Traffic road law enforcement intelligent management system based on video and image recognition technology

Abstract

The invention relates to the technical field of video and image analysis, in particular to a traffic road law enforcement intelligent management system based on a video and image recognition technology, which comprises a road video monitoring acquisition module, a video image recognition module, an intelligent calculation analysis module, a risk level quantification module and a decision generation module. According to the method, through dynamic comparison of pixel differences of continuous frames in a video sequence and fusion of convolution feature extraction, a vehicle running track and road surface state change can be accurately captured in a changeable traffic environment, and quantitative aggregation is performed on abnormal areas in combination with a probabilistic calculation mode, so that a risk distribution result has stable and continuous spatial features, and the risk distribution accuracy is improved. And further, joint evaluation of multi-dimensional parameters is introduced into risk level division, so that boundaries among different risk levels are clearer and more consistent, the identification precision and classification rationality of complex traffic risks are improved, refined monitoring of road operation states and visual expression of risk results are realized, and a reliable spatial decision basis is provided.

Description

Technical Field

[0001] This invention relates to the field of video and image analysis technology, and in particular to an intelligent management system for traffic and road law enforcement based on video and image recognition technology. Background Technology

[0002] The field of video and image analysis technology mainly studies the use of computer vision and pattern recognition methods to extract features, identify targets, understand scenes, and monitor dynamics from acquired image information. Its core aspects include image acquisition and preprocessing, target detection and classification, scene semantic segmentation, and time-series image change analysis. This technology field has important application value in security monitoring, traffic management, medical diagnosis, and industrial inspection, and has formed a systematic technical system centered around image perception, recognition, and understanding.

[0003] The traditional intelligent management system for traffic enforcement refers to a system that acquires road images using fixed camera equipment, removes noise interference through image preprocessing, identifies traffic flow, pedestrians, and road obstacles using target detection methods, locates damaged areas of the road surface using scene segmentation technology, and continuously monitors and records the road operation status using image sequence analysis.

[0004] Traditional road enforcement management systems often rely on static information from a single frame during image recognition, making it difficult to reflect the continuous changes in road conditions. When vehicles change speed, turn, or minor road surface damage occurs, the lack of inter-frame correlation comparison can easily lead to delayed or missed risk identification. Furthermore, risk areas often rely on fixed thresholds for determination, resulting in results that are greatly affected by ambient light and noise interference. The area division is not continuous enough, and the risk level range is ambiguous, leading to biases in road safety status assessments and making it difficult to provide timely and stable reference for enforcement decisions. Summary of the Invention

[0005] To address the shortcomings of existing technologies that rely on fixed cameras for static road monitoring, making them ill-suited for dynamically changing traffic environments, and to improve responsiveness in emergency situations or complex scenarios due to the inability to adjust monitoring parameters in real-time, the present invention provides an intelligent traffic enforcement management system based on video and image recognition technology. The technical solution is as follows:

[0006] On the one hand, it provides an intelligent management system for traffic and road law enforcement based on video and image recognition technology. This system includes:

[0007] The highway video surveillance acquisition module acquires visible light video through cameras, extracts one frame per second at fixed time intervals, records key frame timestamps, generates standardized road monitoring data, and transmits it to the video image recognition module.

[0008] The video image recognition module receives the standardized road monitoring data, performs pixel-level difference comparison between the current frame and the previous frame, inputs it into the convolutional neural network for feature extraction, performs temporal comparison in conjunction with target detection, identifies road traffic safety risk characteristics, generates an abnormal area boundary coordinate set, and transmits it to the intelligent computing and analysis module.

[0009] The intelligent computing and analysis module receives the set of boundary coordinates of the abnormal area, uses the random forest algorithm to calculate the risk probability of the area parameter of the abnormal area, merges and associates risk areas based on the probability threshold, generates a road law enforcement risk distribution map, and transmits it to the risk level quantification module.

[0010] The risk level quantification module receives the road enforcement risk distribution map, quantifies and classifies the associated risk areas based on the risk probability value, marks the risk location coordinates, generates a road risk level map, and transmits it to the decision generation module.

[0011] As a further embodiment of the present invention, the standardized road monitoring data includes spatial positioning information, image feature information, and acquisition time sequence information; the abnormal area boundary coordinate set includes a spatial coordinate information set, boundary contour parameters, and time sequence identifiers; the road risk distribution map includes risk areas, risk probabilities, and merged areas; and the road risk level map includes risk level categories, risk persistence characteristics, and spatial location annotations.

[0012] As a further aspect of the present invention, the highway video surveillance acquisition module includes:

[0013] The image acquisition submodule acquires visible light video stream data captured by the camera device, performs frame-by-frame parsing of the video stream to extract the pixel matrix, synchronously acquires the time code and records the timestamp, binds the image data with time information, and generates a time-series image dataset.

[0014] The fixed-interval frame extraction submodule, based on the time-series image dataset, periodically extracts video frame sequences according to a fixed time interval parameter, extracts key frame images that meet the interval requirements, records the timestamp index position of the key frames on the time axis, and generates a key frame sequence.

[0015] The data standardization generation submodule receives the keyframe sequence, establishes the correspondence between the keyframe index and the time axis calibration, performs structured encoding on the image data and time index according to a unified format specification, and encapsulates it into standardized road monitoring data.

[0016] As a further aspect of the present invention, the video image recognition module includes:

[0017] The data stream receiving submodule acquires the standardized road monitoring data, performs integrity verification and format parsing on the data packets, extracts continuous frame timestamps and image data matrices, sorts the timestamps in time sequence, pairs them frame by frame according to time order, and generates a time sequence input sequence set.

[0018] The pixel difference comparison submodule extracts the temporally adjacent current frame and previous frame images based on the temporally input sequence set, performs pixel-level difference comparison operation on the current frame and previous frame, calculates the pixel value change and marks the change area, and generates an inter-frame difference feature map.

[0019] The feature extraction and recognition submodule receives the inter-frame difference feature map, inputs the difference image into the convolutional neural network to perform convolution operations to extract spatial feature vectors, combines the target detection algorithm to perform temporal comparison analysis on the feature vectors, identifies the boundaries of abnormal road areas, and generates a set of coordinates for the boundaries of abnormal areas.

[0020] As a further aspect of the present invention, the intelligent computing analysis module includes:

[0021] The coordinate receiving submodule acquires the boundary coordinate set of the abnormal area, arranges the longitude and latitude values ​​of the boundary points in order, calculates the straight-line distance for each adjacent boundary point and forms a boundary point spacing sequence, stores the spacing sequence with the original coordinate points in a positional correspondence, and generates a boundary coordinate sequence.

[0022] The parameter probability calculation submodule calls the boundary coordinate sequence, calculates the area value for each closed coordinate region, divides the area value into intervals, performs discrimination calculation using the random forest algorithm, and outputs the probability value sequence to obtain the regional risk probability sequence.

[0023] The risk area merging submodule compares the probability difference values ​​between adjacent areas based on the regional risk probability sequence, merges areas with difference values ​​lower than the threshold benchmark value, redefines the boundary coordinates of the merged areas and integrates the risk probability parameters to generate a road enforcement risk distribution map.

[0024] As a further aspect of the present invention, the risk level quantification module includes:

[0025] The distribution map parsing submodule obtains the pixel distribution matrix and location information in the road law enforcement risk distribution map, decomposes the pixel distribution matrix into multiple partition units according to the grid indexing rules, and performs index matching between the partition units and the corresponding geographic coordinates to generate a partition mapping matrix.

[0026] The risk coefficient quantification submodule, based on the partition pixel distribution values ​​in the partition mapping matrix, calls the risk correlation coefficient threshold benchmark, compares each partition pixel distribution value with the threshold benchmark item by item, converts the comparison results into a continuous risk probability, and obtains the partition risk persistence interval value.

[0027] The location labeling generation submodule calls the partition risk persistence interval value and partition index information, performs a level classification operation on the interval value, establishes a correspondence between the level classification result and the partition coordinate point, and superimposes the label in the coordinate system of the road risk distribution map to obtain the road risk level map.

[0028] As a further aspect of the present invention, the risk correlation coefficient threshold is a numerical benchmark established based on the abnormal pixel distribution value of the road and a preset risk judgment standard;

[0029] The partition risk persistence interval value is a continuous numerical interval calculated from the comparison result of the partition pixel distribution value relative to the risk correlation coefficient threshold.

[0030] As a further embodiment of the present invention, the decision generation module receives the road risk level map, sorts and calculates the priority of law enforcement patrols based on the risk level classification, selects and judges law enforcement resources according to the risk location distribution, and outputs a road risk warning list.

[0031] The road risk warning list includes a patrol priority sequence, law enforcement dispatch plan, and task list.

[0032] As a further aspect of the present invention, the decision generation module includes:

[0033] The risk level analysis submodule extracts the spatial coordinates and risk level values ​​of the area based on the road risk level map, calls the risk level threshold to compare the levels by partition, matches the results with the spatial coordinates, and generates a risk level distribution matrix.

[0034] The priority calculation submodule calls the level value and spatial coordinates in the risk level distribution matrix, sets the priority benchmark value, performs a weighted operation on the regional level value, compares the result with the priority benchmark value, and generates the patrol priority sequence value.

[0035] The task list generation submodule sorts the areas according to the patrol priority sequence value, calls the sorting results and spatial location, judges and selects law enforcement resources, merges the sorting and judgment results, and generates a road risk warning list.

[0036] As a further aspect of the present invention, the risk level threshold is the boundary of the interval that divides the original risk value into different risk level values;

[0037] The patrol priority sequence value is a value calculated by weighting the risk level value, spatial location parameter, and priority benchmark value.

[0038] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0039] By dynamically comparing pixel differences in consecutive frames of a video sequence and fusing convolutional feature extraction, vehicle trajectories and road surface condition changes can be accurately captured in variable traffic environments. Combined with probabilistic calculation methods, abnormal areas are quantified and aggregated, giving the risk distribution results stable and continuous spatial characteristics. Furthermore, the joint evaluation of multi-dimensional parameters is introduced into the risk level classification, making the boundaries between different risk levels clearer and more consistent. This improves the identification accuracy and classification rationality of complex traffic risks, enabling refined monitoring of road operation status and visualization of risk results, and providing reliable spatial decision-making basis for traffic law enforcement. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a system schematic diagram of the present invention;

[0042] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0043] Figure 3 This is a flowchart of the highway video surveillance acquisition module in this invention;

[0044] Figure 4 This is a flowchart of the video image recognition module in this invention;

[0045] Figure 5 This is a flowchart of the intelligent computing and analysis module in this invention;

[0046] Figure 6 This is a flowchart of the risk level quantification module in this invention;

[0047] Figure 7 This is a flowchart of the decision generation module in this invention. Detailed Implementation

[0048] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0049] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0050] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0051] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0052] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0053] This invention provides an intelligent management system for traffic and road enforcement based on video and image recognition technology, such as... Figure 1-2 The diagram shown illustrates a traffic enforcement intelligent management system based on video and image recognition technology. The system includes:

[0054] The highway video surveillance acquisition module acquires visible light video through cameras, extracts one frame per second at fixed time intervals, records key frame timestamps, generates standardized road monitoring data, and transmits it to the video image recognition module.

[0055] The video image recognition module receives standardized road monitoring data, performs pixel-level difference comparison between the current frame and the previous frame, inputs it into the convolutional neural network for feature extraction, combines target detection for temporal comparison, identifies road traffic safety risk characteristics, generates an abnormal area boundary coordinate set, and transmits it to the intelligent computing and analysis module.

[0056] The intelligent computing and analysis module receives the boundary coordinate set of the abnormal area, uses the random forest algorithm to calculate the risk probability of the area parameter of the abnormal area, merges and associates risk areas based on the probability threshold, generates a road law enforcement risk distribution map, and transmits it to the risk level quantification module.

[0057] The risk level quantification module receives the road enforcement risk distribution map, quantifies and classifies the associated risk areas based on the risk probability value, marks the risk location coordinates, generates a road risk level map, and transmits it to the decision generation module.

[0058] The decision generation module receives the road risk level map, sorts and calculates the patrol priority based on the risk level classification, selects and judges law enforcement resources according to the risk location distribution, sorts them according to the priority sequence, and outputs a road risk warning list.

[0059] Standardized road monitoring data includes spatial positioning information, image feature information, and collection time sequence information. The boundary coordinate set of abnormal areas includes a set of spatial coordinate information, boundary contour parameters, and time series identifiers. The road risk distribution map includes risk areas, risk probabilities, and merged areas. The road risk level map includes risk level categories, risk persistence characteristics, and spatial location annotations. The road risk warning list includes patrol priority sequence, law enforcement dispatch plan, and task list content.

[0060] Specifically, such as Figure 2 , 3 As shown, the highway video surveillance acquisition module includes:

[0061] The image acquisition submodule acquires visible light video stream data captured by the camera device, performs frame-by-frame parsing of the video stream to extract the pixel matrix, synchronously acquires the time code and records the timestamp, binds the image data with time information, and generates a time-series image dataset.

[0062] To acquire visible light video stream data from a camera device, the specific operation involves activating a DS-2CD3T87EWD-L camera device installed 15 meters above the road intersection. This device continuously captures 1920×1080 resolution video information at a rate of 30 frames per second. The system interface receives a digital video stream in H.264 encoding format. This video stream is sent to a dedicated decoding buffer. The decoder program reads the data frames in the buffer one by one, performs decoding operations on each frame, and restores the compressed video data to the uncompressed raw pixels. This process produces a 1920×1080 two-dimensional array. Each element in the array is a pixel containing three channel values. For example, at the position of row 100 and column 250 in the video frame, the extracted pixel value might be (R:150, G:165, B:170), representing a red channel intensity of 150, a green channel intensity of 165, and a blue channel intensity of 170. Simultaneously with decoding and extracting the complete pixel matrix for a specific frame, such as frame 50, the system immediately queries and reads the SMPTE timecode embedded in the video stream, obtaining a value such as "0". The system retrieves the time information "1:15:30:19". Simultaneously, it accesses the local operating system clock synchronized with the network time protocol server to obtain a high-precision UNIX timestamp, valued at 1728950400.123. This timestamp represents the total number of seconds since January 1, 1970, accurate to milliseconds. Subsequently, the system creates a new data structure containing two main parts: the first is the previously extracted 1920×1080 pixel matrix, and the second is the recorded timestamp value of 1728950400.123. These two parts are then forcibly associated... Within a data object, for example, Frame_Object_50 = \{timestamp:1728950400.123, pixels:[...]\}, the binding of image data and time information is completed. This process is repeated for each frame in the video stream. That is, after the 51st frame is decoded, the corresponding timestamp 1728950400.156 will be obtained synchronously, and a new bound data object Frame_Object_51 will be generated. This process continues until the continuously generated data objects are collected together to generate a time-series image dataset.

[0063] The fixed-interval frame extraction submodule, based on a time-series image dataset, periodically extracts key frame images that meet the interval requirements according to a fixed time interval parameter from the video frame sequence, records the timestamp index position of the key frames on the time axis, and generates a key frame sequence.

[0064] Based on a time-series image dataset, a fixed time interval parameter is first set. This parameter is set according to monitoring requirements. For routine analysis of traffic congestion on urban main roads, it is necessary to capture changes in traffic flow at the minute level. Therefore, this interval parameter is set to 30 seconds. The specific extraction process begins with the first image data unit in the dataset, which is directly selected as the first keyframe, and the timestamp is recorded and set as t. start =1728950400.000, and then the system begins to traverse subsequent time-series image data units. For each data unit, its timestamp t is extracted. current And perform a subtraction operation, using t current Subtract the timestamp t of the previous recorded keyframe last_keyframe The time difference Δt = t is obtained. current ―t last_keyframe Then, the calculated time difference Δt is compared with the preset fixed time interval parameter of 30 seconds to determine whether Δt is greater than or equal to 30. For example, if the system detects the 899th data unit with a timestamp of 1728950429.967, then t... last_keyframe The timestamp is still 1728950400.000, and the calculated time difference is 29.967 seconds. This value is less than 30, so this data unit is skipped. The system continues to check until the 900th data unit, whose timestamp is 1728950430.000. At this point, the calculated time difference is 30.000 seconds, which meets the condition of being greater than or equal to 30 seconds. Therefore, the system marks this 900th data unit as a new keyframe and immediately updates t. last_keyframe The value is 1728950430.000. When a new keyframe image is extracted, the system immediately records the specific timestamp index position of this keyframe on the complete timeline, such as 1728950430.000, and stores the image data of this keyframe along with the timestamp index into a new sequence set. This process is repeated continuously. The system will then start from the 901st data unit and look for the next data unit whose difference between the timestamp and 1728950430.000 is greater than or equal to 30 seconds, as the next keyframe. This process is repeated until all the time-series image data is processed, and finally a new sequence consisting of the extracted keyframe images and their corresponding timestamp indices is formed, generating a keyframe sequence.

[0065] The data standardization generation submodule receives keyframe sequences, establishes the correspondence between keyframe indexes and time axis calibrations, and performs structured encoding of image data and time indexes according to a unified format specification, encapsulating them into standardized road monitoring data.

[0066] Upon receiving the keyframe sequence, each keyframe data unit in the sequence is first assigned an integer index starting from 1 and incrementing. For example, the first keyframe in the sequence is assigned index 1, the second is assigned index 2, and so on. Then, this integer index is paired with the timestamp carried by the keyframe itself. For example, index 1 corresponds to timestamp 1728950400.000, and index 2 corresponds to timestamp 1728950430.000, thus constructing a mapping list from index number to timestamp. Next, the image data and time index are structured and encoded. This process follows a predefined unified format specification. Specifically, the image data is processed first, and the original 1920×1080 resolution RGB pixel matrix data is JPEG compressed, with the compression quality set... With a quantization factor of 85, this operation compresses approximately 5.9MB of raw data into a JPEG file of approximately 300KB. Then, this JPEG-formatted binary data stream is converted to a plain text string using Base64 encoding, resulting in a string like " / 9j / 4AAQSkZJRgABAQEAAAAAAAD / …". Next, the time information is processed, converting the original UNIX timestamp 1728950430.000 into a human-readable ISO8601 format string, i.e., "2025-10-15T12:00:30.000Z", while retaining the original UNIX timestamp value. Finally, the processed data is encapsulated into a JSON object containing the fields defined below, as shown in Table 1.

[0067] Table 1: Standardized Road Monitoring Data Structure

[0068]

[0069] As shown in Table 1, by performing such structured encoding and encapsulation on each frame in the keyframe sequence, the originally discrete image and time data are integrated into standardized data units with a unified structure and clear field definitions, and finally aggregated into standardized road monitoring data that can be directly called by subsequent systems.

[0070] Specifically, such as Figure 2 , 4 As shown, the video image recognition module includes:

[0071] The data stream receiving submodule acquires standardized road monitoring data, performs integrity verification and format parsing on the data packets, extracts continuous frame timestamps and image data matrices, sorts the timestamps in time sequence, pairs them frame by frame according to time order, and generates a time sequence input sequence set.

[0072] Acquiring standardized road monitoring data begins with the system's network listening port receiving a complete JSON data packet. The system immediately calls a data verification function, which first checks if the root level of the JSON object contains eight preset required fields: "Record ID," "UNIX Timestamp," "ISO Timestamp," "Camera Device ID," "Monitoring Point Location," "Image Encoding Format," "Image Resolution," and "Image Data." If the "Image Data" field is missing in a reception, the data packet is considered incomplete, an error log is logged, and the packet is discarded. For data packets that pass the integrity check, the system then initiates a format parsing process, extracting the value of the "UNIX Timestamp" field from the JSON structure to obtain a floating-point number, such as 1728950490.000. Simultaneously, it extracts the Base64 encoded string stored within the "Image Data" field and passes this string to the decoding unit, which performs Base64 encoding. The reverse operation restores the data to JPEG format binary data. Then, the image processing library decodes this binary data, generating a 1920×1080 pixel three-channel RGB image data matrix. When the system's data buffer accumulates, for example, three data packets in a short period, it extracts the timestamp sequence [1728950490.000, 1728950430.000, 1728950520.000] and the corresponding image data matrix. The system then performs an ascending sorting algorithm on this timestamp array, comparing and swapping element positions to arrange it into [1728950430.000, 1728950490.000, 1728950520.000]. Finally, based on this sorted timestamp list, the system re-pairs each timestamp with the image data matrix it was associated with at the beginning of parsing, ensuring that the order of the image frames is completely consistent with their actual chronological order, generating a time-series input sequence set.

[0073] The pixel difference comparison submodule extracts temporally adjacent images of the current frame and the previous frame based on the temporal input sequence set, performs pixel-level difference comparison operation on the current frame and the previous frame, calculates the change in pixel value and marks the change area, and generates an inter-frame difference feature map.

[0074] Based on the time-series input sequence set, the system sequentially extracts the preceding frame image I1 with timestamp t1 = 1728950490.000 and the current frame image I2 with timestamp t2 = 1728950520.000. First, it performs pixel-by-pixel grayscale conversion on these two images. For any pixel at coordinates (x, y) in the image, the three-channel RGB values ​​(R, G, B) are weighted and averaged. The grayscale value is calculated using the formula: =0.299×R + 0.587×G + 0.114×B. For example, in the preceding frame I1, a pixel located at coordinates (1060, 520) represents a normal asphalt road surface, with RGB values ​​of (120, 125, 130). The calculated grayscale value is 0.299×120 + 0.587×125 + 0.114×130 = 35.88 + 73.375 + 14.82 = The value is 124.075, which is rounded to 124. In the current frame I2, due to a dark-colored car illegally parking at this location, the RGB value of the pixel at the same coordinates (1060, 520) becomes (40, 45, 50). Its grayscale value is calculated as 0.299×40 + 0.587×45 + 0.114×50 = 11.96 + 26.415 + 5.7 = 44.075, which is rounded to 44. Subsequently, the system performs pixel-level difference comparison operations on the two grayscale images. That is, at each pixel position, the absolute value of the difference between the grayscale value of the current frame and the previous frame is calculated. For coordinates (1060, 520), the change in pixel value is |44-124| = 80. This operation will traverse all 2,073,600 pixels in the image, generating a 1920×1080 difference value matrix. Next, each difference value in this matrix is ​​compared with a preset pixel change threshold T. pixel For comparison, this threshold is set based on statistical analysis of the average pixel change rate between video frames under normal traffic flow over 24 consecutive hours. The average value of 18.5 is taken and multiplied by a fluctuation coefficient of 1.6, i.e., T. pixel =18.5×1.6=29.6, rounded to 30. During the comparison, if the difference value of a certain pixel is greater than 30, for example, 80 at coordinates (1060,520), then the corresponding position in the new binarized matrix is ​​marked as a white pixel value of 255. If the difference value is less than or equal to 30, for example, the difference value of another background area at coordinates (300,300) is 12, then the corresponding position is marked as a black pixel value of 0. After this process is completed, an inter-frame difference feature map is generated.

[0075] The feature extraction and recognition submodule receives inter-frame difference feature maps, inputs the difference images into a convolutional neural network to perform convolution operations to extract spatial feature vectors, combines the feature vectors with a target detection algorithm to perform temporal comparison analysis, identifies the boundaries of abnormal road areas, and generates a set of coordinates for the boundaries of abnormal areas.

[0076] The system receives an inter-frame difference feature map, which is a 1920×1080 binary matrix. Pixel clusters with a value of 255 indicate significantly changed object contours, while regions with a value of 0 represent static background. This difference map is used as input data and fed into the first convolutional layer of a pre-trained convolutional neural network. This layer uses 16 different 5×5 convolutional kernels to perform convolution operations on the input image. For example, the parameters of one of the convolutional kernels used to sharpen edge features are shown in Table 2.

[0077] Table 2: Example Table of Convolution Kernel Parameters

[0078] Position (column) 1 Position (column) 2 Position (column) 3 Position (column) 4 Position (column) 5 0 0 -1 0 0 0 -1 5 -1 0 -1 5 -12 5 -1 0 -1 5 -1 0 0 0 -1 0 0

[0079] As shown in Table 3, when the convolutional kernel slides on the difference map, it covers a 5×5 pixel region. The pixel values ​​within this region are multiplied by the coefficients at the corresponding positions of the convolutional kernel, and then all products are summed. For example, when the center of the convolutional kernel is aligned with a pixel located at the edge of the difference region, the result of the operation between the pixel values ​​of the covered region and the kernel coefficients might be (―12×255)+(5×255)+(5×255)+(5×0)+(5×0)+…=―3060+1275+1275+0+0+…=―510. This result forms a pixel value in the feature map of the next layer. Through the stacking of multiple convolutional and pooling operations, the network abstracts and compresses information layer by layer, finally outputting a 1×4096-dimensional spatial feature vector at the fully connected layer. The system generates the feature vector V from the current frame sequence. t The feature vector V generated from the previous frame sequence t―1 A time-series alignment analysis is performed. This analysis process calculates the sum of squares of the differences between corresponding elements of two vectors. If this value is lower than a preset stability threshold S... v =500 (this threshold is set by statistically analyzing the inter-frame changes of feature vectors from 1000 stationary object samples and taking the 95th percentile), and if a specific numerical cluster representing the core location of the object in the vector (e.g., bits 1024 to 1088 of the vector) remains stable for three consecutive time intervals (90 seconds), then the region is determined to have a persistent anomaly. The system then performs a reverse mapping calculation, tracing the source of the stable numerical clusters in the feature vector to their pixel coordinate range in the original difference map, and determining the minimum row number of the anomaly region on the image as y. min =510, the largest row number is y max =535, the smallest column number is x min =1048, the largest column number is x max =1082, generating the boundary coordinate set of the abnormal region [(1048,510),(1082,510),(1082,535),(1048,535)].

[0080] Specifically, such as Figure 2 , 5 As shown, the intelligent computing and analysis module includes:

[0081] The coordinate receiving submodule obtains the boundary coordinate set of the abnormal area, arranges the longitude and latitude values ​​of the boundary points in order, calculates the straight-line distance for each adjacent boundary point and forms a boundary point spacing sequence, stores the spacing sequence with the original coordinate points in a positional correspondence, and generates a boundary coordinate sequence.

[0082] The system obtains the coordinate set of the generated anomaly region boundary, which is the upper left corner (1048, 510) and lower right corner (1082, 535) in the image coordinate system. Using preset camera intrinsic and extrinsic parameter matrices (including the camera's precise GPS coordinates and attitude parameters in geographic space, for example, the camera's GPS coordinates are 113.35012°E, 23.12005°N, altitude 50 meters, pitch angle -30°, and heading angle 90°), the system converts the image coordinates into actual geographic coordinates. Specifically, the conversion process involves mapping the image pixel coordinates to the road surface plane in the three-dimensional world coordinate system through projection transformation and inverse matrix operations. Converting to latitude and longitude coordinates, the top-left corner point (1048, 510) corresponds to longitude 113.35105° and latitude 23.11998°, and the bottom-right corner point (1082, 535) corresponds to longitude 113.35118° and latitude 23.11990°. From this, the coordinates of the other two corner points of the rectangle are derived as (113.35118°, 23.11998°) and (113.35105°, 23.11990°), thus forming a set P = {P1, P2, P3, P4} containing four geographical boundary points, where:

[0083] P1=(113.35105°,23.11998°), P2=(113.35118°,23.11998°), P3=

[0084] (113.35118°,23.11990°), P4=(113.35105°,23.11990°);

[0085] Next, the longitude and latitude values ​​of the boundary points are arranged in order, with the system defaulting to a clockwise direction, starting from the top left corner point P1. The boundary point sequence is represented as S. P = [P1, P2, P3, P4, P1], where P1 is repeated to form a closed path. The straight-line distances between adjacent boundary points are calculated one by one to form a sequence of boundary point spacings. The system then uses the Haversine formula to calculate the spherical distance. The formula is:

[0086]

[0087] Where r is the Earth's average radius of 6371 kilometers, φ is latitude (in radians), λ is longitude (in radians), and Δφ and Δλ are the differences in latitude and longitude between two points, respectively. For example, to calculate the distance from P1 to P2: P1 longitude λ1 = 113.35105°, latitude φ1 = 23.11998°; P2 longitude λ2 = 113.35118°, latitude φ2 = 23.11998°. Converting these to radians: λ 1_rad ≈1.978396rad, φ 1_rad ≈0.403513rad, λ 2_rad ≈1.978398rad, φ 2_rad ≈0.403513rad, then Δφ=0rad, Δλ=λ 2_rad ―λ 1_rad =0.000002rad, substituting into the formula, we get d. 12 ≈0.0116 kilometers, or 11.6 meters. Similarly, calculate the distance d from P2 to P3. 23 ≈0.0089 km, or 8.9 m, the distance d from P3 to P4 34 ≈11.6 meters, the distance d from P4 to P1 41 ≈8.9 meters, forming a boundary point spacing sequence D =

[0088] [11.6m, 8.9m, 11.6m, 8.9m], the spacing sequence is stored in correspondence with the original coordinate points. The system associates each element of the D sequence with S. P The corresponding starting points in the sequence are associated. For example, the spacing of 11.6 meters is associated with P1, which represents the distance from P1 to the next point P2, forming a list. Each element contains the point coordinates and the length of its successor edge, generating a boundary coordinate sequence.

[0089] The parameter probability calculation submodule calls the boundary coordinate sequence, calculates the area value for each closed coordinate region, divides the area value into intervals, uses the random forest algorithm to perform discrimination calculations and outputs the probability value sequence to obtain the regional risk probability sequence.

[0090] The boundary coordinate sequence generated in the preceding steps, i.e., the closed set of geographic coordinate points S, is invoked. P =

[0091] [P1,P2,P3,P4,P1], where:

[0092] P1=(113.35105°,23.11998°), P2=(113.35118°,23.11998°), P3=

[0093] (113.35118°,23.11990°), P4=(113.35105°,23.11990°);

[0094] The system calculates the area value for each closed coordinate region individually. It uses the Gaussian area formula from geodesy, which calculates the area by summing the cross products of the vertex coordinates of a polygon and then dividing by two. For a polygon with n vertices (x... i ,y i The area of ​​the polygon formed by ( ) is:

[0095]

[0096] Where (x) n+1 ,y n+1 (x1, y1) is equivalent to (x1, y1). In actual calculations, the latitude and longitude are first converted to local planar coordinates in meters. With P1 as the origin, P1 = (0, 0). According to d 12 = 11.6 meters, d 23

[0097] = 8.9 meters, so the approximate plane coordinates can be calculated as P1 = (0,0), P2 = (11.6,0), P3 = (11.6,-8.9), P4 = (0,-8.9). Substituting these into the Gaussian area formula:

[0098]

[0099] The calculation shows that:

[0100]

[0101] The area values ​​are then divided into intervals. The system divides the area into three intervals based on empirical values, as shown in Table 3.

[0102] Table 3. Division of Abnormal Area Ranges

[0103] <![CDATA[Area range (m 2 )]]> Classification A≤10 small 10<A≤100 medium A>100 Large

[0104] As shown in Table 3, based on the area of ​​103.24 square meters, the abnormal area was classified as "large". Then, a random forest algorithm was used for discrimination and output a probability value sequence. The system uses the area value (103.24) and other relevant parameters extracted from the aforementioned visible light and infrared features, such as the average gray value (85), average temperature (55.8℃), average edge gradient (58.6), and frequency of occurrence (0.83) of the abnormal area, as input features into a pre-trained random forest classifier. This classifier contains 100 decision trees, each of which evaluates the training data... Decisions are made using random sampling and feature subsets. The probability of the current anomaly type (e.g., "vehicle cargo falling off") is voted on. For example, one tree judges it as "spillage" based on "area greater than 80 square meters" and outputs 1 vote, while another tree judges it as "pothole" based on "low average gray value" and "high edge gradient" and outputs 0 votes. After voting by the decision trees, the final probability value is the proportion of decision trees supporting a certain category out of the total number of trees. Assuming that the voting proportion for the category "vehicle cargo falling off" is 75 votes, 75 / 100 = 0.75, the regional risk probability sequence is obtained.

[0105] The risk area merging submodule compares the probability differences between adjacent areas based on the regional risk probability sequence, merges areas with differences below the threshold benchmark, redefines the boundary coordinates of the merged areas and integrates the risk probability parameters to generate a road enforcement risk distribution map.

[0106] Based on the regional risk probability sequence generated by the aforementioned steps, assuming that three adjacent abnormal regions R are currently identified... A R B R C Their risk probabilities are P A =0.75 (vehicle cargo falling off), P B =0.72 (vehicle cargo falling off), P C =0.30 (road surface water). The system first compares the probability differences between adjacent areas and calculates P. A With P B The absolute difference between them |P A —P B |=|0.75―0.72|=0.03, and P B With P C The absolute difference between them |P B —P C |=|0.72―0.30|=0.42, the system sets the difference value to be lower than the threshold baseline value T. merge The regions are merged, with a threshold benchmark value T. mergeThe basis for this setting is that experience shows that physically adjacent anomalies of the same type have similar risk probabilities, while the probabilities of different types of anomalies differ significantly. For example, the risk probability difference between two adjacent spills or connected craters is less than 0.10 to 0.15, while the probability difference between spills and puddles is much greater. Therefore, T is set as... merge Setting it to 0.10 will cause the system to |P A —P B |=0.03 ​​and T merge =0.10 for comparison. Since 0.03 < 0.10, then R A and R B Marked as mergeable, |P B —P C |=0.42 and T merge =0.10 for comparison. Since 0.42 > 0.10, then R B and R C Instead of merging, the system redefines the boundary coordinates of the merged region for R. A and R B When merging the two bounding rectangles, the system calculates the union of their smallest bounding rectangles, resulting in a new, larger bounding rectangle. For example, if R... A The boundary is from (113.35105°, 23.11998°) to (113.35118°, 23.11990°), while R B The boundary is from (113.35120°, 23.11995°) to (113.35135°, 23.11985°). The boundary of the merged new region will be the smallest rectangle containing all points of the two rectangles. Its upper left corner will be the point with the smallest longitude and largest latitude among the two, and its lower right corner will be the point with the largest longitude and smallest latitude among the two. The calculated upper left corner is (113.35105°, 23.11998°) and the lower right corner is (113.35135°, 23.11985°). Integrating the risk probability parameters, the risk probability of the merged new region is taken as the average of the risk probabilities of the regions before the merge, i.e., P. AB =(P A +P B ) / 2=(0.75+0.72) / 2=0.735, where 0.735 represents the risk of “vehicle cargo falling off” in the merged area, resulting in a road enforcement risk distribution map.

[0107] Specifically, such as Figure 2 , 6 As shown, the risk level quantification module includes:

[0108] The distribution map parsing submodule obtains the pixel distribution matrix and location information in the road law enforcement risk distribution map, decomposes the pixel distribution matrix into multiple partition units according to the grid index rules, and indexes and matches the partition units with the corresponding geographic coordinates to generate a partition mapping matrix.

[0109] Obtain the pixel distribution matrix and location information from the generated road enforcement risk distribution map. This map, at the data level, is a 2000×500 logical pixel matrix covering a geographical area with a total length of 2 kilometers and a width of 50 meters across the monitored road segment. Each pixel corresponds to a 1-meter × 1-meter actual geographical area. Most pixels in the matrix have a value of 0, while the aforementioned merged risk area R... AB The corresponding pixel area has a pixel value of 0.735, which is in the risk area R. C The corresponding pixel regions all have a pixel value of 0.30. The system then decomposes this 2000×500 pixel distribution matrix into multiple partition units according to the grid indexing rules. Here, the grid indexing rules define each partition unit as a 10×10 pixel square, i.e., an area with an actual geographical area of ​​10 meters × 10 meters. Therefore, the entire matrix is ​​decomposed into 10,000 partition units of 200×50 pixels each. Each partition unit is uniquely identified by its row and column number (i, j) in the grid, where i ranges from 0 to 199 and j ranges from 0 to 49. Next, the system performs index matching between the partition units and their corresponding geographical coordinates. Specifically, the process is as follows... Based on the starting geographic coordinates of the entire monitoring area (e.g., 113.35000°E, 23.12050°N) and the 1-meter dimension represented by each pixel, the system calculates the four corner coordinates of the geographic boundary of each 10×10 partition unit. For example, for the partition unit with index (35,11), the system calculates its covered geographic range as 113.35100°E to 113.35110°E, and 23.11990°N to 23.12000°N. Simultaneously, the system calculates the average value of all pixel values ​​within this partition unit as the pixel distribution value for that partition. If partition (35,11) falls exactly within the aforementioned risk area R... AB Within the area, the pixel distribution value of the partition is 0.735. If a part of the partition falls into the area, the average value is calculated by area weighting to generate the partition mapping matrix.

[0110] The risk coefficient quantification submodule, based on the partition pixel distribution values ​​in the partition mapping matrix, calls the risk correlation coefficient threshold benchmark, compares each partition pixel distribution value with the threshold benchmark item by item, converts the comparison results into a continuous risk probability, and obtains the partition risk persistence interval value.

[0111] Based on the partition pixel distribution values ​​in the generated partition mapping matrix, the system calls a preset risk correlation coefficient threshold benchmark. This benchmark is set separately for different types of road anomaly events (such as "debris spills," "potholes," and "water accumulation"). For the current event type "vehicle cargo falling off," the threshold benchmark is based on data analysis of 2000 similar historical events. The analysis results show that 98% of events with initial pixel distribution values ​​below 0.05 are ultimately confirmed as false alarms, while 95% of events with values ​​above 0.60 constitute a persistent risk requiring immediate action. Therefore, a low-risk threshold T is set. low =0.05 and high-risk threshold T high =0.60. The system then compares the pixel distribution value of each partition with the threshold benchmark item by item. Taking the partition with the index (35,11) as an example, its partition pixel distribution value V (35,11) =0.735, the system will compare this value with T low and T high The comparison revealed that 0.735 > 0.60, indicating that the risk value of this partition falls within the high-risk range. The comparison result was then converted into a continuous risk probability. The conversion rule here is: if the distribution value V is lower than T... low If V is higher than T, then the probability of risk is 0; high If the probability is 1, then the risk probability is 1; if V is between the two, then the probability is obtained through linear interpolation formula P. s =(V―T) low ) / (T high ―T low Calculations are performed on the partition (35, 11), since its distribution value of 0.735 is greater than T. high The probability of continuous risk is 0.60, and its continuous risk probability P s(35,11) It is directly assigned a value of 1.0, while for another partition (80,20), assuming its pixel distribution value is 0.45, its continuous risk probability P is... s(80,20) The value is calculated as (0.45―0.05) / (0.60―0.05)=0.40 / 0.55≈0.727, which gives the interval value of the risk persistence of the partition.

[0112] The location labeling generation submodule calls the interval values ​​of the risk persistence of the zone and the zone index information, performs a level classification operation on the interval values, establishes a correspondence between the level classification results and the zone coordinate points, and superimposes the labels in the coordinate system of the road risk distribution map to obtain the road risk level map.

[0113] The system uses the calculated interval values ​​of partition risk persistence and partition index information to calculate the continuous risk probability (P) for each partition. s The risk level is classified according to a preset risk level classification standard, as shown in Table 4.

[0114] Table 4: Classification of Road Law Enforcement Risk Levels

[0115] <![CDATA[Risk persistence interval value (P s )]]> Risk level Level label (0.8，1.0] Level 1 (High Risk) 1 (0.5，0.8] Level 2 (Severe) 2 (0.2，0.5] Level 3 (General) 3 [0，0.2] Level 4 (Minor) 4

[0116] As shown in Table 4, this table lists the correspondence between risk persistence interval values ​​and four risk levels. The system will assign P to each partition. s The values ​​are compared with the intervals in Table 4 to determine their risk level; for example, for the partition (35, 11), its P... s The value is 1.0, falling within the interval (0.8, 1.0], therefore its risk level is classified as Level 1. For the partition (80, 20), its P... s The value is 0.727, falling within the range of (0.5, 0.8], therefore its risk level is classified as Level 2. Subsequently, the system establishes a correspondence between the level classification results and the coordinate points of the partitions. That is, a data structure is created to associate the geographic coordinate range of partition (35, 11) (113.35100° to 113.35110° E, 23.11990° to 23.12000° N) with the level identifier "1", and associate the geographic coordinates of partition (80, 20) with the level identifier "2". The labels are then overlaid in the coordinate system of the road risk distribution map. The overlay label here is generated on the original geographic information layer to produce a new vector layer data. This data contains the geographic boundary polygons of partitions with risk levels greater than Level 4 and their corresponding level identifiers, thus obtaining the road risk level map.

[0117] Specifically, such as Figure 2 , 7 As shown, the decision generation module includes:

[0118] The risk level analysis submodule extracts the spatial coordinates and risk level values ​​of the area based on the road risk level map, calls the risk level threshold to compare the levels in different regions, matches the results with the spatial coordinates, and generates a risk level distribution matrix.

[0119] Based on the generated road risk level map, the system first extracts areas in the map whose risk level is not level four (minor). For example, it extracts the spatial coordinates of partition (35,11), namely the geographic coordinates of its center point (113.35105°E, 23.11995°N) and its risk level value, namely the level identifier "1", and the geographic coordinates of the center point of partition (80,20), (113.35855°E, 23.11845°N) and its risk level value "2". The system then calls a risk level threshold to compare the risk levels of the partitions. Here, the risk level threshold T is used. levelThe threshold is set to 3. This threshold is based on the management's responsiveness and risk tolerance. Level 1 and Level 2 risks are defined as events that require immediate response, Level 3 is to be observed, and Level 4 is to be ignored. The system will assign a level value of 1 to the partition (35,11) and a threshold value of T. level The risk level is compared with the risk level of 3. Since 1 < 3, the partition is determined to be a high-concern area. Similarly, the risk level of partition (80, 20) is compared with the risk level of 3. Since 2 < 3, the partition is also determined to be a high-concern area. If there is a partition with a risk level of 4, it is filtered out because 4 > 3. Then, this comparison result is matched with the spatial coordinates of the partition. This matching process integrates the risk level of the high-concern area and its corresponding geographic coordinates into a new data structure. Specifically, it generates a list containing multiple entries. Each entry records the index, geographic coordinates and risk level of a high-concern area. For example, the first item in the list is {index: (35, 11), coordinates: (113.35105°, 23.11995°), risk level: 1}, and the second item is {index: (80, 20), coordinates: (113.35855°, 23.11845°), risk level: 2}, generating a risk level distribution matrix.

[0120] The priority calculation submodule calls the level value and spatial coordinates in the risk level distribution matrix, sets the priority benchmark value, performs a weighted operation on the regional level value, compares the result with the priority benchmark value, and generates the patrol priority sequence value.

[0121] The generated risk level distribution matrix is ​​invoked. This matrix contains the level values ​​and spatial coordinates of high-concern areas, such as {index:(35,11), coordinates:(113.35105°, 23.11995°), level:1} and {index:(80,20), coordinates:(113.35855°, 23.11845°), level:2}. The system first sets a priority baseline value P. base =12.0. This benchmark value is set based on statistical analysis of historical data. Statistics show that when a region's overall priority score exceeds 12.0, the probability of it causing traffic congestion or secondary accidents within the next 30 minutes exceeds 85%, thus requiring the highest level of response. Next, the system performs a weighted calculation on the priority score of each region. This calculation comprehensively considers three factors: the risk level itself, the region's importance, and dynamic traffic flow. The calculation method is: Priority P = W level ×W loc ×C traffic W level This refers to the risk level weighting. According to the risk level classification in Table 4, Level 1 risk is weighted at 10.0, Level 2 at 5.0, and Level 3 at 2.0; W locThis refers to location weights, which are set based on the administrative level and function of the roads within the area. For example, urban expressways have a weight of 1.5, arterial roads 1.2, and secondary arterial roads 1.0. Zone (35,11) is located on an urban expressway, therefore W... loc(35,11) =1.5, partition (80,20) is located on the main road, therefore W loc(80,20) =1.2; C traffic This is the real-time traffic flow coefficient, obtained through access to real-time traffic data from the traffic management platform. Its value ranges from 0.5 (smooth traffic) to 2.0 (severe congestion). Assuming the current traffic congestion occurs on the road segment in zone (35,11), its C... traffic The value is 1.3, and the traffic in the section (80,20) is relatively smooth. traffic The value is 0.8, and the system calculates for partition (35, 11): P (35,11) =10.0×1.5×1.3=19.5, calculate for partition (80,20): P (80,20) =5.0 × 1.2 × 0.8 = 4.8, and then the calculation result is compared with the priority reference value P. base =12.0 for comparison. The priority of partition (35,11) is 19.5>12.0, and the priority of partition (80,20) is 4.8<12.0. The patrol priority sequence value is generated.

[0122] The task list generation submodule sorts the areas according to the patrol priority sequence value, calls the sorting results and spatial location, judges and selects law enforcement resources, merges the sorting and judgment results, and generates a road risk warning list.

[0123] Based on the generated patrol priority sequence values ​​containing regional priority scores, i.e., the priority of partition (35,11) is 19.5 and the priority of partition (80,20) is 4.8, the system first sorts the regions in descending order of priority values ​​to obtain an ordered list: [{index:(35,11), priority:19.5}, {index:(80,20), priority:4.8}]. Then, the system calls the spatial location information in the sorting results and a real-time law enforcement resource database to judge and select law enforcement resources. The database records the status of all available law enforcement units, as shown in Table 5.

[0124] Table 5: Real-time Status Table of Law Enforcement Resources

[0125] law enforcement agencies Current location (longitude, latitude) state Responsible area Patrol Car A (113.35200°，23.12100°) Available expressway Patrol Car B (113.36000°，23.11800°) Available Main road Patrol Car C (113.34500°，23.11500°) During the mission -

[0126] As shown in Table 5, the system first processes the highest priority task, namely processing partition (35,11), with location coordinates (113.35105°, 23.11995°). The system selects the law enforcement unit responsible for area matching (expressway) with a status of "available", namely patrol car A. Then, it calculates the straight-line distance from patrol car A to the target location, which is approximately 0.15 kilometers. The system determines that this resource is the optimal choice at present and assigns it to this task. Subsequently, the system processes the next task, namely processing partition (80,20), with location coordinates (113.35855°, 23.11845°). The system selects the law enforcement unit responsible for area matching (main road) with a status of "available", namely patrol car B, and calculates its distance to the target location, which is approximately 0.16 kilometers. The system assigns it to this task. Finally, the system merges the sorting results with the resource judgment results, integrating the task description, location, priority, assigned law enforcement unit, and other information into a structured task entry, generating a road risk warning list.

[0127] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A traffic enforcement intelligent management system based on video and image recognition technology, characterized in that: The system includes: The highway video surveillance acquisition module acquires visible light video through cameras, extracts one frame per second at fixed time intervals, records key frame timestamps, generates standardized road monitoring data, and transmits it to the video image recognition module. The video image recognition module receives the standardized road monitoring data, performs pixel-level difference comparison between the current frame and the previous frame, inputs it into the convolutional neural network for feature extraction, performs temporal comparison in conjunction with target detection, identifies road traffic safety risk characteristics, generates an abnormal area boundary coordinate set, and transmits it to the intelligent computing and analysis module. The intelligent computing and analysis module receives the set of boundary coordinates of the abnormal area, uses the random forest algorithm to calculate the risk probability of the area parameter of the abnormal area, merges and associates risk areas based on the probability threshold, generates a road law enforcement risk distribution map, and transmits it to the risk level quantification module. The risk level quantification module receives the road enforcement risk distribution map, quantifies and classifies the associated risk areas based on the risk probability value, marks the risk location coordinates, generates a road risk level map, and transmits it to the decision generation module.

2. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 1, characterized in that, The standardized road monitoring data includes spatial positioning information, image feature information, and acquisition time sequence information. The abnormal area boundary coordinate set includes a spatial coordinate information set, boundary contour parameters, and time series identifiers. The road risk distribution map includes risk areas, risk probabilities, and merged areas. The road risk level map includes risk level categories, risk persistence characteristics, and spatial location annotations.

3. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 1, characterized in that, The highway video surveillance acquisition module includes: The image acquisition submodule acquires visible light video stream data captured by the camera device, performs frame-by-frame parsing of the video stream to extract the pixel matrix, synchronously acquires the time code and records the timestamp, binds the image data with time information, and generates a time-series image dataset. The fixed-interval frame extraction submodule, based on the time-series image dataset, periodically extracts video frame sequences according to a fixed time interval parameter, extracts key frame images that meet the interval requirements, records the timestamp index position of the key frames on the time axis, and generates a key frame sequence. The data standardization generation submodule receives the keyframe sequence, establishes the correspondence between the keyframe index and the time axis calibration, performs structured encoding on the image data and time index according to a unified format specification, and encapsulates it into standardized road monitoring data.

4. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 1, characterized in that, The video image recognition module includes: The data stream receiving submodule acquires the standardized road monitoring data, performs integrity verification and format parsing on the data packets, extracts continuous frame timestamps and image data matrices, sorts the timestamps in time sequence, pairs them frame by frame according to time order, and generates a time sequence input sequence set. The pixel difference comparison submodule extracts the temporally adjacent current frame and previous frame images based on the temporally input sequence set, performs pixel-level difference comparison operation on the current frame and previous frame, calculates the pixel value change and marks the change area, and generates an inter-frame difference feature map. The feature extraction and recognition submodule receives the inter-frame difference feature map, inputs the difference image into the convolutional neural network to perform convolution operations to extract spatial feature vectors, combines the target detection algorithm to perform temporal comparison analysis on the feature vectors, identifies the boundaries of abnormal road areas, and generates a set of coordinates for the boundaries of abnormal areas.

5. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 1, characterized in that, The intelligent computing and analysis module includes: The coordinate receiving submodule acquires the boundary coordinate set of the abnormal area, arranges the longitude and latitude values ​​of the boundary points in order, calculates the straight-line distance for each adjacent boundary point and forms a boundary point spacing sequence, stores the spacing sequence with the original coordinate points in a positional correspondence, and generates a boundary coordinate sequence. The parameter probability calculation submodule calls the boundary coordinate sequence, calculates the area value for each closed coordinate region, divides the area value into intervals, performs discrimination calculation using the random forest algorithm, and outputs the probability value sequence to obtain the regional risk probability sequence. The risk area merging submodule compares the probability difference values ​​between adjacent areas based on the regional risk probability sequence, merges areas with difference values ​​lower than the threshold benchmark value, redefines the boundary coordinates of the merged areas and integrates the risk probability parameters to generate a road enforcement risk distribution map.

6. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 1, characterized in that, The risk level quantification module includes: The distribution map parsing submodule obtains the pixel distribution matrix and location information in the road law enforcement risk distribution map, decomposes the pixel distribution matrix into multiple partition units according to the grid indexing rules, and performs index matching between the partition units and the corresponding geographic coordinates to generate a partition mapping matrix. The risk coefficient quantification submodule, based on the partition pixel distribution values ​​in the partition mapping matrix, calls the risk correlation coefficient threshold benchmark, compares each partition pixel distribution value with the threshold benchmark item by item, converts the comparison results into a continuous risk probability, and obtains the partition risk persistence interval value. The location labeling generation submodule calls the partition risk persistence interval value and partition index information, performs a level classification operation on the interval value, establishes a correspondence between the level classification result and the partition coordinate point, and superimposes the label in the coordinate system of the road risk distribution map to obtain the road risk level map.

7. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 6, characterized in that, The risk correlation coefficient threshold is a numerical benchmark established based on the abnormal pixel distribution values ​​of the road and the preset risk judgment standard; The partition risk persistence interval value is a continuous numerical interval calculated from the comparison result of the partition pixel distribution value relative to the risk correlation coefficient threshold.

8. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 1, characterized in that, The decision generation module receives the road risk level map, sorts and calculates the priority of law enforcement patrols based on the risk level classification, selects and judges law enforcement resources according to the risk location distribution, and outputs a road risk warning list. The road risk warning list includes a patrol priority sequence, law enforcement dispatch plan, and task list.

9. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 8, characterized in that, The decision generation module includes: The risk level analysis submodule extracts the spatial coordinates and risk level values ​​of the area based on the road risk level map, calls the risk level threshold to compare the levels by partition, matches the results with the spatial coordinates, and generates a risk level distribution matrix. The priority calculation submodule calls the level value and spatial coordinates in the risk level distribution matrix, sets the priority benchmark value, performs a weighted operation on the regional level value, compares the result with the priority benchmark value, and generates the patrol priority sequence value. The task list generation submodule sorts the areas according to the patrol priority sequence value, calls the sorting results and spatial location, judges and selects law enforcement resources, merges the sorting and judgment results, and generates a road risk warning list.

10. The intelligent traffic enforcement management system based on video and image recognition technology according to claim 9, characterized in that, The risk level threshold is the boundary of the interval that divides the original risk value into different risk level values; The patrol priority sequence value is a value calculated by weighting the risk level value, spatial location parameter, and priority benchmark value.

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