an accumulation of audit tasks and low disposal efficiency, making it difficult to meet the actual needs of large-scale cross-road network ETC audits. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides an intelligent processing method and system for ETC digital audit under road network linkage.

[0005] The technical solution adopted in this invention is an intelligent processing method for ETC digital audit under road network linkage, including the following steps: S1, collecting original ETC passage data, equipment operation status data, and road network topology correlation data from multiple road network nodes, and extracting passage timestamps, entrance and exit identifiers, vehicle unique identification codes, path trajectory features, and toll-related data; S2, performing correlation analysis on the collected data through a cross-road network anomaly clustering discrimination algorithm, filtering out abnormal data sets that deviate from normal passage patterns, and determining the road network segments and associated passage records corresponding to the abnormal data; S3, based on a hierarchical audit priority ranking algorithm, combining the severity of abnormal data, the scope of road network impact, and data... The credibility parameter prioritizes and sorts the abnormal data set to form a hierarchical audit task queue; S4, a model is built using toll evasion behavior profiling to extract features and construct profiles for the prioritized abnormal data, determining the behavioral feature labels and associated feature dimensions corresponding to various types of abnormal data; S5, the audit evidence chain intelligent management platform integrates and verifies the abnormal data, behavioral profiles, and associated passage records to form a complete audit evidence chain; S6, based on the road network linkage mechanism, the audit evidence chain and handling instructions are pushed to the corresponding road network management nodes to execute digital audit handling operations and simultaneously update the multi-road network linkage audit handling status data.

[0006] Furthermore, the cross-network anomaly clustering discrimination algorithm in S2 adopts the following expression: ,in, This is the discriminant value for cross-network anomaly clustering. Let be the weight coefficients for the i-th class of data. For the actual collected value of the i-th type of data, For the i-th type of data, the normal reference value for the road network is... For the number of connections to the road gateway, Let be the influencing factor of the topology parameters of the j-th type of road network. Let be the correlation deviation value between the i-th type of data and the j-th type of topological parameters. For the stability parameters of road network node connections, This is the amplification factor. The identification factor for the k-th type of abnormal feature. Let be the matching degree of the k-th class of abnormal features in the i-th class of data. This represents the total number of categories of abnormal features. This represents the total number of categories of road network topology parameters.

[0007] Furthermore, the hierarchical audit priority sorting algorithm in S3 adopts the following expression: ,in, This is a quantified value for audit priority. Adjust the coefficient for priority. Let be the weight of the severity of the t-th type of anomaly. The impact score for the type t anomaly. Let be the importance coefficient of the s-th road network segment. Let be the abnormal correlation degree of the s-th road network segment. Let v be the resource occupancy coefficient. The available amount of resources for disposal of type v. The total amount of abnormal data. q represents the total amount of traffic data, r represents the total number of abnormal types, and w represents the total number of road network segments.

[0008] Furthermore, the fare evasion behavior profiling model in S4 adopts the following expression: Portrait Here, Portrait is a set of vector images representing fare evasion behavior. These are the weighting coefficients for each portrait dimension. For feature mapping functions of different portrait dimensions, These are the feature parameters corresponding to each portrait dimension. For road network association operators, This is the feature matrix of road network linkage.

[0009] Furthermore, the evidence chain integration and verification of the audit evidence chain intelligent management platform in S5 adopts the following expression: ,in, To complete the audit evidence chain, For the i-th type of abnormal data set, Let i be the set of road network associated records corresponding to the i-th type of data. Let i be the set of authenticity verification results for the i-th type of data. Let be the validity coefficient of the j-th type of evidence. Let be the matching verification value between the i-th type of data and the j-th type of evidence, n be the total number of abnormal data categories, and m be the total number of evidence categories.

[0010] Furthermore, the road network linkage handling instruction push in S6 adopts the following expression: ,in, This is the final set of processing instructions to be pushed out. Let be the priority coefficient of the i-th type of disposal instruction. For the content of the i-th type of disposal instruction, For the execution urgency parameter of the i-th type of instruction, Let j be the response weight of the j-th road network management node. Let be the current load parameter of the j-th road network management node. For road network interconnection communication matrix, is the command synchronization coefficient, n is the total number of categories of handling commands, and m is the total number of road network management nodes.

[0011] Further, S3 includes the following sub-steps: S31, extracting traffic deviation, road network coverage, and data correlation strength parameters corresponding to various anomalies in the abnormal data set, establishing a multi-dimensional priority evaluation index system, and determining the quantitative standards and correlation relationships of each index; S32, inputting the extracted evaluation index parameters into the hierarchical audit priority ranking algorithm, dynamically assigning weights to each index through the algorithm's built-in weight allocation mechanism, and generating an index weight vector; S33, based on the index weight vector and the actual parameters of the abnormal data, obtaining the preliminary priority score for each abnormal data through algorithm calculation, forming an initial priority ranking result; S34, combining the road network linkage handling resource allocation and historical audit handling feedback data, dynamically adjusting the initial priority ranking result, and finally forming an orderly hierarchical audit task queue.

[0012] Further, step S4 includes the following sub-steps: S41, extracting vehicle travel path features, toll difference features, time matching features, and road network node interaction features from the prioritized abnormal data to determine the calibration feature dimensions of the toll evasion behavior profile; S42, standardizing the extracted calibration feature dimensions to construct a feature vector space and determining the value range and association rules of each feature dimension; S43, using the toll evasion behavior profile construction model to calculate the feature parameters in the feature vector space and generate a preliminary behavior profile label corresponding to each abnormal data; S44, combining historical toll evasion behavior data from multiple road networks and profile matching rules to optimize and adjust the preliminary behavior profile label to form an accurate toll evasion behavior profile.

[0013] Further, S5 includes the following sub-steps: S51, collecting the original passage records, equipment operation logs, and road network environment data corresponding to the abnormal data through the audit evidence chain intelligent management platform to establish an evidence data pool; S52, classifying and organizing the data in the evidence data pool, grouping them according to time sequence, logical association, and evidence type, and determining the association relationship of each group of data; S53, verifying the authenticity and completeness of the grouped evidence data based on evidence association rules and verification algorithms, and eliminating invalid and conflicting data; S54, integrating the verified evidence data according to the audit logic order to form a logically rigorous and data-complete audit evidence chain, and storing it in a designated database.

[0014] The intelligent processing system for ETC digital audit under road network linkage applies intelligent processing methods for ETC digital audit under road network linkage. It includes: a multi-road network data collaborative collection unit, a cross-road network abnormal data clustering and discrimination unit, a hierarchical audit priority dynamic sorting unit, a multi-dimensional profiling unit for toll evasion behavior, an intelligent integration and management unit for audit evidence chains, and a road network linkage processing instruction push and execution unit. The multi-road network data collaborative collection unit and the cross-road network abnormal data clustering and discrimination unit are bidirectionally connected for collecting and transmitting multi-road network ETC passage and related data. The cross-road network abnormal data clustering and discrimination unit is electrically connected to the hierarchical audit priority dynamic sorting unit for... The system collects data, identifies anomalies, and outputs an abnormal data set. The hierarchical audit priority dynamic sorting unit connects with the multi-dimensional profile construction unit for toll evasion, completing the priority sorting of abnormal data and transmitting it to the profile construction unit. The multi-dimensional profile construction unit for toll evasion interacts with the intelligent integration and management unit for audit evidence chains, outputting a behavioral profile to the evidence chain integration unit. The intelligent integration and management unit for audit evidence chains communicates with the road network linkage disposal instruction push and execution unit, generating a complete evidence chain and pushing disposal instructions. The road network linkage disposal instruction push and execution unit forms a feedback connection with the multi-road network data collaborative collection unit, executing disposal operations and synchronously updating status data.

[0015] Beneficial Effects: This invention proposes an intelligent handling method and system for ETC digital auditing under road network linkage. Based on the collaborative collection of multi-road network data, it utilizes a cross-road network anomaly clustering and discrimination mechanism to deeply integrate traffic data, equipment status, and road network topology correlation features, breaking the limitations of single road network data and accurately identifying cross-regional abnormal traffic behavior, thus solving the problems of missed and misjudged anomalies in traditional technologies. Through a hierarchical audit priority dynamic sorting mechanism, it quantitatively sorts the anomalies based on their severity, the scope of road network impact, and the allocation of handling resources, avoiding the disorderly accumulation of audit tasks and improving handling efficiency. By constructing a multi-dimensional profile of toll evasion behavior, it accurately extracts abnormal behavior feature tags, providing a reliable basis for audit judgment. Through intelligent integration and management of audit evidence chains, the system sorts out related data to form a complete logical chain, making up for the shortcomings of traditional evidence being scattered and weakly correlated. The road network linkage handling instruction push execution unit realizes multi-road network collaborative response, ensuring accurate transmission of handling instructions and status synchronization. The overall solution constructs a fully intelligent system encompassing data collection, anomaly detection, priority ranking, profile building, evidence integration, and coordinated handling, achieving precise, efficient, and systematic cross-road network ETC auditing. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0017] Figure 2 This is a flowchart of method step S3 of the present invention;

[0018] Figure 3 This is a flowchart of method step S4 of the present invention;

[0019] Figure 4 This is a flowchart of step S5 of the method of the present invention;

[0020] Figure 5 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] like Figure 1 As shown, the intelligent processing method for ETC digital audit under road network linkage includes the following steps: S1, collecting original ETC passage data, equipment operation status data, and road network topology correlation data from multiple road network nodes, and extracting passage timestamps, entrance and exit signs, vehicle unique identification codes, path trajectory features, and billing-related data; S2, performing correlation analysis on the collected data using a cross-road network anomaly clustering discrimination algorithm, filtering out abnormal data sets that deviate from normal passage patterns, and determining the road network segments and associated passage records corresponding to the abnormal data; S3, based on a hierarchical audit priority ranking algorithm, combined with the severity of abnormal data, the scope of road network impact, and data credibility parameters... The process involves: S4, prioritizing and sorting the abnormal data set to form a tiered audit task queue; S5, using a toll evasion behavior profiling model to extract features and construct profiles for the prioritized abnormal data, determining the behavioral feature labels and associated feature dimensions corresponding to various types of abnormal data; S6, integrating and verifying the abnormal data, behavioral profiles, and associated passage records through an intelligent audit evidence chain management platform to form a complete audit evidence chain; and S7, based on the road network linkage mechanism, pushing the audit evidence chain and handling instructions to the corresponding road network management nodes to execute digital audit handling operations and simultaneously update the multi-road network linkage audit handling status data.

[0023] Step S1 involves comprehensive collection of multi-dimensional data and extraction of core features. During implementation, ETC microwave reading and writing devices, laser sensing devices, and topology information collection terminals deployed at provincial, municipal, and road segment nodes of various road networks are used to simultaneously collect raw ETC passage data, device operation status data, and road network topology association data within the coverage area of ​​multiple road networks. The original ETC passage data includes a 15-digit unique code for the entrance toll station, an 8-digit unique code for the exit toll station, a 3-digit lane identifier, millisecond-accurate entrance and exit timestamps, an 18-digit unique vehicle identification code (OBU device serial number bound to the license plate), and records of key nodes passed every 5 kilometers along the passage route. The device operation status data includes the radio frequency signal strength of the ETC reader / writer (-85dBm to -40dBm), data transmission rate (1Mbps to 10Mbps), fault alarm code, and continuous online duration of the device. The collection frequency is set to twice per second to ensure the capture of instantaneous fluctuations in the device. The road network topology association data includes the latitude and longitude coordinates of the start and end points of each road segment, the technical grade code of the road segment, the number of one-way lanes (2 to 16), the design speed (60km / h to 120km / h), the road network node connection matrix, and the communication interface protocol version of adjacent road networks. After data collection, an embedded data filtering module removes garbled, redundant, and non-standard formatted data. It then extracts key data such as passage timestamps, entrance / exit signs, vehicle unique identifiers, path trajectory features (total path length, number of turns, duration of stay at key nodes), and billing-related data (amount due, actual deducted amount, billing rate level, and preferential policy code). This provides comprehensive and accurate foundational data for subsequent steps. This step achieves a data collection completeness rate of over 99.8% and a core feature extraction accuracy rate of over 99.5%, ensuring the accuracy of subsequent algorithm calculations and the efficiency of the processing flow from the source.

[0024] Step S2 utilizes a cross-road network anomaly clustering algorithm to conduct multi-dimensional data correlation analysis and anomaly screening. During implementation, the structured data extracted in Step S1 is first classified into two categories: road network administrative affiliation (provincial, municipal, and road segment levels) and travel time interval (24-hour interval). A data correlation index table is established using a distributed database to clarify the many-to-many mapping relationship between travel, equipment, and topology data. The algorithm operates with reference to a benchmark database of normal road network travel patterns. This benchmark database is constructed based on 10 million valid historical travel data points from the same quarter, time period, and road network segment over the past three years. It includes thresholds such as normal travel time ranges for different vehicle types, probability distribution of common route selection, toll amount fluctuation range, and equipment interaction response time standards. By quantitatively calculating the deviation between the collected data and the benchmark data, and integrating the road network gateway connection count (0.6 to 1.0) and road network node connection stability parameters (0.85 to 1.0, calculated based on the node communication success rate over the past 30 days), the rationality of each travel data point is comprehensively judged. For individual records, the analysis checks whether the travel time exceeds the normal range by ±30%, whether the route selection falls within the top 80% probability range, and whether the billing data is consistent with the route length and rate standard. For cross-network data, the analysis verifies whether there are gaps exceeding 30 minutes in the timestamp connection between records of different sections, whether there are missing fields or abnormal jumps in data transmission, and whether the device identification is consistent. Through the algorithm density clustering function, datasets deviating from the normal pattern are synthesized into anomaly data sets, tracing the corresponding network sections (clearly defining the start and end mileage markers and the affected area), retrieving 3 travel records from upstream and downstream, all travel records of the same vehicle in the past 30 days, and the concurrent operation data of related equipment, forming a multi-level anomaly data correlation map. The anomaly identification accuracy rate reaches over 98%, providing clearly defined and correlated analysis objects for subsequent steps, solving the anomaly identification problem caused by fragmented cross-network data.

[0025] Step S3 uses a hierarchical audit priority ranking algorithm to quantify and sort abnormal data and construct a task queue. During implementation, three core evaluation parameters are defined: severity of abnormal data, road network impact range, and data reliability. The severity of abnormal data is quantified using anomaly deviation (0 to 1.0), potential toll loss (0 to 5000), and whether it involves malicious toll evasion (0 or 1), and is divided into five levels, with losses exceeding 1000 yuan, anomaly deviation ≥ 0.8, and involvement of malicious toll evasion being the highest level. The road network impact range is assessed using the number of road network segments involved (1 to 10), the number of affected lanes (1 to 8), and the traffic congestion risk coefficient (0.3 to 1.0, calculated based on road segment saturation). Areas spanning three or more road network sections, affecting two or more main lanes, and with a congestion risk coefficient ≥0.7 are considered to have a large-scale impact. Data reliability is determined by the reliability level of the data collection equipment (Levels A, B, and C, corresponding to weights of 1.0, 0.8, and 0.5, based on the failure rate over the past 180 days), data transmission error rate (0 to 0.05%), and data consistency (0.8 to 1.0, cross-validation of multi-source data). Data with an error rate below 0.01%, consistency above 98%, and equipment at Level A is considered highly reliable. The algorithm uses the analytic hierarchy process (AHP) to allocate dynamic weights, with anomaly severity accounting for 40%, road network impact range for 35%, and data reliability for 25%. A priority quantification score of 0-100 is obtained through weighted summation (the original indicator data is standardized and mapped to the 0-10 score range). The system categorizes tasks into four priority levels based on scores: Level 1 (emergency handling) for scores of 85 and above, Level 2 (priority handling) for scores of 70-84, Level 3 (routine handling) for scores of 55-69, and Level 4 (delayed handling) for scores below 55. A tiered audit task queue is generated from highest to lowest priority, employing a scheduling mechanism that combines first-in-first-out (FIFO) with priority queue jumping to ensure that high-priority abnormal data is processed within one hour. This step improves task scheduling efficiency by over 40%, enhances the utilization efficiency of audit processing resources, and avoids delays for high-priority tasks.

[0026] Step S4 utilizes a toll evasion behavior profiling model to perform multi-dimensional feature extraction and precise profile construction. During implementation, the abnormal data sorted in Step S3 is used as the analysis object. A feature extraction engine is activated to mine 12 core feature dimensions: vehicle travel path features (path deviation coefficient, lane change frequency, detour ratio, number of short-distance round trips, and deviation duration at key nodes), billing difference features (difference between receivable and actual deductions, applicable rate deviation value, number of duplicate charges, duration of missed charges, and number of times preferential policies are abused), time matching features (matching coefficient between entrance / exit travel time difference and path length, and duration of overstay at road network nodes), and road network node interaction features (frequency of communication with ETC devices, duration of a single communication, data transmission integrity rate, number of device authentication failures, and signal strength fluctuation amplitude). After feature extraction, qualitative features are transformed into quantitative data in the 0-1 range through Min-Max standardization, clearly defining the three-level division range for each feature dimension (e.g., a matching degree of 0-0.3 indicates severe mismatch, 0.3-0.7 indicates moderate match, and 0.7-1.0 indicates perfect match). The model utilizes 500,000 labeled samples from a multi-network historical toll evasion behavior database. Supervised training is performed on the quantified feature parameters, and the feature weight coefficients (totaling 1.0) are iteratively optimized using a gradient descent algorithm. Feature fusion operations generate eight preliminary behavioral profile labels (toll evasion types such as malicious detours, equipment malfunctions, billing system anomalies, and false passage information). The preliminary labels are optimized and adjusted based on profile matching rules. A cosine similarity algorithm is used to compare the similarity between current abnormal data and historical profiles of the same type. If the similarity is higher than 90%, the label is confirmed; if it is lower than 90%, feature extraction and computation are re-verified. If three verifications fail, the profile is marked as "awaiting manual review." Ultimately, a toll evasion behavior profile with an accuracy of ≥95% is formed, clearly identifying the essence and core causes of abnormal behavior, providing accurate evidence for auditing and judgment.

[0027] Step S5 involves integrating, verifying, and storing the evidence chain through the intelligent management platform for audit evidence chains. This begins by activating the platform's distributed data acquisition function. Through API interfaces, database synchronization, and file transfer, it comprehensively collects original passage records (ETC card transaction logs, toll station images, lane controller records), equipment operation logs (ETC reader / writer transaction response logs, billing system calculation logs, communication equipment data stream transmission logs, server operation status logs), and road network environment data (weather conditions during abnormal periods, road construction information, and traffic control information) corresponding to the abnormal data. This data is then categorized and stored in a distributed evidence data pool (supporting retrieval and access of 100,000 data entries per second). Subsequently, the data in the pool is categorized and organized. All data related to the same abnormal event is arranged chronologically. Data with clear causal relationships is grouped by logical association. Data is categorized into three types: electronic, image, and log data. Each group of data is labeled with associated abnormal data identifiers and characteristic information, clarifying the logical relationships between different groups. The verification module is activated to conduct authenticity and integrity checks. Authenticity checks involve verifying data encryption, tracing its source, and cross-validating to check for data tampering, legality of the source, and conflicts with related data. Integrity checks involve verifying whether key data is missing or the data chain is broken, eliminating tampered, conflicting, or missing key information. Finally, the verified evidence data is integrated according to the audit logic, creating a complete evidence chain of "raw data - anomaly detection results - feature extraction data - behavioral profiling - related supporting data." This ensures that each audit conclusion is supported by corresponding evidence, is logically rigorous, and the data is complete. The data is stored in a dedicated database with access permissions and a triple backup mechanism to guarantee evidence security and traceability, thus constructing a complete and effective audit evidence system.

[0028] Step S6 implements digital audit and handling based on the road network linkage mechanism. During implementation, the intelligent management platform for the audit evidence chain first formats the complete evidence chain according to a unified data standard, generating an audit report including basic information on abnormal data, behavioral profile conclusions, an evidence list, and audit judgment results. Based on the road network section and management authority corresponding to the abnormal data, the platform determines the corresponding road network management nodes at the provincial road network management center, road section management office, and toll station management station levels. The audit report and handling instructions are pushed through a road network linkage communication network using a 5G+fiber dual-link transmission mode, ensuring transmission latency is controlled within 1 second and guaranteeing the real-time performance and stability of the instructions. Handling instruction types include toll payment notifications, equipment fault diagnosis, toll system calibration, and vehicle access restrictions, clearly specifying the handling task, execution time limit, and responsible entity information. Upon receiving the instructions, the corresponding road network management node initiates the digital handling process, sending toll payment notifications to users via SMS and APP, handling equipment faults through remote monitoring or on-site investigation, correcting toll system anomalies through remote system calibration or manual debugging, and restricting the access rights of violating vehicles through the road network linkage control system. During the handling process, each node provides real-time feedback on user payment status, equipment repair status, system calibration results, and vehicle control effectiveness, among other progress and outcome data. The platform receives and verifies this feedback data in real time. Finally, based on the handling results, the multi-network coordinated audit handling status data is updated, marking completed anomalies as "completed" and incomplete ones as "in progress." Key data and experience parameters from the handling process are recorded and synchronized to the historical database, providing a reference for handling similar anomalies in the future. Through network-wide coordinated push and closed-loop handling processes, efficient execution and status synchronization of audit handling are achieved, ensuring the synergy and integrity of cross-network audit management.

[0029] Preferably, the cross-road network anomaly clustering discrimination algorithm in S2 adopts the following expression: ,in, This is the discriminant value for cross-network anomaly clustering. Let be the weight coefficients for the i-th class of data. For the actual collected value of the i-th type of data, For the i-th type of data, the normal reference value for the road network is... For the number of connections to the road gateway, Let be the influencing factor of the topology parameters of the j-th type of road network. Let be the correlation deviation value between the i-th type of data and the j-th type of topological parameters. For the stability parameters of road network node connections, This is the amplification factor. The identification factor for the k-th type of abnormal feature. Let be the matching degree of the k-th class of abnormal features in the i-th class of data. This represents the total number of categories of abnormal features. This represents the total number of categories of road network topology parameters.

[0030] Specifically, all calculation parameters are derived from historical data statistics, experimental verification, and real-time status measurements of multiple road networks. Data weighting coefficients are allocated based on the importance of ETC passage timestamps, vehicle unique identification codes, core tolling data, and auxiliary data, and are verified using 100,000 sets of experimental data. Normal reference values ​​for the road network are determined through statistical fitting of ten million valid historical passage data points from the same period and road network segment over the past three years. The number of road-to-gateway connections is calculated based on the average daily data interaction frequency between road networks, with higher values ​​for higher interaction frequencies. Road network topology parameter influence factors are assigned values ​​according to the strength of topological constraints. Road network node connection stability parameters are calculated in real-time based on the node communication success rate over the past 30 days. The anomaly amplification coefficient is determined to have its optimal fixed value through multiple sets of anomaly identification experiments. Anomaly feature identification factors are determined according to their correlation with toll evasion behavior. Anomaly feature matching degrees are dynamically calculated using real-time feature comparison algorithms. All parameters are supported by historical data, experimental data, and real-time monitoring data. The cross-road network anomaly clustering and discrimination algorithm is based on the core requirement of multi-dimensional data correlation analysis. First, it amplifies the difference between abnormal and normal data by calculating the square of the deviation between the actual collected values ​​of various data and the normal reference values ​​of the road network. Then, it introduces the road network connection coefficient to strengthen the mutual influence of cross-road network data. Subsequently, it corrects the interference of different road network topologies on anomaly judgment by calculating the square root of the sum of the squares of the correlation deviation values ​​of road network topology parameters. Finally, it incorporates the product term of the anomaly feature identification factor and the matching degree to improve the sensitivity of identifying specific toll evasion-related anomaly features. The formula is established based on the normal distribution law of cross-road network data, the multi-dimensional manifestation of abnormal behavior, and the strong correlation characteristics of road network topology, ensuring that it captures the overall anomaly trend without missing local key features. The parameter values ​​were verified by 100,000 sets of experimental data. The data weight coefficients were allocated according to the importance of the data, with core data such as passage timestamps and vehicle identification codes having a weight of 0.8 to 0.9, and auxiliary data having a weight of 0.3 to 0.5. The normal reference value for the road network was derived from statistics of 10 million valid historical passage data over the past three years. The number of road-gateway connections was set at 0.6 to 1.0 based on the average daily data interaction frequency between road networks, and 0.9 to 1.0 when the interaction frequency exceeded 100,000 times / day. The influence factor of the road network topology parameter was allocated at 0.4 to 0.9 based on the strength of topological constraints. The node connection stability parameter was calculated based on the communication success rate over the past 30 days, with a value of 0.85 to 1.0, and 1.0 when the success rate was above 99%. The anomaly amplification coefficient was set to a fixed value of 1.2. The anomaly feature identification factor was allocated at 0.5 to 0.9 based on the correlation with toll evasion behavior. The matching degree was calculated at 0 to 1.0 through feature comparison. During implementation, various types of data and corresponding parameters are first collected, and then substituted into the formula to calculate the anomaly clustering discrimination value. The discrimination threshold is set at 0.75. Data higher than this value is judged as anomalous. The anomaly discrimination threshold of 0.75 is not arbitrarily set, but a scientific critical value determined through a large number of data experiments and algorithm verification.This threshold is derived from 100,000 sets of experimental data using a cross-network anomaly clustering discrimination algorithm, combined with statistical fitting of the distribution characteristics of 10 million historical normal passage data and abnormal toll evasion data. By comparing the distribution patterns of anomaly discrimination values ​​between normal and abnormal passage data, a threshold of 0.75 can accurately distinguish between normal passage and abnormal toll evasion behavior, improving the anomaly identification accuracy to over 98%, while minimizing the probability of missed and false positives. It is the optimal threshold for cross-network ETC audit scenarios, with sufficient data experiments and practical application verification. This formula, through multi-parameter collaboration, improves the anomaly identification accuracy to over 98%, accurately capturing the characteristics of cross-network anomaly data, providing a reliable data foundation for subsequent audits, and solving the problem of incomplete anomaly identification in traditional single-network systems.

[0031] Preferably, the hierarchical audit priority sorting algorithm in S3 adopts the following expression: ,in, This is a quantified value for audit priority. Adjust the coefficient for priority. Let be the weight of the severity of the t-th type of anomaly. The impact score for the type t anomaly. Let be the importance coefficient of the s-th road network segment. Let be the abnormal correlation degree of the s-th road network segment. Let v be the resource occupancy coefficient. The available amount of resources for disposal of type v. The total amount of abnormal data. q represents the total amount of traffic data, r represents the total number of abnormal types, and w represents the total number of road network segments.

[0032] Specifically, in step S3, the severity weight of the anomaly is allocated based on five years of historical audit and handling experience, according to the actual impact ratio of core dimensions such as the amount of toll evasion and the impact on the road network; the impact score is determined according to the severity of the anomaly type, combined with toll revenue loss data from historical toll evasion cases, with a score range of 1 to 10 points; the importance coefficient of the road network section is determined based on the average daily traffic volume of the section, the economic value of the road, and the trunk line level of the road network; the anomaly correlation degree is obtained by calculating the matching correlation between the anomaly data and the corresponding road network section through a multi-source data correlation analysis algorithm; the handling resource occupancy coefficient is assigned a graded value according to the actual consumption of handling personnel, equipment computing power, and communication bandwidth; the available handling resources are dynamically obtained through real-time monitoring of the resource utilization rate of road network management nodes; all parameters are calculated and generated by combining historical data, real-time operating status, and industry standards. The hierarchical audit priority ranking algorithm uses the degree of anomaly impact and the urgency of handling as its core logic. It smooths the total score of anomaly severity through logarithmic operations to avoid excessive interference from single extreme anomalies in the overall ranking. Then, it balances the relationship between the magnitude of anomaly impact and the cost of handling by calculating the ratio of the total score of road network impact range to the total score of handling resource consumption. Finally, it introduces a sine function term to quantify the effect of the proportion of anomaly data on priority, making the ranking result more closely reflect the actual needs of handling resource allocation. The formula is based on the principles of audit resource optimization, a multi-dimensional anomaly impact assessment system, and five years of historical handling data feedback, ensuring that the priority ranking reflects both the severity of anomalies and resource utilization efficiency. Priority adjustment coefficients were determined using the analytic hierarchy process (AHP). The severity coefficient was set at 0.4, the network impact range coefficient at 0.35, and the data reliability coefficient at 0.25. Severity weights were allocated based on historical handling experience, with a weight of 0.6 for toll evasion amount and 0.4 for impact range. Impact scores were set from 1 to 10 points based on the type of anomaly, with 8 to 10 points for malicious toll evasion. The importance coefficient for road network sections was determined based on the section's average daily traffic volume and economic value, with 0.8 to 1.0 for sections with over 50,000 traffic trips per day or major economic arterial roads. Anomaly correlation was calculated using data correlation analysis and ranged from 0.3 to 1.0. Resource occupancy coefficients were set from 0.2 to 0.8 based on resource consumption levels. Availability was calculated based on real-time resource status, with higher values ​​used when the utilization rate of personnel, equipment, and other resources was below 60%. The total amount of abnormal data and the total amount of traffic data were actual statistical values. During implementation, various assessment parameters are first collected, standardized, and then entered into the formula to calculate the priority quantification value from 0 to 100 points. The priority is divided into four levels according to the score. 85 points and above are in the emergency handling category, and 80% of the handling resources are allocated to them in priority. This formula improves the efficiency of audit task scheduling by 40%, ensures that high-priority tasks are handled in a timely manner, and improves the overall audit efficiency.

[0033] Preferably, the fare evasion behavior profile construction model in S4 adopts the following expression: Portrait Here, Portrait is a set of vector images representing fare evasion behavior. These are the weighting coefficients for each portrait dimension. For feature mapping functions of different portrait dimensions, These are the feature parameters corresponding to each portrait dimension. For road network association operators, This is the feature matrix of road network linkage.

[0034] Specifically, the weight coefficients of each dimension of the toll evasion behavior profile in step S4 are generated through machine learning algorithms and training with historical data. The weight coefficients of each dimension are determined using the gradient descent algorithm, based on one million labeled samples in a multi-network historical toll evasion behavior database, after 100,000 iterations of training. The weights of travel path features, toll difference features, time matching features, and network node interaction features are repeatedly calculated iteratively based on the correlation and feature discrimination between various features and toll evasion behavior, with the total weight coefficient strictly set to 1.0. This weight allocation scheme has been verified in actual audit scenarios, enabling the toll evasion behavior profile to achieve an accuracy of over 95%, and all weights are derived from algorithm training and data fitting. The model for constructing a toll evasion behavior profile follows the core logic of multi-dimensional feature fusion. First, linear or non-linear mapping operations are performed on the feature parameters of each profile dimension to extract the core feature information of each dimension. Then, the importance of different dimension features is assigned through weight coefficients. Finally, a road network association operator and a road network linkage feature matrix are introduced to enhance the correlation and applicability of features in cross-road network scenarios. The formula is based on the twelve core feature manifestations of toll evasion behavior, the strong collaborative characteristics of road network linkage, and the pattern summary of one million historical profile data, ensuring that the profile can accurately reflect the essence of toll evasion behavior. The weight coefficients for each profile dimension were obtained through 100,000 iterations of gradient descent training. The weights for the travel path feature were set to 0.7 to 0.8, the toll difference feature to 0.6 to 0.75, the time matching feature to 0.5 to 0.6, and the road network node interaction feature to 0.4 to 0.5. The feature mapping function was designed according to the feature type of each dimension. Linear features used linear function mapping, and nonlinear features used exponential function mapping. The feature parameters included fifteen specific indicators, all of which were standardized data. The road network association operator was set to 0.6 to 1.0 according to the road network association strength, and 0.9 to 1.0 for cross-provincial road networks. The road network linkage feature matrix was constructed based on the national road network topology and data interaction rules, including the association information of more than two thousand road network nodes. The implementation process first extracts fifteen feature parameters from the abnormal data, substitutes them into the mapping functions of each dimension, then performs weighted fusion through weight coefficients, and finally performs correlation calculations with the road network linkage feature matrix to generate eight types of toll evasion behavior profile labels. The profile accuracy reaches over 95%. This formula achieves effective integration of multi-dimensional features, provides a clear basis for audit judgment, and solves the problem of fuzziness in traditional profiles.

[0035] Preferably, the evidence chain integration and verification of the audit evidence chain intelligent management platform in S5 adopts the following expression: ,in, To complete the audit evidence chain, For the i-th type of abnormal data set, Let i be the set of road network associated records corresponding to the i-th type of data. Let i be the set of authenticity verification results for the i-th type of data. Let be the validity coefficient of the j-th type of evidence. Let be the matching verification value between the i-th type of data and the j-th type of evidence, n be the total number of abnormal data categories, and m be the total number of evidence categories.

[0036] Specifically, the audit evidence chain integration and verification takes the relevance and validity of evidence as its core logic. First, it uses intersection operations to filter out a valid data set that simultaneously includes abnormal data, road network correlation records, and authenticity verification results, ensuring the core relevance of the data. Then, it introduces the validity coefficients and matching verification values ​​of each piece of evidence through a product term, strengthening the weight of highly credible evidence. The formula is established based on the logical closed-loop rules of the evidence chain construction, the triple verification standard of data authenticity, and the legal relevance requirements of audit evidence, ensuring that the integrated evidence chain is complete, valid, and logically rigorous. In the parameter values, the abnormal data set and road network correlation... The record set consists of structured data collected and organized in practice, with each data entry including twenty core fields. The authenticity verification result set is binary judgment data, which undergoes triple verification through encryption, source tracing, and cross-validation. Qualified data is marked as valid. The evidence validity coefficient is set according to the type of evidence and the reliability of its source: 0.8 to 1.0 for direct evidence such as ETC transaction records and high-definition captured images, and 0.3 to 0.7 for indirect evidence. The matching verification value is calculated through correlation analysis between evidence and abnormal events: 0.9 to 1.0 for directly correlated evidence and 0.5 to 0.8 for indirectly correlated evidence. During implementation, thirty categories of relevant evidence data are collected, categorized into electronic data, image data, and log data. After triple authenticity verification, the data is substituted into a formula to calculate a complete audit evidence chain. Each evidence chain includes no fewer than fifteen key pieces of evidence. This formula enables precise screening and efficient integration of evidence data, eliminating invalid and conflicting data to form a logically rigorous evidence chain, ensuring the legality and accuracy of the audit results and improving the audit success rate.

[0037] Preferably, the road network linkage handling instruction push in S6 adopts the following expression: ,in, This is the final set of processing instructions to be pushed out. Let be the priority coefficient of the i-th type of disposal instruction. For the content of the i-th type of disposal instruction, For the execution urgency parameter of the i-th type of instruction, Let j be the response weight of the j-th road network management node. Let be the current load parameter of the j-th road network management node. For road network interconnection communication matrix, is the command synchronization coefficient, n is the total number of categories of handling commands, and m is the total number of road network management nodes.

[0038] Specifically, the formula for pushing out road network coordinated response instructions follows the core requirements of accurate instruction delivery and efficient execution. It calculates the weighted sum of the priority of the response instructions and the weighted sum of the urgency of execution by the numerator, highlighting the priority of pushing high-priority and high-urgency instructions. Then, it introduces the response weight and current load parameters of the road network management nodes by the denominator to avoid pushing instructions to nodes with excessive load. Finally, it introduces the road network coordinated communication matrix and instruction synchronization coefficient to ensure the reliability of instruction transmission and the synchronization of multiple nodes. The formula is established based on the three-level management architecture of the road network coordination, the performance characteristics of node response, and the real-time requirements of instruction transmission, ensuring that response instructions are accurately and timely pushed to the corresponding nodes. The priority coefficient for handling instructions is set according to the importance of the instructions: 0.7 to 0.8 for supplementary payment notices, 0.6 to 0.7 for equipment inspections, and 0.9 to 1.0 for traffic restrictions. The urgency parameter is set according to the degree of impact of the anomaly: 0.9 to 1.0 when the impact range exceeds three road network sections. The response weight of road network management nodes is determined based on the node's handling capacity: 0.9 to 1.0 for provincial management centers and 0.7 to 0.8 for road section management offices. The current load parameter is obtained through real-time monitoring: 0.8 to 1.0 when CPU utilization is below 50% and 0.3 to 0.5 when it is above 80%. The road network linkage communication matrix is ​​constructed based on node communication protocols and network topology, including communication path information for more than 500 nodes. The instruction synchronization coefficient is set to a fixed value of 0.95. During implementation, the relevant parameters of the disposal instructions and node status parameters are first clarified, and the final set of push instructions is calculated by substituting them into the formula. Through 5G + fiber optic dual-link transmission, the transmission delay is controlled within 1 second. This formula realizes the scientific scheduling and accurate push of disposal instructions, improves the instruction response efficiency by 50%, ensures the efficiency and coordination of road network linkage disposal, and ensures that abnormal events are initiated within 1 hour.

[0039] Preferred, such as Figure 2As shown, step S3 includes the following sub-steps: S31, extracting traffic deviation, road network coverage, and data correlation strength parameters corresponding to various anomalies in the abnormal data set, establishing a multi-dimensional priority evaluation index system, and determining the quantitative standards and correlation relationships of each index; S32, inputting the extracted evaluation index parameters into the hierarchical audit priority ranking algorithm, dynamically assigning weights to each index through the algorithm's built-in weight allocation mechanism, and generating an index weight vector; S33, based on the index weight vector and the actual parameters of the abnormal data, obtaining the preliminary priority score of each abnormal data through algorithm calculation, forming an initial priority ranking result; S34, combining the road network linkage handling resource allocation and historical audit handling feedback data, dynamically adjusting the initial priority ranking result, and finally forming an orderly hierarchical audit task queue.

[0040] Specifically, the hierarchical audit priority ranking process in step S3 achieves accurate sorting of abnormal data and construction of a task queue through four sub-steps. Each sub-step is closely linked and has a clear technical objective. Sub-step S31 first extracts the core evaluation parameters from the abnormal data set, including traffic deviation (calculated as the difference between the collected data and the normal benchmark value, with a value range of 0 to 1.0), road network coverage (statistically counted according to the number of road network sections involved, 1 to 10), and data association strength (calculated through the cross-matching degree of multi-source data, 0.5 to 1.0). Based on these parameters, a multi-dimensional priority evaluation system including 12 specific indicators is established, clarifying the quantitative standards of each indicator and the weight relationship between indicators to ensure that the evaluation dimensions are comprehensive and logically clear. Step S32 inputs the extracted 12 evaluation index parameters into the hierarchical audit priority ranking algorithm. The algorithm has a built-in dynamic weighting mechanism based on the analytic hierarchy process (AHP). According to the differences in anomaly type and road network scenario, it assigns weights of 0.3 to 0.6 to core indicators such as traffic deviation and loss amount, and weights of 0.1 to 0.2 to auxiliary indicators, generating a standardized index weight vector to ensure the scientific nature of the weight allocation. Step S33, based on the index weight vector and the actual parameters of the anomaly data, obtains the initial priority score (0 to 100 points) for each anomaly data through weighted summation. The initial priority ranking is formed according to the scores, using a bubble sort algorithm to ensure efficiency, keeping the processing time for millions of data points within 30 seconds. Step S34, combined with the road network linkage response resource configuration (including the number of personnel, equipment computing power, communication bandwidth, etc., with real-time monitoring and updates every minute) and historical audit response feedback data from the past five years (success rate, response time, etc.), dynamically adjusts the initial ranking result, with the adjustment range controlled within ±5 points, ultimately forming an ordered hierarchical audit task queue. This step is implemented in four detailed stages, which enables the priority ranking accuracy rate to reach over 96%, ensuring that abnormal data with high severity and wide impact are dealt with first, significantly improving the efficiency of audit resource utilization, and solving the problem of lack of dynamic adaptability in traditional ranking methods.

[0041] Preferred, such as Figure 3 As shown, S4 includes the following sub-steps: S41, extracting vehicle travel path features, toll difference features, time matching features, and road network node interaction features from the prioritized abnormal data to determine the calibration feature dimensions of the toll evasion behavior profile; S42, standardizing the extracted calibration feature dimensions to construct a feature vector space and determining the value range and association rules of each feature dimension; S43, using the toll evasion behavior profile construction model to calculate the feature parameters in the feature vector space and generate a preliminary behavior profile label corresponding to each abnormal data; S44, combining historical toll evasion behavior data from multiple road networks and profile matching rules to optimize and adjust the preliminary behavior profile label to form an accurate toll evasion behavior profile.

[0042] Specifically, step S4, the process of constructing a profile of toll evasion behavior, achieves multi-dimensional feature extraction and accurate profile generation through four sub-steps. Sub-step S41 extracts four core feature dimensions from the prioritized abnormal data, including vehicle travel path features (including five specific indicators such as lane change frequency and detour ratio), billing difference features (including four indicators such as deduction difference and rate deviation), time matching features (including three indicators such as dwell time and travel time difference), and road network node interaction features (including three indicators such as communication frequency and signal strength). The collection standards and quantification methods for each type of feature are clearly defined to ensure that the feature dimensions comprehensively cover the key manifestations of toll evasion behavior. Sub-step S42 standardizes the 15 extracted core feature dimensions, using the Min-Max standardization method to map all feature parameters to a value range of 0 to 1.0, constructing a high-dimensional feature vector space. The effective value range of each feature dimension and the association rules between features are clarified, such as the matching rules between travel time difference and path length, and the linkage between toll difference and rate deviation, laying the foundation for subsequent model calculations. Step S43 inputs standardized parameters from the feature vector space into the toll evasion behavior profile model. The model is optimized through a gradient descent algorithm trained over 100,000 iterations, performing weighted fusion and nonlinear mapping on each feature parameter to generate preliminary behavior profile labels. These labels include eight types of toll evasion, such as malicious detours and equipment malfunctions, with each label corresponding to a specific feature threshold range. Step S44 combines a multi-network historical toll evasion behavior database (including one million labeled samples) and preset profile matching rules to calculate the similarity (0 to 1.0) between the current abnormal data features and historical similar profiles. If the similarity is higher than 90%, the label is directly confirmed; otherwise, the process returns to step S41 to re-verify the completeness of feature extraction, ultimately forming an accurate toll evasion behavior profile. This step, through four-stage refinement, improves the profile accuracy to over 95%, clearly defining the essential characteristics of abnormal behavior, providing a reliable basis for audit judgment, and solving the problems of single features and insufficient accuracy in traditional profile construction.

[0043] Preferred, such as Figure 4 As shown, step S5 includes the following sub-steps: S51, collecting the original passage records, equipment operation logs, and road network environment data corresponding to the abnormal data through the audit evidence chain intelligent management platform to establish an evidence data pool; S52, classifying and organizing the data in the evidence data pool, grouping them according to time sequence, logical association, and evidence type, and determining the association relationship of each group of data; S53, verifying the authenticity and completeness of the grouped evidence data based on evidence association rules and verification algorithms, and eliminating invalid and conflicting data; S54, integrating the verified evidence data according to the audit logic order to form a logically rigorous and data-complete audit evidence chain, and storing it in a designated database.

[0044] Specifically, the audit evidence chain integration and verification process in step S5 achieves comprehensive collection, classification, verification, and integration of evidence data through four sub-steps. Sub-step S41 activates the data collection function of the audit evidence chain intelligent management platform. The collection scope includes the original passage records (ETC transaction flow, captured images, etc.) corresponding to abnormal data, equipment operation logs (transaction response time, fault records, etc.), and road network environment data (weather, construction information, etc.). The collection frequency is set to real-time collection, and the data transmission delay is controlled within 1 second. After collection, the data is stored in a distributed evidence data pool, which supports the writing and retrieval of 100,000 data entries per second. Sub-step S52 classifies and organizes the evidence data in the data pool. All data of the same abnormal event are sorted by millisecond-level timestamps according to time order. Data with clear causal relationships are divided into 8 to 12 data groups according to logical association. Data is divided into three categories according to evidence type: electronic data, image data, and log data. Each group of data is labeled with associated abnormal data identifiers and characteristic information, clarifying the logical dependencies between the data groups. Step S53, based on preset evidence relevance rules and verification algorithms, verifies the authenticity and completeness of the grouped evidence data. Authenticity verification employs a triple mechanism of data encryption verification, source tracing, and cross-validation to ensure the data has not been tampered with and its source is legitimate. Completeness verification checks for missing key fields; data with no more than two missing fields is considered valid. Data that has been tampered with, conflicted, or has excessive missing key fields is discarded. The verification pass rate is controlled between 85% and 90%. Step S54 integrates the verified evidence data according to the audit logic sequence of original data, anomaly detection results, feature extraction data, behavioral profiles, and related supporting data, forming a logically rigorous and data-complete audit evidence chain. Each evidence chain includes no fewer than 15 key pieces of evidence. After integration, the evidence is stored in a dedicated database with three levels of access permissions and a triple backup mechanism to ensure the security and traceability of the evidence. This step, through four detailed implementation stages, achieves an evidence chain completeness and validity of over 98%, providing solid evidence support for audit handling and solving the problems of fragmented and weakly correlated traditional evidence management.

[0045] like Figure 5 As shown, the ETC digital audit intelligent processing system under road network linkage is applied to the intelligent processing method of ETC digital audit under road network linkage. It includes: a multi-road network data collaborative collection unit, a cross-road network abnormal data clustering and discrimination unit, a hierarchical audit priority dynamic sorting unit, a multi-dimensional profile construction unit for toll evasion behavior, an intelligent integration and management unit for audit evidence chains, and a road network linkage processing instruction push and execution unit. The multi-road network data collaborative collection unit and the cross-road network abnormal data clustering and discrimination unit are bidirectionally connected for collecting and transmitting multi-road network ETC passage and related data. The cross-road network abnormal data clustering and discrimination unit is electrically connected to the hierarchical audit priority dynamic sorting unit. The system performs anomaly detection on the collected data and outputs an abnormal data set; the hierarchical audit priority dynamic sorting unit is connected to the toll evasion behavior multi-dimensional profile construction unit to complete the priority sorting of abnormal data and transmit it to the profile construction unit; the toll evasion behavior multi-dimensional profile construction unit interacts with the audit evidence chain intelligent integration management unit to output the behavior profile to the evidence chain integration unit; the audit evidence chain intelligent integration management unit communicates with the road network linkage disposal instruction push execution unit to generate a complete evidence chain and push disposal instructions; the road network linkage disposal instruction push execution unit forms a feedback connection with the multi-road network data collaborative collection unit to execute disposal operations and synchronously update status data.

[0046] The intelligent handling method and system for ETC digital auditing under road network linkage forms a closed-loop handling system through multi-dimensional technological innovation. Based on the collaborative collection of data from multiple road networks, it integrates traffic, equipment, and topology-related data to break down data silos within a single road network; it leverages a cross-road network anomaly clustering and discrimination mechanism to deeply mine potential correlations between data and accurately identify cross-regional abnormal behavior; it achieves scientific scheduling of abnormal tasks through a hierarchical audit priority ranking mechanism; it clarifies the essence of abnormal characteristics through the construction of toll evasion behavior profiles; it utilizes an intelligent audit evidence chain management platform to ensure the integrity and validity of evidence; and it achieves multi-node collaborative response through a road network linkage handling mechanism, constructing a full-process intelligent auditing model encompassing collection, discrimination, ranking, profiling, evidence collection, and handling.

[0047] This invention addresses the lack of a deep correlation and discrimination mechanism for cross-network data. By combining multi-network data collaborative collection with anomaly clustering discrimination, it fully integrates multi-dimensional correlation features to replace the traditional single-network or simple threshold judgment mode, significantly reducing the probability of anomaly omissions and misjudgments, and accurately locating cross-network toll evasion behavior. Furthermore, addressing the lack of systematic prioritization and evidence chain integration capabilities in the audit process, it achieves orderly task processing through a dynamic priority ranking mechanism to avoid backlogs. Simultaneously, it leverages intelligent evidence chain integration and management to form a logically rigorous and complete evidence chain, coupled with a network-linked processing channel, solving the problems of scattered evidence and low processing efficiency in traditional technologies, and fully adapting to the actual needs of large-scale cross-network ETC audits.

[0048] In the description of this invention, it should be noted that, unless otherwise specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent processing of ETC digital audit under road network linkage, characterized in that, Includes the following steps: S1 collects raw ETC passage data, equipment operation status data and road network topology association data from multiple network nodes, and extracts passage timestamps, entrance and exit signs, vehicle unique identification codes, route trajectory features and toll-related data. S2, through the cross-road network anomaly clustering discrimination algorithm, performs correlation analysis on the collected data, filters the abnormal data set that deviates from the normal traffic pattern, and determines the road network section and associated traffic records corresponding to the abnormal data; S3, based on the hierarchical audit priority sorting algorithm, combines the severity of abnormal data, the scope of road network impact and data credibility parameters to perform priority quantification sorting of abnormal data sets, forming a hierarchical audit task queue; S4. Use the fare evasion behavior profile to build a model to extract features and build profiles for the priority-ranked abnormal data, and determine the behavioral feature labels and associated feature dimensions corresponding to various types of abnormal data. S5 integrates and verifies abnormal data, behavioral profiles, and related access records through the intelligent management platform for audit evidence chains, forming a complete audit evidence chain. S6, based on the road network linkage mechanism, pushes the audit evidence chain and handling instructions to the corresponding road network management nodes, executes digital audit handling operations, and simultaneously updates the multi-road network linkage audit handling status data.

2. The intelligent processing method for ETC digital auditing under road network linkage as described in claim 1, characterized in that, The cross-road network anomaly clustering discrimination algorithm in S2 uses the following expression: ， in, This is the discriminant value for cross-network anomaly clustering. Let be the weight coefficients for the i-th class of data. For the actual collected value of the i-th type of data, For the i-th type of data, the normal reference value for the road network is... For the number of connections to the road gateway, Let be the influencing factor of the topology parameters of the j-th type of road network. Let be the correlation deviation value between the i-th type of data and the j-th type of topological parameters. For the stability parameters of road network node connections, This is the amplification factor. The identification factor for the k-th type of abnormal feature. Let be the matching degree of the k-th class of abnormal features in the i-th class of data. This represents the total number of categories of abnormal features. This represents the total number of categories of road network topology parameters.

3. The intelligent processing method for ETC digital auditing under road network linkage as described in claim 1, characterized in that, The hierarchical audit priority sorting algorithm in S3 uses the following expression: ， in, This is a quantified value for audit priority. Adjust the coefficient for priority. Let be the weight of the severity of the t-th type of anomaly. The impact score for the type t anomaly. Let be the importance coefficient of the s-th road network segment. Let be the abnormal correlation degree of the s-th road network segment. Let v be the resource occupancy coefficient. The available amount of resources for disposal of type v. The total amount of abnormal data. q represents the total amount of traffic data, r represents the total number of abnormal types, and w represents the total number of road network segments.

4. The intelligent processing method for ETC digital auditing under road network linkage as described in claim 1, characterized in that, The model for constructing a fare evasion behavior profile in S4 uses the following expression: Portrait , Here, Portrait is a set of vector profiles representing fare evasion behavior. These are the weighting coefficients for each portrait dimension. For feature mapping functions of different portrait dimensions, These are the feature parameters corresponding to each portrait dimension. For road network association operators, This is the feature matrix of road network linkage.

5. The intelligent processing method for ETC digital auditing under road network linkage as described in claim 1, characterized in that, The evidence chain integration and verification of the intelligent management platform for audit evidence chains in S5 adopts the following expression: ， in, To complete the audit evidence chain, For the i-th type of abnormal data set, Let i be the set of road network associated records corresponding to the i-th type of data. Let i be the set of authenticity verification results for the i-th type of data. Let be the validity coefficient of the j-th type of evidence. Let be the matching verification value between the i-th type of data and the j-th type of evidence, n be the total number of abnormal data categories, and m be the total number of evidence categories.

6. The intelligent processing method for ETC digital auditing under road network linkage as described in claim 1, characterized in that, The S6 network linkage handling instruction push uses the following expression: ， in, This is the final set of processing instructions to be pushed out. Let be the priority coefficient of the i-th type of disposal instruction. For the content of the i-th type of disposal instruction, For the execution urgency parameter of the i-th type of instruction, Let j be the response weight of the j-th road network management node. Let be the current load parameter of the j-th road network management node. For road network interconnection communication matrix, is the command synchronization coefficient, n is the total number of categories of handling commands, and m is the total number of road network management nodes.

7. The intelligent processing method for ETC digital auditing under road network linkage as described in claim 1, characterized in that, S3 includes the following steps: S31. Extract the parameters of traffic deviation, road network coverage and data correlation strength corresponding to various anomalies in the abnormal data set, establish a multi-dimensional priority evaluation index system, and determine the quantitative standards and correlation relationships of each index. S32, input the extracted evaluation index parameters into the hierarchical audit priority ranking algorithm, and dynamically assign weights to each index through the algorithm's built-in weight allocation mechanism to generate an index weight vector. S33, based on the indicator weight vector and the actual parameters of the abnormal data, the algorithm calculates the preliminary priority score of each abnormal data to form the initial priority ranking result; S34, combining the resource allocation for road network linkage and historical audit feedback data, dynamically adjusts the initial priority ranking results to ultimately form an orderly hierarchical audit task queue.

8. The intelligent processing method for ETC digital auditing under road network linkage as described in claim 1, characterized in that, S4 includes the following steps: S41. Extract vehicle travel path features, toll difference features, time matching features and road network node interaction features from the abnormal data after priority sorting to determine the labeling feature dimensions of the toll evasion behavior profile. S42, standardize the extracted calibration feature dimensions, construct a feature vector space, and determine the value range and association rules of each feature dimension; S43, by constructing a model based on the evasion behavior profile, the model calculates the feature parameters in the feature vector space to generate a preliminary behavior profile label corresponding to each abnormal data. S44 combines historical toll evasion behavior data from multiple networks with profile matching rules to optimize and adjust the initial behavior profile labels, forming an accurate toll evasion behavior profile.

9. The intelligent processing method for ETC digital auditing under road network linkage as described in claim 1, characterized in that, S5 includes the following steps: S51, through the audit evidence chain intelligent management platform, collect the original passage records, equipment operation logs and road network environment data corresponding to abnormal data, and establish an evidence data pool; S52, classify and organize the data in the evidence data pool, group them according to time sequence, logical relationship and evidence type, and determine the relationship between the data in each group; S53, based on the evidence relevance rules and verification algorithms, performs authenticity and integrity verification on the grouped evidence data, and removes invalid and conflicting data; S54 integrates the verified evidence data according to the audit logic order to form a logically rigorous and data-complete audit evidence chain, and stores it in the designated database.

10. A road network-linked ETC digital audit and intelligent processing system, characterized in that: The system is applied to the intelligent processing method for ETC digital audit under road network linkage as described in claim 1, including: a multi-road network data collaborative collection unit, a cross-road network abnormal data clustering and discrimination unit, a hierarchical audit priority dynamic sorting unit, a multi-dimensional profile construction unit for toll evasion behavior, an intelligent integration and management unit for audit evidence chain, and a road network linkage processing instruction push and execution unit. The multi-network data collaborative acquisition unit and the cross-network abnormal data clustering and discrimination unit are bidirectionally connected to collect and transmit multi-network ETC passage and related data. The cross-network abnormal data clustering and discrimination unit is electrically connected to the hierarchical audit priority dynamic sorting unit to perform abnormal judgment on the collected data and output abnormal data sets. The hierarchical audit priority dynamic sorting unit is signal-connected to the toll evasion behavior multi-dimensional profile construction unit to complete the abnormal data priority sorting and transmit it to the profile construction unit. The toll evasion behavior multi-dimensional profile construction unit interacts with the audit evidence chain intelligent integration management unit to output behavior profiles to the evidence chain integration unit. The audit evidence chain intelligent integration management unit is communicatively connected to the road network linkage disposal instruction push execution unit to generate a complete evidence chain and push disposal instructions. The road network linkage disposal instruction push execution unit forms a feedback connection with the multi-network data collaborative acquisition unit to execute disposal operations and synchronously update status data.

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