A precise inspection method and system based on multi-dimensional green pass image

Abstract

The application relates to the computer technical field and discloses a precision inspection method and system based on a multi-dimensional green pass image, aiming to solve the problems of low efficiency, high artificial dependence, inaccurate fee evasion identification and the difficulty in balancing the traffic experience and supervision in the existing green pass vehicle inspection. The method comprises the following steps: acquiring vehicle static information, cargo images, travel paths, historical records and environment perception and other multi-source data; after being structured, cleaned and aligned, a green pass image covering four-dimensional features of vehicles, cargos, travel and behavior is constructed; a pre-trained multi-layer fusion inspection decision model is inputted, and a risk score and an inspection level suggestion are outputted, and the inspection strategies of exemption, image review or opening-box inspection are executed in parallel. Through a cross-dimensional attention mechanism and online incremental learning, high-precision, self-adaptive and intelligent inspection is realized, and the efficiency and fairness are significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of computer technology, specifically relating to a precise verification method and system based on a multi-dimensional green channel profile. Background Technology

[0002] Green channel vehicle traffic management plays an increasingly important role in highway operations. Traditional inspection methods mainly rely on manual visual inspection or fixed-ratio random checks, with their core logic based on on-site intuitive judgment of the goods carried by the vehicles. However, the transportation scenario of green channel vehicles is highly dynamic and complex: the types of goods are diverse, the loading forms are varied, and there are many instances of mixed loading and smuggling of non-cataloged goods to evade regulations. Manual inspection is not only inefficient and highly subjective, but it is also difficult to identify violations that have been disguised or concealed, leading to the dual risks of "exemption when it should be exempted" and "exemption when it should not be exempted." In addition, existing systems generally lack a systematic portrayal of vehicles' historical behavior, traffic patterns, and credit status. The information silos between toll stations are serious, blacklists and graylists cannot be shared in real time, and abnormal vehicles frequently evade inspection at other stations, seriously undermining the fairness and effectiveness of green channel policies.

[0003] Among these, the technological approach of constructing precise profiles of green channel vehicles based on multi-dimensional data fusion has become a key path to improving the intelligence level of vehicle inspection. This approach aims to integrate multi-source heterogeneous data, including BeiDou positioning trajectories, ETC gantry passage records, map road network information, historical inspection results, and credit evaluation systems. Through data correlation and feature extraction, it forms a comprehensive, dynamic, and quantifiable representation of individual vehicles. This type of profile not only reflects the static attributes of vehicles but also depicts their dynamic behavioral patterns, providing a data foundation for differentiated and targeted inspections.

[0004] Existing technologies for green channel inspection still have significant shortcomings: First, data sources are limited to local information from a single passage, lacking historical behavior accumulation analysis across time and space. Second, the profile dimensions are coarse, failing to effectively integrate multi-dimensional features such as location, time, goods, and credit, making it difficult to support refined risk identification. Third, system collaboration capabilities are weak, with data fragmentation between stations within the province, and delayed updates to blacklists and graylists, hindering real-time synchronization and coordinated response to abnormal information. Fourth, inspection strategies are rigid, failing to dynamically adjust sampling priorities based on vehicle profiles, resulting in high missed inspection rates for high-risk vehicles and repeated inspections of low-risk vehicles, making it difficult to balance overall traffic efficiency and regulatory accuracy. These problems are particularly prominent in the current context of surging traffic volume and increasingly covert mixed-cargo methods, urgently requiring a precise inspection method and system based on multi-dimensional green channel profiles to achieve a rapid passage mechanism driven by credit, data collaboration, and intelligent decision-making throughout the province. Summary of the Invention

[0005] This invention provides a precise inspection method and system based on multi-dimensional green channel profiles, aiming to solve the technical problems existing in the current inspection process of vehicles transporting fresh agricultural products through green channels, such as low inspection efficiency, high dependence on manual labor, insufficient accuracy in identifying toll evasion, and the difficulty in balancing passenger experience and regulatory effectiveness. Existing technologies mainly rely on on-site manual visual inspection, single image recognition, or preliminary screening mechanisms based on fixed rules. They cannot deeply integrate and dynamically model multi-source heterogeneous information such as vehicles, goods, itineraries, and historical behavior, resulting in highly subjective inspection results, high misjudgment rates, and a high risk of missed detections. Furthermore, they are difficult to adapt to complex and ever-changing transportation scenarios and constantly evolving violations.

[0006] As one embodiment of the present invention, the precise verification method based on multi-dimensional green channel profiles includes the following steps: Multi-source data acquisition and preprocessing: Collect visible light images, near-infrared images, vehicle static information, dynamic weighing data, travel trajectory data and historical behavior credit data of the vehicle to be inspected, and perform structured cleaning and spatiotemporal alignment processing to generate a standardized data input set; Construction of a four-dimensional green channel profile: Based on the standardized data input set, feature vectors of a four-dimensional green channel profile covering vehicle dimension, cargo dimension, trip dimension and behavior dimension are extracted and encoded; wherein, the cargo dimension features include the actual loading volume fill rate calculated by three-dimensional reconstruction based on visible light and near-infrared multi-view images; Attention-based risk decision-making: The feature vector of the four-dimensional green channel profile is input into a pre-trained multi-layer fusion verification decision model; the model dynamically calculates the interaction weights between the four-dimensional features of vehicle, cargo, trip and behavior through a cross-dimensional attention interaction layer, and outputs the comprehensive risk score of the vehicle after fusion. Tiered inspection execution and closed-loop optimization: Based on the preset risk level to which the comprehensive risk score belongs, the corresponding exemption release, image verification, or on-site unpacking inspection strategy is executed; the final result of each inspection and the corresponding four-dimensional feature vector are used as new samples, and the parameters of the last two layers of the decision model are fine-tuned only through online incremental learning to update the model.

[0007] Furthermore, acquiring visible light and near-infrared images specifically includes: Multi-view cargo images are simultaneously acquired by deploying at least one visible light imaging unit and one near-infrared imaging unit above and to the side of the vehicle cargo compartment; the wavelength range of the near-infrared imaging unit is 850-950 nanometers, which is used to penetrate the cargo surface to identify non-catalog cargo hidden or concealed inside.

[0008] Furthermore, the actual loading volume fill rate is calculated through 3D reconstruction based on visible light and near-infrared multi-view images, specifically including: The multi-view images are uniformly projected onto a three-dimensional coordinate system with the center of the cargo box bottom as the origin; A sparse 3D point cloud of the cargo surface is generated using a stereo matching algorithm, and then converted into a dense triangular mesh model using a Poisson surface reconstruction algorithm. The volume enclosed by the triangular mesh model is calculated as the actual loading volume, and compared with the standard approved volume of the vehicle cargo box to obtain the loading volume fill rate.

[0009] Furthermore, the construction of behavioral dimension features specifically includes: Retrieve vehicle's historical passage and inspection records from the credit management platform; The behavioral stability index is calculated using a time decay weighted formula, where the weight of each historical record is negatively exponentially related to the number of days between its occurrence and the current time, in order to strengthen the impact of recent behavior on the current risk assessment.

[0010] Furthermore, the construction of vehicle-dimensional features also includes the calculation of axle load distribution rationality index, specifically: Obtain the actual load distribution vector of each axle of the vehicle, and the standard axle load distribution vector of the corresponding vehicle model retrieved from the standard vehicle model library; Calculate the Euclidean distance between the two vectors and perform normalization to obtain the rationality index of the axle load distribution. Furthermore, it also includes real-time environmental perception data acquisition. When calculating the risk score, the multi-layer fusion inspection and decision model dynamically adjusts the weight ratio of visible light image features and near-infrared image features in the fusion process based on the real-time acquired visibility and light intensity data.

[0011] Furthermore, the cross-dimensional attention interaction layer adopts a multi-head self-attention mechanism, treating the feature fragments of the four dimensions of vehicle, cargo, trip, and behavior as independent lexical units. By calculating the attention weights of query, key, and value vectors between lexical units, dynamic interaction and information fusion of cross-dimensional features are achieved.

[0012] Furthermore, online incremental learning specifically includes: Once the number of newly added samples reaches a preset number, model fine-tuning is triggered. The fine-tuning process fixes all parameters in the multi-layer fusion verification decision model except for the last two fully connected layers, and updates the parameters of the last two layers using only new samples through a mini-batch gradient descent algorithm.

[0013] Furthermore, the implementation of the tiered inspection strategy specifically includes: If the risk score is lower than the first threshold, the lane barrier will automatically lift and display an exemption from inspection message. If the risk score is between the first and second thresholds, multi-view cargo images and risk feature prompts will be pushed to the inspector's terminal for manual review. If the risk score is higher than the second threshold, the on-site unpacking and inspection process will be triggered, and a targeted inspection guide list will be automatically generated based on the risk characteristics.

[0014] As another aspect of the present invention, a precise inspection system based on a multi-dimensional green channel profile is provided, comprising: The multi-source data acquisition unit includes visible light and near-infrared multi-view cameras deployed in toll station lanes, vehicle weighing platforms, license plate recognition equipment, weather sensor arrays, and data interfaces that connect with the toll collection system and credit management platform. The data processing and profile building unit is used to clean and align the collected data and build a four-dimensional green channel profile feature vector. The intelligent decision-making and execution unit has a built-in multi-layer fusion inspection decision model, which is used to calculate risk scores, generate inspection level instructions, and control lane barriers, information screens and inspection terminals to execute graded inspection strategies. The model incremental learning unit is used to collect feedback on the verification results and execute an online incremental learning process to continuously update the parameters of the decision model.

[0015] In summary, this application includes the following beneficial technical effects: This invention constructs a comprehensive, structured, and quantitative representation of green channel vehicles by constructing a profile encompassing four dimensions: vehicle, goods, journey, and behavior. This overcomes the limitations of traditional inspection methods that rely solely on single images or human experience. Utilizing a cross-dimensional attention mechanism in a multi-layered fusion inspection decision model, it dynamically identifies the most discriminative combinations of risk features in different scenarios, significantly improving the accuracy of identifying toll evasion. By introducing a time-decay-weighted behavioral stability index and a dynamic weight adjustment mechanism based on real-time environmental perception data, the inspection strategy possesses adaptive capabilities, effectively balancing traffic efficiency and regulatory accuracy. The system supports online incremental learning, continuously optimizing model performance as inspection data accumulates, forming a closed-loop intelligent evolution capability. The overall solution reduces manual inspection costs, shortens vehicle dwell time, and enhances the fairness and credibility of green channel policy implementation. Attached Figure Description

[0016] Figure 1 This is an overall schematic diagram of a precise inspection method based on a multi-dimensional green channel profile; Figure 2 This is a schematic diagram illustrating the principle of a multi-layered fusion verification decision model; Figure 3 This is a flowchart illustrating the logic of multi-source heterogeneous data preprocessing and the construction of a four-dimensional green channel profile. Detailed Implementation

[0017] This invention provides a precise inspection method and system based on a multi-dimensional green channel profile, aiming to solve the technical problems existing in the current inspection process of vehicles transporting fresh agricultural products through green channels, such as low inspection efficiency, high reliance on manual labor, insufficient accuracy in identifying toll evasion, and the difficulty in balancing passenger experience and regulatory effectiveness. Existing technologies mainly rely on on-site manual visual inspection, single image recognition, or preliminary screening mechanisms based on fixed rules. These methods cannot deeply integrate and dynamically model multi-source heterogeneous information such as vehicles, goods, itineraries, and historical behavior, resulting in highly subjective inspection results, high misjudgment rates, and a high risk of missed detections. Furthermore, they are ill-suited to complex and ever-changing transportation scenarios and constantly evolving violations. To overcome these shortcomings, this embodiment constructs a precise inspection method based on a four-dimensional green channel profile, integrating multi-source heterogeneous data, and possessing adaptive decision-making capabilities, and implements a corresponding system architecture.

[0018] Firstly, the method for accurate inspection based on a multi-dimensional green channel profile disclosed in this application includes the following steps: Step S1: Acquire multi-source heterogeneous data of the green channel vehicle to be inspected, including vehicle static identity information, cargo loading image data, travel route information, historical passage records, and real-time environmental perception data; Step S2: Perform structured cleaning, time alignment, and spatial coordinate unification on the multi-source heterogeneous data to generate a standardized data input set; Step S3: Based on the standardized data input set, construct a four-dimensional green channel profile feature vector containing vehicle dimension, cargo dimension, travel dimension, and behavior dimension; Step S4: Input the four-dimensional green channel profile feature vector into a pre-trained multi-layer fusion inspection decision model to output the vehicle's comprehensive risk score and inspection level suggestion; Step S5: Execute the corresponding inspection strategy according to the inspection level suggestion, including exemption from inspection, image verification, or on-site unpacking inspection.

[0019] Step S1 aims to collect various information about green channel vehicles from multiple sources, providing a comprehensive and real-time data foundation for building accurate profiles. The acquired data covers five aspects: vehicle static identity, cargo loading images, travel routes, historical passage records, and real-time environmental perception. Data is collected synchronously or asynchronously through dedicated equipment and interfaces deployed at toll stations and network systems, ensuring the diversity and reliability of data sources.

[0020] Step S101: Simultaneously collect static vehicle identification information through license plate recognition equipment, vehicle type classifiers, and vehicle weighing systems deployed at highway entrances. The license plate recognition equipment uses a high frame rate industrial camera with an infrared fill light module to stably capture license plate images under day and night conditions and in adverse weather conditions. The complete license plate number is then parsed using an optical character recognition algorithm. The optical character recognition algorithm employs an end-to-end recognition model based on deep learning, specifically using a structure combining convolutional neural networks and recurrent neural networks. During training, a dataset containing millions of license plate images is used.

[0021] The vehicle type classifier is based on the fusion perception of millimeter-wave radar and lidar. It measures the vehicle's outline height, width, length, and wheelbase distribution, and matches it with a pre-set national standard vehicle type database to output a vehicle type code. The millimeter-wave radar operates at a frequency of 77 GHz, and the lidar uses a 905-nanometer wavelength with a scanning frequency of 10 Hz. The vehicle weighing system uses a dynamic weighing platform with built-in piezoelectric sensors to measure the load on each axle of the vehicle in real time at a sampling frequency of 1000 Hz. The load utilization rate is obtained by calculating the ratio of the total mass to the rated load capacity.

[0022] All collected vehicle static identity information is compared and verified in real time with the national motor vehicle registration database through a secure communication channel based on the HTTPS protocol. If the information is inconsistent or the vehicle status is abnormal, such as cancellation or seizure, it will be immediately marked as a high-risk vehicle, and the subsequent process will be interrupted, directly triggering a level 3 inspection.

[0023] Step S102: Deploy multi-view high-definition camera devices above and to the side of the toll station exit lane to collect cargo loading image data. The camera device includes at least three imaging units: one top-down visible light imaging unit, two side-oblique visible light imaging units, and one top-down near-infrared imaging unit; the visible light imaging unit has a resolution of not less than 1920×1080 pixels, and the near-infrared imaging unit has a wavelength range of 850-950 nanometers.

[0024] The visible light imaging unit is used to acquire the color, texture, shape and stacking status of the cargo surface, while the near-infrared imaging unit uses the absorption differences of different materials in the near-infrared band to penetrate the surface and acquire the internal density distribution characteristics, thereby identifying whether there are hidden layers, empty boxes or goods not in the catalog.

[0025] All image data were preprocessed immediately after acquisition. Preprocessing included lens distortion correction using the Brown-Conrad model for parameter calibration, white balance adjustment using the gray-world algorithm, illumination equalization using the histogram equalization method, and background segmentation using a deep learning-based semantic segmentation model with a U-Net architecture. During training, a dataset of 100,000 labeled cargo images was used to accurately separate the cargo compartment area from the complex background.

[0026] Step S103: The preprocessed image data is fed into the goods category recognition sub-model based on convolutional neural network. During the training phase, this sub-model uses a million-level labeled image dataset covering all categories of the "Fresh Agricultural Products Catalogue". During training, a stochastic gradient descent optimizer is used with a learning rate of 0.001, a batch size of 32, and 50 training epochs.

[0027] The model architecture is ResNet-50, and the output layer is a Softmax function, which generates a product category confidence matrix. Each dimension of the matrix corresponds to a category in the catalog, and its value is the matching probability of that category. The probability value is calculated through forward propagation of the model.

[0028] The main cargo category is determined by the one with the highest confidence level, with a confidence threshold set at 0.8. The proportion of auxiliary cargo mixed loading is calculated by statistically analyzing the pixel proportion of other high-confidence categories. Specifically, in the segmented cargo compartment area image, the ratio of the total number of pixels of non-main cargo categories to the total number of pixels in the cargo compartment area is calculated.

[0029] Step S104: Obtain travel route information by reconstructing the trajectory through ETC gantry transaction records in the highway network toll collection system. The system uses the vehicle's exit time as a benchmark, traces back to the most recent entry transaction record, and extracts the timestamps and location information of all gantries along the route to construct a complete travel trajectory; the trajectory reconstruction adopts a time-series-based linear interpolation method.

[0030] Based on this trajectory, the actual average vehicle speed is calculated using the formula: total mileage divided by total travel time. The total mileage is obtained by summing the distances between adjacent gantries, using the Havesing formula for distance calculation. A deviation analysis is performed between the actual average speed and the design speed limit for this road segment, as well as the historical average speed for the same period. The historical average speed for the same period is obtained by querying a database of vehicle average speeds for the same time period and road segment over the past 30 days.

[0031] If the actual average speed deviates from the historical average speed by more than 30%, or falls below the design speed limit by 50%, an anomaly marker is assigned to the trip dimension features. In addition, the system also counts whether the vehicle passed through multiple provinces during this trip. The frequency of inter-provincial travel is included in the feature system as a trip complexity indicator, and the determination of inter-provincial travel is based on changes in the province code in the gantry record.

[0032] Step S105: Retrieve the vehicle's green channel passage count, non-green channel passage count, number of inspections, inspection results, whether toll evasion occurred, and credit rating status from the green channel credit management platform over the past twelve months. Data retrieval is achieved through an API interface with HTTPS protocol and JSON data format. To reflect the greater impact of recent behavior on current risk, the system uses a time decay weighted method to generate a behavior stability index.

[0033] The formula for calculating the behavioral stability index is: in, This represents the total number of historical access records. For the first The compliance mark for the second pass, when compliant When violating the rules , For the first The time decay weight of each record is calculated using the following formula: , For the first The number of days since the next passage. The attenuation coefficient is 0.02.

[0034] The closer the index is to 1, the more stable and reliable the vehicle's historical behavior is; the closer it is to 0, the more frequent violations or serious recent breaches of trust there are.

[0035] Step S106: Real-time environmental perception data is collected through a meteorological sensor array and video traffic monitoring unit deployed in the toll station area. The meteorological sensor array integrates a rain gauge, visibility meter, illuminance meter, and temperature and humidity sensor, updating environmental parameters once per second. The rain gauge has a measurement accuracy of 0.1 mm, the visibility meter has a measurement range of 0-2000 meters, and the illuminance meter has a measurement range of 0-100000 lux.

[0036] The video traffic monitoring unit analyzes vehicle density and speed in the lane video stream to calculate the traffic flow density index. Specifically, it uses a background subtraction algorithm to detect vehicles and calculates the number of vehicles per unit lane length. Real-time environmental perception data is used to dynamically adjust the confidence threshold of the image recognition sub-model. The adjustment rule is as follows: when visibility is below 500 meters or illumination intensity is below 100 lux, the category confidence weight of the visible light image output is reduced to 0.3, the near-infrared image weight is increased to 0.7, and the historical behavior data weight remains unchanged. The weight adjustment is achieved through a weighted fusion formula, which is: Final Confidence = Weight 1 × Visible Light Confidence + Weight 2 × Near-Infrared Confidence + Weight 3 × Historical Behavior Baseline Value.

[0037] Step S106: Real-time environmental data is collected through a weather sensor array and video traffic monitoring unit deployed in the toll station area. The weather sensor array integrates rain gauges, visibility meters, illuminometers, and temperature and humidity sensors, updating weather conditions, visibility, and light intensity every second. The video traffic monitoring unit calculates the traffic flow density index by analyzing the lane video flow.

[0038] This real-time data is used to dynamically adjust the weight allocation during subsequent multi-source data fusion. For example, in rainy or foggy weather, the system automatically reduces the confidence weight of visible light image recognition results while increasing the fusion weight of near-infrared image data and historical behavioral features to ensure the robustness of the overall decision-making.

[0039] In summary, through step S1, the system completes multi-dimensional data collection and preliminary processing, encompassing vehicle identity, cargo visual data, travel trajectory, historical credit data, and real-time environmental data. These heterogeneous data, after subsequent structured cleaning and feature encoding, will collectively construct a comprehensive, quantifiable, and computable profile of green channel vehicles, providing a solid data foundation for intelligent inspection decisions.

[0040] Step S2: Perform structured cleaning, temporal alignment, and spatial coordinate unification on the multi-source heterogeneous data to generate a standardized data input set. This step aims to standardize the raw multi-source heterogeneous data collected in Step S1, eliminating inconsistencies in time, space, and completeness, and providing high-quality, standardized input for subsequent feature extraction and profile construction.

[0041] Step S201: Perform multi-source data alignment based on a reference time. The system uses the precise moment when a vehicle passes the inductive loop detector at the exit lane as the reference time point. For all data sources with timestamps, such as vehicle static information, cargo images, and trip records, the system sets a time window of ±5 seconds. Within this window, backtracking alignment and matching are performed on each data stream to ensure that all data belonging to the same vehicle remain synchronized on the timeline, laying the foundation for subsequent cross-modal correlation analysis.

[0042] Step S202: Fill in missing data using a state estimation method. For sensor data loss due to temporary equipment malfunctions, such as the failure to successfully acquire a near-infrared image from a certain viewpoint, the system employs a Kalman filter-based state estimation method for intelligent data filling. Specifically, the system uses the mean value of near-infrared image features of similar vehicles under similar loading conditions from historical data as the state prior of the Kalman filter. Simultaneously, the feature vector obtained from the currently successfully acquired visible light image of the vehicle after semantic segmentation and feature extraction is used as the observation value. The filter iteratively updates to predict reasonable feature values ​​for the missing near-infrared data, thus completing data filling. This method better preserves the physical correlation between data compared to simple linear interpolation.

[0043] Step S203: Unify image data to a standard 3D spatial coordinate system. To achieve accurate volume calculation and 3D reconstruction, the system projects all cargo image data acquired from multiple perspectives into a unified 3D coordinate system with the center of the current vehicle cargo compartment floor as the origin. This process is achieved by calibrating the internal and external parameters of the multi-view high-definition camera device beforehand. After calibration, the system uses the parameter matrix of each camera to project the 2D image pixels back into 3D space, thereby unifying the image information from all perspectives into the same 3D reference system. This makes image data from different angles spatially comparable and fusionable.

[0044] In summary, step S2 transforms the raw, heterogeneous, and potentially incomplete pipeline data into a standardized data input set that is time-synchronized, spatially unified, and content-complete by performing strict time alignment, intelligent data imputation, and precise spatial coordinate normalization on the multi-source data. This high-quality dataset provides the necessary preprocessing foundation for accurately constructing a structured four-dimensional green channel profile in the next step, enabling reliable feature extraction and model computation.

[0045] Step S3 aims to transform the standardized multi-source data output from step S2 into a structured, machine-readable feature vector. This feature vector quantitatively represents green channel vehicles from four dimensions: vehicle, cargo, trip, and behavior, providing a unified input for the subsequent risk assessment model.

[0046] Step S301: Vehicle dimension features focus on the legality and physical state of the vehicle itself. The system first determines the vehicle type legality flag. If the vehicle type compared with the national motor vehicle registration database matches the result of the vehicle type classifier's on-site identification, the flag is 1; otherwise, it is 0.

[0047] Next, the load capacity utilization rate is calculated. This value is obtained by dividing the actual total mass measured by the vehicle weighing system by the approved load capacity obtained from the vehicle registration information.

[0048] The axle load distribution rationality index is then calculated, which measures the degree of deviation between the actual axle load distribution and the standard distribution. The system retrieves the standard axle load distribution vector for the vehicle model from the vehicle model database, denoted as... ,in For the number of axes, For the first The standard load percentage for each axis. Simultaneously, the actual load on each axis is obtained from the dynamic weighing platform, and the actual load distribution vector is calculated. ,in For the first The actual load percentage of the shaft. Axle load distribution rationality index. The Euclidean distance between the two vectors is calculated and normalized. The formula is as follows: in, Representing vectors and Euclidean distance, A preset value for the maximum possible distribution deviation of this vehicle model is used to normalize the results to a range of 0 to 1. The vehicle type legality flag, load capacity utilization rate, and axle load distribution rationality index together constitute the first feature sub-vector. .

[0049] Step S302: Describe the compliance and loading status of the goods as cargo dimension features. The system first selects the category with the highest confidence level from the output of the cargo category identification sub-model as the main cargo; its corresponding confidence value is the main cargo category confidence level. .

[0050] Mixed loading ratio of auxiliary goods This is obtained by statistically analyzing the percentage of pixels representing non-primary cargo categories in the image. Specifically, on the binary image of the cargo compartment area obtained through semantic segmentation, the total number of pixels identified by the model as high-confidence categories other than the primary cargo is counted. and the total number of pixels in the cargo compartment area ,but .

[0051] Loading volume fill rate The calculations are based on 3D reconstruction technology. The system utilizes multi-view images in a unified coordinate system from step S203 to generate a sparse 3D point cloud of the cargo surface using a stereo matching algorithm. Then, a Poisson surface reconstruction algorithm is employed to transform this sparse point cloud into a continuous, dense triangular mesh model, thereby reconstructing the 3D shape of the cargo.

[0052] Based on the standard internal dimensions of the vehicle's cargo box obtained from the vehicle model database, the length... ,Width ,high Calculate the approved volume of the cargo compartment The system calculates the actual loading volume by measuring the volume enclosed by the triangular mesh model. Ultimately, the loading volume fill rate. The confidence level of the main cargo category, the proportion of mixed auxiliary cargo, and the loading volume fill rate together constitute the second feature vector. .

[0053] Step S303: Trip dimension features reflect the spatiotemporal regularity of this journey. Path rationality score. The system calculates the path integrity and rationality based on map matching technology. It reconstructs the vehicle trajectory point sequence from step S104 and matches it with the high-precision map road network, calculating the proportion of successfully matched trajectory points to the total number of trajectory points.

[0054] Speed ​​anomaly Used to quantify abnormal driving speed conditions. The calculation formula is: in, The actual average vehicle speed calculated in step S104, This refers to the expected average vehicle speed for the same road segment during the same time period, retrieved from a historical traffic speed database. The historical standard deviation of the expected vehicle speed is used to standardize the deviation value.

[0055] Inter-provincial travel frequency This is a statistical value that records the number of times a vehicle's journey has crossed different provinces in the past 30 days. The route rationality score, speed anomaly score, and frequency of cross-provincial travel together constitute the third feature vector. .

[0056] Step S304: Behavioral dimension features characterize the vehicle's long-term historical credit and recent performance. Behavioral stability index. The result calculated in step S105 is used directly.

[0057] Credit rating code The credit ratings assigned by the platform are converted into numerical values. Credit ratings A, B, C, and D are mapped to numerical values ​​of 0.9, 0.7, 0.4, and 0.1, respectively.

[0058] Inspection pass rate in the past 30 days The total number of times the vehicle was inspected in the past 30 days was statistically analyzed. And the number of times the inspection results were compliant. Calculation The behavioral stability index, credit rating code, and verification pass rate over the past 30 days together constitute the fourth feature sub-vector. .

[0059] Step S305: The system will convert the four feature vectors The components are concatenated in a fixed order according to vehicles, goods, routes, and behaviors to form a high-dimensional joint feature vector.

[0060] The joint vector is then fed into a batch normalization layer for processing. This layer computes the mean for each feature dimension across the entire training set. and standard deviation And for each eigenvalue Perform normalization: .

[0061] This step eliminates the differences in units and value ranges between different features, enabling subsequent models to be trained and inferred more stably and efficiently. The vector after batch normalization is the final four-dimensional green channel profile feature vector.

[0062] In summary, through step S3, the system successfully transformed the multi-source, heterogeneous raw data into a comprehensive, structured, and scale-uniform numerical feature representation. This four-dimensional green channel profile feature vector integrates the static attributes of vehicles, the real-time status of goods, the dynamic patterns of the journey, and historical credit behavior, providing accurate and rich input data for subsequent intelligent risk decision-making models.

[0063] For step S4, a deep neural network model is used to perform in-depth analysis and fusion of the four-dimensional feature vector constructed in step S3, and finally quantitatively assess the risk level of the vehicle and give a clear inspection level recommendation.

[0064] Step S401: Input the four-dimensional green channel profile feature vector obtained in step S3 into the first component of the multi-layer fusion verification decision model—the feature embedding layer. This layer consists of two sequentially connected fully connected layers. The first fully connected layer increases the dimension of the input vector to 256 dimensions, and the second fully connected layer further maps it to a 512-dimensional high-dimensional semantic space.

[0065] Each fully connected layer is followed by a ReLU activation function to introduce non-linearity. The weight matrix of this layer is learned during the model pre-training stage through backpropagation, and its role is to transform the original features into a deeper representation that is more conducive to the subsequent attention mechanism computation.

[0066] Step S402: The 512-dimensional embedded feature vector is fed into a cross-dimensional attention interaction layer for processing. This layer is built based on a multi-head self-attention mechanism. Specifically, the system first divides the 512-dimensional input vector into feature segments representing four dimensions: vehicle, cargo, trip, and behavior, according to the dimensional category information of the feature source. Each segment is regarded as an independent "word unit".

[0067] For each attention head, the model generates a query vector for each lexical unit using a learnable weight matrix. Key vector Sum value vector Lexical units with lexical elements Attention weights between Calculated using the following formula: in, It is the dimension of the key vector. This is the scaling factor. This weight... Reflects dimensions Dimension The level of attention given to it.

[0068] Subsequently, the updated feature vector for each word We obtain this by weighted summation of the value vectors of all lexical units: In this embodiment, the number of attention heads is set to 8. Finally, the outputs of all attention heads are concatenated and passed through a linear transformation layer to form a 512-dimensional feature vector containing cross-dimensional interaction information. This mechanism can automatically capture the inherent correlations between features of different dimensions. For example, when a "behavioral dimension" of poor credit and a "cargo dimension" of high mixed-load ratio occur simultaneously, the model will automatically assign them higher mutual attention weights, thereby amplifying the signal of this high-risk combination.

[0069] Step S403: Input the attention-interacted feature vector into the risk aggregation layer. This layer consists of a three-layer fully connected network, responsible for fusing and compressing information into a more discriminative low-dimensional risk representation. The first layer maps the 512-dimensional input to 256 dimensions, the second layer further maps it to 128 dimensions, and the third layer outputs a 128-dimensional comprehensive risk representation vector.

[0070] Each layer is followed by a ReLU activation function, and a Dropout strategy is applied during training to prevent overfitting, with a Dropout ratio set to 0.2. This layer deeply fuses attention-weighted multidimensional features through multi-level nonlinear transformations.

[0071] Step S404: Input the 128-dimensional risk aggregation feature vector into the final decision output layer. This layer is a fully connected layer composed of single neurons, using the sigmoid activation function. Its output value is a scalar between 0 and 1, defined as the vehicle's comprehensive risk score. The Sigmoid function maps any real number to the interval (0,1), making it ideal for representing probability or risk levels.

[0072] Step S405: The system assigns continuous risk scores based on a preset static grading threshold range. This is mapped to discrete inspection level recommendations. The specific rules are as follows: If If it is determined to be a Level 1 risk, it is recommended to release it without inspection; if If so, it is judged as a level 2 risk, and image review is recommended; if If so, it is judged as a level three risk, and it is recommended to open the box and inspect it on site.

[0073] In summary, through steps S401 to S405, the multi-layered fusion inspection decision model completes the intelligent mapping from structured feature vectors to quantitative risk scores and decision recommendations. The model enhances feature representation capabilities through embedding layers, dynamically focuses on key risk correlations using attention mechanisms, and then performs deep fusion and judgment through multi-layered neural networks, ultimately outputting an interpretable and actionable inspection level instruction. This process realizes the transformation of inspection decision-making from "rule-based" to "data and model-based," providing a direct basis for executing precise and differentiated inspection strategies.

[0074] Step S5 transforms the intelligent decision output from step S4 into specific on-site operations, forming a complete closed loop from decision-making to execution, and then from the feedback of execution results to optimize the decision. This not only enables precise hierarchical and categorized verification but also endows the system with the ability to continuously self-evolve.

[0075] Step S501: When the inspection level recommendation is Level 1, the system determines that the vehicle risk is extremely low and executes the fast-pass procedure. The system's core control module sends a digital barrier-raising command to the corresponding lane's barrier control system via standard RS-485 or Ethernet communication protocols. After the command is issued, the system synchronously drives the large information display screen above the lane to display the green text message "Green Channel Exemption from Inspection, Fast Pass" to remind the driver.

[0076] Simultaneously, the system's business log module automatically generates a complete passage record. This record is stored in a structured format, with core fields including the unique serial number of this passage, license plate number, passage timestamp, the final risk score R value calculated in step S4, key feature values ​​extracted from step S3 across four dimensions: vehicle, cargo, trip, and behavior, and a brief summary of decision-making criteria generated by model attention weight analysis, such as "excellent historical credit and high cargo image recognition matching degree." This record is written to the passage log table in the backend database in real time for subsequent auditing and data analysis.

[0077] Step S502: When the inspection level recommendation is Level 2, the system determines that there is a certain risk and manual remote verification is required. The system immediately pushes the key data packets related to this inspection to the smart terminal device held by the on-duty inspector via the toll station's internal wireless network or 4G / 5G network. The data packet content mainly includes: multi-view high-definition images of cargo loading collected in step S102, the risk score R obtained in step S4, and descriptions of high-risk features automatically marked by the system, such as "the calculated value of mixed cargo loading ratio is 35%" or "the abnormality of travel speed is marked as yes".

[0078] After receiving the task, the inspector's handheld terminal's dedicated application will issue an audio and vibration alert. The inspector must enter the task interface and complete the remote review of the cargo images within the specified 3-minute time limit. If the inspector confirms that the images show the cargo fully complies with the requirements of the "Catalogue of Fresh Agricultural Products," they will click the "Confirm Release" button on the terminal. Upon receiving this instruction, the system will execute the same barrier raising and recording process as step S501. If the inspector finds the cargo suspicious, the image unclear, or other issues during the image review, they can manually upgrade the inspection to level three via the "Upgrade Inspection" button on the terminal interface, and the system will then proceed to step S503.

[0079] Step S503: When the inspection level recommendation is Level 3 or it is manually upgraded from Level 2 to Level 3, the system determines that the risk is very high and on-site physical inspection is necessary. The system first activates the on-site audible and visual alarm device, emitting a specific frequency audible and visual signal to warn staff that there is a high-risk vehicle that needs to be handled. At the same time, the lane information display screen switches to a red warning message: "Please enter the dedicated inspection area".

[0080] The system uses a programmable logic controller to change the display status of lane guidance lights, guiding vehicles to pre-set, physically isolated, dedicated inspection lanes. Subsequently, based on the specific risk points analyzed in steps S3 and S4, the system automatically generates a structured inspection guidance checklist, which is then displayed to the on-site inspectors on the computer screen in the inspection area.

[0081] The checklist is highly targeted. For example, if the model indicates a "high proportion of mixed auxiliary cargo," an entry is generated: "Focus on checking whether non-cataloged goods are hidden in the rear and edges of the cargo compartment." If it indicates an "abnormal load capacity utilization rate," an entry is generated: "Verify whether the total weight of the actual loaded cargo matches the declaration." On-site inspectors use this checklist to conduct efficient and targeted unpacking inspections, and the final manual inspection results (compliance or non-compliance) are entered into the system through the inspection terminal.

[0082] Through steps S501, S502, and S503, the system achieves seamless linkage with toll station hardware facilities and staff, transforming abstract algorithm outputs into hierarchical and executable specific actions, ensuring a dynamic balance between monitoring accuracy and traffic efficiency.

[0083] Step S504: After each complete inspection process, regardless of whether the final result is automatic system release, manual review and release, or release or interception after unpacking and inspection, the system will use the final factual result of this inspection as a labeled training sample. This sample includes the four-dimensional green channel profile feature vector constructed in step S3 as the input feature X, and the final compliance judgment result of this inspection as the label Y, where Y=1 represents the final conclusion is compliant, and Y=0 represents the final conclusion is non-compliant. The system asynchronously sends samples X and Y to the backend model training module through a message queue.

[0084] Step S505: After the training module receives new labeled samples, it does not retrain the entire complex multi-layer fusion verification decision model from scratch. Instead, it uses incremental learning to fine-tune the network parameters of only the last two layers of the model. Specifically, it fine-tunes the third fully connected layer of the risk aggregation layer in step S403 and the decision output layer in step S404.

[0085] The fine-tuning process employs a mini-batch stochastic gradient descent algorithm. The optimization objective is to minimize the cross-entropy loss function between the predicted risk score and the true label, with an L2 regularization term added to prevent overfitting. The gradient used for each parameter update is calculated from the loss function of the current mini-batch samples. The mathematical expression of this optimization process is to minimize the following objective function. : in, This represents the number of samples in the current mini-batch. Representing the The true label (0 or 1) of each sample. Representative model for the first Risk scores predicted for each sample This represents the set of model parameters to be fine-tuned. This is the L2 regularization coefficient, which controls the strength of regularization. Model parameters. The update formula is: in, Representing the The parameters at the next iteration It's the learning rate. It is the gradient of the objective function with the current parameters. In this embodiment, the learning rate... The value is fixed at 0.001, the mini-batch size is set to 32, the momentum coefficient is set to 0.9 to accelerate convergence, and the L2 regularization coefficient is... Set it to 0.0005.

[0086] Step S506: The system sets up an update trigger mechanism. The training module continuously receives and caches new samples. Every time 100 new samples are accumulated, a fine-tuning training process based on these 100 samples is automatically triggered. The entire training and update process is completed on the backend server, completely isolated from the frontend business process of real-time vehicle inspection, and does not affect each other.

[0087] After a round of fine-tuning and updating of the model parameters, the system will generate a new version of the model parameter file. This new file will be automatically distributed and deployed to all edge computing server nodes responsible for real-time inference via a secure file synchronization protocol, replacing the old version of the parameters. The deployment process is smooth, typically employing hot-reloading technology to ensure that the verification business is unaffected and uninterrupted during model switching.

[0088] In summary, step S5 not only completes the closed loop of precise execution from intelligent decision-making to the physical world, but also constructs a continuously optimized data intelligence closed loop through steps S504 to S506. The system utilizes the real-world feedback from each inspection to drive the continuous evolution of the core decision-making model, enabling it to adapt to new violations and transportation scenarios. This ensures long-term high accuracy and reliability, truly realizing a rapid passage mechanism driven by credit, data collaboration, and intelligent decision-making.

[0089] On the other hand, corresponding to the above method, this embodiment provides a precise inspection system based on a multi-dimensional green channel profile.

[0090] The system includes a multi-source data acquisition unit, a data preprocessing unit, a green channel profile construction unit, an inspection decision model unit, and an inspection execution control unit.

[0091] The multi-source data acquisition unit includes license plate recognition cameras, vehicle model recognition radar, dynamic weighing platforms, multi-view high-definition camera devices, and meteorological sensor arrays deployed at toll station entrances and exits, as well as API interface modules for the provincial networked toll collection system, the national motor vehicle database, and the green channel credit management platform. The API interface modules employ HTTPS protocol and the national cryptographic SM4 encryption algorithm to ensure the security and integrity of data transmission. The data preprocessing unit incorporates a time alignment engine and a Kalman filter filling module to ensure the integrity and temporal consistency of the input data. The green channel profile construction unit integrates a 3D point cloud reconstruction engine and a feature encoder to achieve automated generation of 4D profiles. The inspection decision model unit is deployed on an edge server, possessing low-latency inference capabilities, with a single decision taking no more than 800 milliseconds. The inspection execution control unit communicates with lane barrier machines, information display screens, audible and visual alarms, and inspection terminals via an industrial control bus to achieve closed-loop execution of the inspection strategy.

[0092] This embodiment constructs a four-dimensional green channel profile, achieving a comprehensive, structured, and quantitative representation of green channel vehicles, overcoming the limitations of traditional inspection methods that rely solely on single images or human experience. Utilizing a cross-dimensional attention mechanism in a multi-layered fusion inspection decision model, it can dynamically identify the most discriminative combinations of risk features in different scenarios, significantly improving the accuracy of toll evasion detection. By introducing a time-decay-weighted behavioral stability index and a dynamic weight adjustment mechanism based on real-time environmental perception data, the inspection strategy possesses adaptive capabilities, effectively balancing traffic efficiency and regulatory accuracy. The system supports online incremental learning, continuously optimizing model performance as inspection data accumulates, forming a closed-loop intelligent evolution capability. The overall solution reduces manual inspection costs, shortens vehicle dwell time, and enhances the fairness and credibility of the green channel policy.

[0093] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects.

[0094] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for intelligent inspection of green channel vehicles based on multi-dimensional profiling, characterized in that, include: Multi-source data acquisition and preprocessing: Collect visible light images, near-infrared images, vehicle static information, dynamic weighing data, travel trajectory data and historical behavior credit data of the vehicle to be inspected, and perform structured cleaning and spatiotemporal alignment processing to generate a standardized data input set; Construction of a four-dimensional green channel profile: Based on the standardized data input set, feature vectors of a four-dimensional green channel profile covering vehicle dimension, cargo dimension, trip dimension and behavior dimension are extracted and encoded; wherein, the cargo dimension features include the actual loading volume fill rate calculated by three-dimensional reconstruction based on visible light and near-infrared multi-view images; Attention-based risk decision-making: The feature vector of the four-dimensional green channel profile is input into a pre-trained multi-layer fusion verification decision model. The model includes a cross-dimensional attention interaction layer, which is used to dynamically calculate the interaction weights between the four-dimensional features of vehicle, cargo, trip and behavior. After fusion, the comprehensive risk score of the vehicle is output. Tiered inspection execution and closed-loop optimization: Based on the preset risk level to which the comprehensive risk score belongs, the corresponding exemption release, image verification, or on-site unpacking inspection strategy is executed; the final result of each inspection and the corresponding four-dimensional feature vector are used as new samples, and the parameters of the last two layers of the decision model are fine-tuned only through online incremental learning to update the model.

2. The method according to claim 1, characterized in that, Acquiring visible light and near-infrared images specifically includes: Multi-view cargo images are simultaneously acquired by deploying at least one visible light imaging unit and one near-infrared imaging unit above and to the side of the vehicle cargo compartment; the wavelength range of the near-infrared imaging unit is 850-950 nanometers, which is used to penetrate the cargo surface to identify non-catalog cargo hidden or concealed inside.

3. The method according to claim 2, characterized in that, The actual loading volume fill rate is calculated based on 3D reconstruction using visible light and near-infrared multi-view images, specifically including: The multi-view images are uniformly projected onto a three-dimensional coordinate system with the center of the cargo box bottom as the origin; A sparse 3D point cloud of the cargo surface is generated using a stereo matching algorithm, and then converted into a dense triangular mesh model using a Poisson surface reconstruction algorithm. The volume enclosed by the triangular mesh model is calculated as the actual loading volume, and compared with the standard approved volume of the vehicle cargo box to obtain the loading volume fill rate.

4. The method according to claim 1, characterized in that, The construction of behavioral dimension features specifically includes: Retrieve vehicle's historical passage and inspection records from the credit management platform; The behavioral stability index is calculated using a time decay weighted formula, where the weight of each historical record is negatively exponentially related to the number of days between its occurrence and the current time, in order to strengthen the impact of recent behavior on the current risk assessment.

5. The method according to claim 1, characterized in that, The construction of vehicle-dimensional features also includes the calculation of axle load distribution rationality index, specifically: Obtain the actual load distribution vector of each axle of the vehicle, and the standard axle load distribution vector of the corresponding vehicle model retrieved from the standard vehicle model library; Calculate the Euclidean distance between the two vectors and normalize it to obtain the rationality index of the axle load distribution.

6. The method according to claim 1, characterized in that, It also includes real-time environmental perception data acquisition. When calculating risk scores, the multi-layer fusion inspection and decision model dynamically adjusts the weight ratio of visible light image features and near-infrared image features in the fusion process based on the real-time acquired visibility and light intensity data.

7. The method according to claim 1, characterized in that, The cross-dimensional attention interaction layer is implemented using a multi-head self-attention mechanism, which treats feature fragments of the four dimensions of vehicle, cargo, trip, and behavior as independent lexical units. By calculating the attention weights of query, key, and value vectors between lexical units, dynamic interaction and information fusion of cross-dimensional features are achieved.

8. The method according to claim 1, characterized in that, Online incremental learning specifically includes: Once the number of newly added samples reaches a preset number, model fine-tuning is triggered. The fine-tuning process fixes all parameters in the multi-layer fusion verification decision model except for the last two fully connected layers, and updates the parameters of the last two layers using only new samples through a mini-batch gradient descent algorithm.

9. The method according to claim 1, characterized in that, The implementation of the tiered inspection strategy specifically includes: If the risk score is lower than the first threshold, the lane barrier will automatically lift and display an exemption from inspection message. If the risk score is between the first and second thresholds, multi-view cargo images and risk feature prompts will be pushed to the inspector's terminal for manual review. If the risk score is higher than the second threshold, the on-site unpacking and inspection process will be triggered, and a targeted inspection guide list will be automatically generated based on the risk characteristics.

10. A smart inspection system for green channel vehicles based on multi-dimensional profiling, characterized in that, The system for implementing the method of any one of claims 1 to 9 comprises: The multi-source data acquisition unit includes visible light and near-infrared multi-view cameras deployed in toll station lanes, vehicle weighing platforms, license plate recognition equipment, meteorological sensor arrays, and data interfaces that connect with the toll collection system and credit management platform. The data processing and profile building unit is used to clean and align the collected data and build a four-dimensional green channel profile feature vector. The intelligent decision-making and execution unit has a built-in multi-layer fusion inspection decision model, which is used to calculate risk scores, generate inspection level instructions, and control lane barriers, information screens and inspection terminals to execute graded inspection strategies. The model incremental learning unit is used to collect feedback on the verification results and execute an online incremental learning process to continuously update the parameters of the decision model.

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