Intelligent traffic monitoring image transmission method and system based on 5G technology

Abstract

The invention relates to the technical field of monitoring image transmission, in particular to an intelligent traffic monitoring image transmission method and system based on the 5G technology, and the method specifically comprises the steps: calculating the content key coefficient of each image block through the synchronous analysis of the traffic road condition and traffic jam condition of an intelligent traffic monitoring image video frame, determining the transmission priority of each image block in the same video frame; the method comprises the following steps of: constructing time slot interference in the transmission process of each uplink channel according to signal interference caused by cross time slot interference generated among different base stations and channel occupancy rate overlapping conditions in the transmission process of a 5G uplink channel, wherein the signal interference is caused by the traffic monitoring image video data packet due to the cross time slot interference generated among the different base stations; and the uploading sequence of the video frame content in the intelligent traffic monitoring image is adjusted based on the transmission priority and the time slot interference, so that the delay jitter and packet loss condition aggravation risk of the traffic monitoring image in the 5G transmission process is effectively reduced, and the 5G transmission quality of the intelligent traffic monitoring image is guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of monitoring video transmission, and specifically to a method and system for intelligent transportation monitoring video transmission based on 5G technology. Background Art

[0002] With the acceleration of the urbanization process, the traffic flow has increased sharply. Traditional traffic monitoring means face bottlenecks such as complex wiring, limited coverage, and high data transmission latency, and it has become difficult to meet the needs of modern traffic management. Intelligent transportation has become an inevitable trend in future traffic development; with its characteristics of high speed, low latency, and large connection, 5G technology has reconstructed the technical paradigm of intelligent transportation monitoring video transmission, providing strong technical support for promoting the transformation of traffic management from experience-driven to data-driven, and laying a solid foundation for the intelligent management of future traffic.

[0003] In the traditional TDD (Time Division Duplexing) time division duplex communication technology mode, the demand for network bandwidth is mainly concentrated in the downlink, resulting in the uplink resources only accounting for 20 - 30% of the overall bandwidth. Although the method of time slot ratio can allocate more resources to the uplink and improve the uplink peak rate and capacity, it is prone to cross-slot interference problems. That is, when adjacent base stations occupy the same frequency band, it may occur that different base stations use the same time slot to transmit downlink data and uplink data respectively, and then interference occurs between the downlink signals and uplink signals of different base stations, resulting in a decline in video quality, increased latency jitter, and packet loss rate during the 5G transmission of intelligent transportation monitoring videos. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for intelligent transportation monitoring video transmission based on 5G technology. The specific technical solutions adopted are as follows: In the first aspect, an embodiment of this application provides a method for intelligent transportation monitoring video transmission based on 5G technology. The method includes the following steps: Obtain the intelligent transportation monitoring video. In the transmission section, collect the SINR data, transmission delay data, and channel occupancy rate data of each uplink channel at each moment during the uplink transmission of the video, and construct a SINR data sequence, a transmission delay data sequence, and a channel occupancy rate data sequence; Divide each video frame in the video into image blocks; calculate the vehicle congestion degree of each image block in each video frame based on the vehicle displacement change between the corresponding image blocks in each video frame and the previous video frame, combine the vehicle speed in the image block, calculate the content key coefficient of each image block, and determine the transmission priority of each image block in the same video frame; Based on the sharp peak characteristics of each peak in the fitting curve of the channel occupancy rate data sequence and the degree of data chaos in the transmission delay data sequence, the occupancy delay in the transmission process of each uplink channel is constructed; based on the difference between adjacent elements in the SINR data sequence, the signal quality interference degree in the transmission process of each uplink channel is constructed; based on the occupancy delay and the signal quality interference degree, the slot disturbance in the transmission process of each uplink channel is constructed; Based on the transmission priority and the slot disturbance, the upload order of the video frame content in the intelligent transportation monitoring image is adjusted.

[0005] In one embodiment, the process of obtaining the vehicle congestion degree of each image block of each video frame is as follows: Cluster the gray values of all pixel points in the grayscale image of each image block to obtain each clustering cluster and the clustering center of the clustering cluster in each image block; use image recognition technology to obtain the number of the same vehicles between any clustering cluster in the a-th image block of the current video frame and any clustering cluster in the a-th image block of the previous video frame; If the number of the same vehicles is greater than or equal to the preset quantity threshold, the corresponding two clustering clusters are formed into a same-region clustering cluster group; Calculate the metric distance between the clustering centers of the two clustering clusters in the same-region clustering cluster group, denoted as the first distance, and use the sum value of the first distances of all same-region clustering cluster groups between the a-th image block of the current video frame and the a-th image block of the previous video frame as the vehicle congestion degree of the a-th image block of the current video frame.

[0006] In one embodiment, the process of obtaining the content key coefficient of each image block is as follows: Calculate the difference between the vehicle speed of each vehicle and the lane speed limit in the current video frame, denoted as the first difference; the expression of the content key coefficient is: In the formula, is the content key coefficient of the a-th image block of the i-th video frame in the traffic monitoring image video; is the sum value of the first differences of the vehicle speeds of all vehicles in the a-th image block of the i-th video frame in the traffic monitoring image video; is the vehicle congestion degree of the a-th image block of the i-th video frame in the traffic monitoring image video; is the exponential function with the natural constant e as the base; is a preset extremely small positive number.

[0007] In one embodiment, the process of obtaining the transmission priority of each image block in the same video frame is as follows: Determine the transmission priority of all image blocks in each video frame in descending order. The greater the content key coefficient, the higher the transmission priority of the image block.

[0008] In one embodiment, the process of obtaining the occupancy delay in the transmission process of each uplink channel is as follows: For the fitting curve of the channel occupancy rate data sequence of each uplink channel, calculate the curvature at the peak value of each peak in the fitting curve; obtain the peak period fluctuation coefficient of each peak based on the time interval between adjacent peaks; Calculate the sum value of the curvatures at all peaks on the fitting curve, denoted as the first sum value; calculate the sum value of the peak period fluctuation coefficients at all peaks on the fitting curve, denoted as the second sum value; calculate the Shannon entropy of all data in the transmission delay data sequence of each uplink channel; divide the product of the first sum value and the Shannon entropy of each uplink channel by the second sum value, and take the calculation result as the occupancy delay in the transmission process of each uplink channel.

[0009] In one embodiment, the process of obtaining the peak period fluctuation coefficient is as follows: Calculate the time interval between the peaks of each peak and its next peak in the fitting curve, denoted as the first time interval; denote the absolute value of the difference between the average value of the first time intervals of all peaks and the first time interval of each peak as the peak period fluctuation coefficient of each peak.

[0010] In one embodiment, the process of obtaining the signal quality interference degree in the transmission process of each uplink channel is as follows: Calculate the difference between each data and its next data in the SINR data sequence of each uplink channel, denoted as the first difference, and take the sum value of the first differences of all data in the SINR data sequence as the signal quality interference degree in the transmission process of each uplink channel.

[0011] In one embodiment, the slot disturbance in the transmission process of each uplink channel is: the product of the occupancy delay and the signal quality interference degree of each uplink channel.

[0012] In one embodiment, the adjustment of the upload order of video frame content in intelligent transportation monitoring images is specifically as follows: When the slot disturbance of the uplink channel where the video data packet of the intelligent transportation monitoring image is transmitted is greater than or equal to the preset slot disturbance evaluation threshold Q, divide all uplink channels according to the priority of the slot disturbance. The smaller the slot disturbance, the higher the priority of the uplink channel to be selected; transmit the data packets of each image block of the traffic monitoring image video frame to the 5G base station through the uplink channels with the highest to lowest priority in turn according to the priority.

[0013] In a second aspect, an embodiment of the present application further provides a smart transportation monitoring image transmission system based on 5G technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above methods are implemented.

[0014] The embodiment of the present application has at least the following beneficial effects: Through the synchronous analysis of the traffic conditions and traffic congestion conditions of the traffic monitoring images of the smart transportation monitoring video frames, the present application calculates the content key coefficients of each image block, determines the transmission priority of each image block in the same video frame, and realizes the criticality division of the content of the smart transportation monitoring video data to be transmitted on the 5G uplink, effectively improving the flexibility and accuracy of the monitoring video transmission of the smart transportation; during the transmission process of the 5G uplink channel, the signal interference and the overlapping condition of the channel occupancy rate suffered by the traffic monitoring video data packets due to the cross-slot interference between different base stations are used to construct the slot interference susceptibility during the transmission process of each uplink channel. Further, a slot priority allocation method during the 5G transmission of the smart transportation monitoring image is provided. Based on the transmission priority and the slot interference susceptibility, the upload order of the video frame content in the smart transportation monitoring image is adjusted, effectively reducing the risk of the aggravation of the delay jitter and packet loss rate during the 5G transmission of the traffic monitoring image, and ensuring the 5G transmission quality of the smart transportation monitoring image. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a step flowchart of a smart transportation monitoring image transmission method based on 5G technology provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the acquisition process of the occupancy delay. Detailed Embodiments

[0017] In order to further elaborate on the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific embodiments, structures, features, and effects of the smart transportation monitoring image transmission method and system based on 5G technology proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs.

[0019] The following specifically describes the specific solutions of the intelligent transportation monitoring image transmission method and system provided by this application with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , which shows the step flowchart of the intelligent transportation monitoring image transmission method provided by an embodiment of this application. The method includes the following steps: Step S1, obtain the intelligent transportation monitoring image. In the transmission section, collect the SINR data, transmission delay data, and channel occupancy rate data of each uplink channel at each moment during the uplink transmission of the image, and construct a SINR data sequence, a transmission delay data sequence, and a channel occupancy rate data sequence.

[0021] By arranging N high-definition cameras that support a resolution of 1080P or higher, a frame rate of 25fps, and 5G at different traffic intersections on the road to be detected, obtain the monitoring video of the road to be detected. Use the Gaussian filtering algorithm to eliminate the vehicle motion blur and high-frequency noise in the monitoring video, improve the quality of the monitoring video, embed OSD information in the video stream, and ensure the integrity of the traffic monitoring image data through digital watermark technology to ensure the security of the intelligent transportation monitoring video. The OSD information embedded in this application is time, lane number, and lane speed limit. Preferably, the value of N is set to 3 in the embodiment of this application. As other embodiments of this application, the implementer can set the value of N according to the actual situation.

[0022] Compress the obtained monitoring video data into the H.265 / HEVC format to reduce the data volume, and uniformly set the monitoring video frame size to . It should be noted that for the setting of the video frame size, the implementer can set it according to the actual situation, and this application does not make specific restrictions.

[0023] To improve the transmission efficiency of intelligent transportation monitoring images, the above image denoising and monitoring video compression processing can adopt edge computing processing methods. Set every 30 minutes as a transmission section. When transmitting the monitoring video of each transmission section, the SINR (Signal to Interference plus Noise Ratio) data of each moment of each uplink channel is obtained through the gateway radio frequency module at a sampling frequency of 30 Hz, and the transmission delay data of each moment of each uplink channel is obtained through the probing packet based on Ping (Packet Internet Groper) at a sampling frequency of 30 Hz. The time series of the SINR data of each uplink channel is recorded as the SINR data sequence of each uplink channel, and the time series of the transmission delay data of each uplink channel is recorded as the transmission delay data sequence of each uplink. Among them, the gateway radio frequency module and the probing packet based on Ping are both well-known contents, and the specific process will not be elaborated. It should be noted that for the acquisition frequency of SINR data and transmission delay data, the implementer can set it according to the actual situation, and this application does not make specific restrictions.

[0024] Measure the signal power of each uplink channel during the video transmission of each transmission section through a spectrum analyzer. Among them, the signal threshold is set to -60 dBm, and the signal with a power greater than the signal threshold in each uplink channel is used as an effectively occupied signal. For each uplink channel, the percentage of the duration of the effectively occupied signal before the current moment to the total time before the current moment is used as the channel occupancy rate of the uplink channel at the current moment; the sequence composed of the channel occupancy rates of all moments of each uplink channel is recorded as the channel occupancy rate data sequence of each uplink channel. Among them, measuring the signal power of the channel by a spectrum analyzer is a well-known technology, and the specific process will not be elaborated.

[0025] Step S2: Divide each video frame in the image into image blocks; calculate the vehicle congestion degree of each image block of each video frame based on the vehicle displacement change situation between the corresponding image blocks in each video frame and the previous video frame, combine the vehicle speed in the image block, calculate the content key coefficient of each image block, and determine the transmission priority of each image block in the same video frame.

[0026] During the 5G uplink transmission of intelligent transportation monitoring images, when vehicle congestion or vehicle traffic accidents occur at a traffic intersection, the higher the priority of the 5G transmission task of the video monitoring device corresponding to the traffic intersection, the more the 5G uplink transmission of the key content of the traffic monitoring video should be guaranteed first, effectively improving the quality of the intelligent transportation monitoring video, assisting relevant personnel or departments to obtain the traffic conditions in a timely manner, and then quickly responding.

[0027] Specifically, when the vehicle speed in the video frame exceeds the speed limit more severely, the possibility of traffic congestion and traffic accidents is higher, and the priority of the video frame data should be increased, and 5G uplink transmission should be prioritized. Further, when the position deviation of the vehicle aggregation area between the monitoring images corresponding to adjacent video frames is small, it indicates that the vehicle aggregation situation at the traffic intersection has not been significantly improved, and the traffic congestion phenomenon has worsened.

[0028] In the traffic monitoring video images to be transmitted uplink via 5G, the H.265 (HEVC) image coding method is adopted, and each video frame image is divided into 32 image blocks of 32×32. To analyze the key degree of the content of each image block in each video frame in the traffic monitoring video to be transmitted uplink via 5G, the content key coefficient of each video frame is constructed, specifically: First, for the traffic monitoring video corresponding to each transmission section, by converting the video screen coordinate system to the road surface coordinate system, using the length and spacing of the lane lines for parameter calibration, extracting the edge pixel points of static objects, and adopting the Gaussian mixture model and the Canny edge detection algorithm to identify moving targets, determining the positions of the moving targets in adjacent frames, and obtaining the speeds of each vehicle in each video frame image through the displacement deviation and time information. Among them, the process of obtaining the speeds of each vehicle in each video frame image is a well-known prior art, and the specific process will not be elaborated here.

[0029] Further, to obtain the position deviation situation between the vehicle aggregation areas of adjacent video frames, each image block in each video frame image is grayscale processed to obtain the grayscale image of each image block, and the grayscale values of all pixel points in the grayscale image are used as the input of the K-mediods clustering algorithm for clustering, obtaining each clustering cluster and the clustering center of the clustering cluster in the image block; through the image recognition technology based on YOLO (You Only Look Once), the license plate numbers of each vehicle in each clustering cluster in each image block are obtained; for any clustering cluster in the a-th image block of the current video frame and any clustering cluster in the a-th image block of the previous video frame, compare whether the license plate numbers of the vehicles in these two clustering clusters are the same, and determine the number of identical vehicles between these two clustering clusters; When the number of identical vehicles between the two clustering clusters is greater than or equal to the preset quantity threshold, it is determined that the vehicle aggregation areas in these two clustering clusters belong to the same vehicle aggregation area, and these two clustering clusters are formed into a same-region clustering cluster group. Preferably, in the embodiment of the present application, the quantity threshold is the average value of the total number of vehicles in these two clustering clusters. As other embodiments of the present application, the implementer can set the quantity threshold according to the actual situation. Among them, the K-mediods clustering algorithm and the image recognition technology based on YOLO are both well-known technologies, and the specific acquisition process will not be elaborated too much.

[0030] Further, calculate the Euclidean distance between the cluster centers of two clusters in the same-region cluster group, denoted as the first distance, and use the sum value of the first distances of all the same-region cluster groups between the a-th image block of the current video frame and the a-th image block of the previous video frame as the vehicle congestion degree of the a-th image block of the current video frame; Calculate the difference between the vehicle speeds of each vehicle in the current video frame and the lane speed limit, denoted as the first difference.

[0031] Finally, based on the above analysis, calculate the content key coefficient of each image block of each video frame in the traffic monitoring image video, and the expression is: In the formula, is the content key coefficient of the a-th image block of the i-th video frame in the traffic monitoring image video; is the sum value of the first differences of the vehicle speeds of all the vehicles in the a-th image block of the i-th video frame in the traffic monitoring image video; is the vehicle congestion degree of the a-th image block of the i-th video frame in the traffic monitoring image video; is the exponential function with the natural constant e as the base; is a preset extremely small positive number. Among them, the role of is to prevent the denominator from being 0. Preferably, in the embodiments of the present application, is set to 0.1. As other embodiments of the present application, the implementer can set the value of according to the actual situation by himself.

[0032] The content key coefficient reflects the key degree of the content in each image block of the traffic monitoring image video frame to be transmitted on the 5G uplink; the vehicle congestion degree reflects the displacement change condition of the vehicle aggregation area in the image block; the more obvious the vehicle congestion or vehicle speeding condition presented in the image block, the more this image block should be used as key content and given priority for 5G uplink transmission, and the calculation index becomes larger; the smaller the displacement change between the same vehicle aggregation areas in adjacent video frames, the calculation index becomes smaller.

[0033] Calculate the content key coefficient of each image block in the above manner, determine the transmission priority of the image blocks of each video frame in descending order, the larger the content key coefficient, the higher the transmission priority of the image block. Obtain the compressed data packets of each image block of each traffic monitoring image video frame in the transmission section, and send the 5G uplink transmission requirements to the 5G base station at the same time.

[0034] Step S3: Based on the sharp feature of each peak value in the fitting curve of the channel occupancy rate data sequence and the degree of data chaos in the transmission delay data sequence, construct the occupancy delay property during the transmission process of each uplink channel; based on the difference between adjacent elements in the SINR data sequence, construct the signal quality interference degree during the transmission process of each uplink channel; based on the occupancy delay property and the signal quality interference degree, construct the slot interference susceptibility during the transmission process of each uplink channel.

[0035] During the 5G transmission process in the TDD communication technology mode of intelligent transportation monitoring video, the base stations used in different traffic monitoring areas may occupy the same frequency band, resulting in different base stations using the same time slots to transmit downlink data and uplink data respectively, causing cross-slot interference problems. This not only affects the transmission quality of intelligent transportation monitoring image videos but also exacerbates the data packet loss rate situation.

[0036] During the transmission process of intelligent transportation monitoring image videos, when the cross-slot interference situation of the monitoring image videos in the 5G uplink channel is more serious, due to the slot interference destroying the signal orthogonality, the downward trend of the SINR of the monitoring image video data packets is more significant; at the same time, the uplink signal and the downlink signal are transmitted through the same time slot, resulting in the overlap of the channel occupancy rate, presenting a periodic peak phenomenon, and the uplink signal cannot be transmitted to the 5G base station in time, and the transmission delay fluctuation of the traffic monitoring image video data packets is more obvious.

[0037] Taking the j-th 5G uplink channel used for the transmission of traffic monitoring image video data in the current transmission section as an example, to analyze the cross-slot interference situation during the 5G uplink transmission of intelligent transportation monitoring images, construct the slot interference susceptibility during the transmission process of each 5G uplink channel, specifically: First, use the channel occupancy rate data sequence of each 5G uplink channel as the input of the least squares fitting algorithm, and the output is the fitting curve of the channel occupancy rate of each channel. Then, use the AMPD (Automatic Multiscale-based Peak Detection) multiscale peak detection algorithm to obtain all the peaks in the channel occupancy rate fitting curve, and calculate the curvature of the channel occupancy rate fitting curve at the peak value of each peak. Among them, the least squares method, the AMPD multiscale peak detection algorithm, and the calculation of the curvature are all well-known technologies, and the specific process will not be elaborated here.

[0038] Further, calculate the time interval between the peaks of each peak and its next peak in the channel occupancy rate fitting curve, denoted as the first time interval, where the time interval between the last peak and the peak of its previous peak is used as the first time interval of the last peak; denote the absolute value of the difference between the mean of the first time intervals of all peaks in the fitting curve and the first time interval of each peak as the peak period fluctuation coefficient of each peak. The larger the peak period fluctuation coefficient, the greater the difference between the time interval between a peak and its next peak compared to the average time interval, and the less obvious the periodic spike condition of the channel occupancy rate.

[0039] Further, calculate the sum value of the curvatures at all peaks on the channel occupancy rate fitting curve of each 5G uplink channel, denoted as the first sum value; calculate the sum value of the peak period fluctuation coefficients of all peaks on the channel occupancy rate fitting curve of each 5G uplink channel, denoted as the second sum value; calculate the Shannon entropy of all data in the transmission delay data sequence of each 5G uplink channel; divide the product of the first sum value and the Shannon entropy by the second sum value, and take the calculation result as the occupancy delay property during the transmission of each 5G uplink channel. The larger the curvature and the smaller the peak period fluctuation coefficient, the more obvious the periodic spike condition of the channel occupancy rate and the greater the occupancy delay property; the larger the Shannon entropy, the more severe the fluctuation of the transmission delay data sequence, indicating the more severe the interference condition and the greater the occupancy delay property.

[0040] Further, calculate the difference between each data and its next data in the SINR data sequence of each 5G uplink channel, denoted as the first difference, and take the sum value of the first differences of all data in the SINR data sequence as the signal quality interference degree during the transmission of this 5G uplink channel.

[0041] Further, calculate the slot interference susceptibility during the transmission of each 5G uplink channel. The expression is: In the formula, is the slot interference susceptibility during the transmission of the traffic monitoring video packet on the j-th 5G uplink channel; is the signal quality interference degree during the transmission of the traffic monitoring video packet on the j-th 5G uplink channel; is the occupancy delay property during the transmission of the traffic monitoring video packet on the j-th 5G uplink channel.

[0042] The slot interference susceptibility reflects the cross-slot interference condition suffered by the traffic monitoring video packet during the transmission on the 5G uplink channel; the signal quality interference degree reflects the SINR downward trend of the monitoring video packet during the transmission on the 5G uplink channel. The more significant the SINR downward trend, the more severe the cross-slot interference condition. The occupancy time delay characterizes the periodic peak condition of the occupancy rate of the 5G uplink channel and the transmission delay fluctuation phenomenon of the traffic monitoring video data packets. The greater the occupancy time delay, the more serious the cross-slot interference suffered by the traffic monitoring video data packets during transmission in the 5G uplink channel.

[0043] Step S4: Adjust the upload order of the video frame content in the intelligent traffic monitoring image based on the transmission priority and the slot disturbance.

[0044] Set a slot disturbance evaluation threshold Q. Preferably, in the embodiment of the present application, the value of Q is set to 0.6. As other embodiments of the present application, the implementer can set the value of Q according to the actual situation. When the slot disturbance of the 5G uplink channel where the traffic monitoring video data packet is transmitted is less than the slot disturbance evaluation threshold Q, it is determined that during the transmission of the traffic monitoring video data packet, due to the cross-slot interference between the uplink signals and downlink signals of different base stations, the video quality degradation, delay jitter, and packet loss rate conditions are slight, and the 5G transmission quality of the traffic monitoring video data packet with a higher priority is good, and no adjustment is required.

[0045] On the contrary, when the slot disturbance of the 5G uplink channel where the traffic monitoring video data packet is transmitted is greater than or equal to the slot disturbance evaluation threshold Q, it is determined that during the transmission of the traffic monitoring video data packet, the cross-slot interference between different base stations causes the degradation of the monitoring video transmission quality, delay jitter, and packet loss rate conditions to intensify, and it is impossible to transmit the traffic monitoring video data with high quality through 5G. It is necessary to adjust the data packet transmission order in time to ensure the transmission quality of the key content of the traffic monitoring image. The specific adjustment method is as follows: In step S2, the data packets corresponding to each image block in the traffic monitoring video frame of the intelligent traffic monitoring have been sorted according to the transmission priority. Further, all 5G uplink channels are divided according to the priority of the slot disturbance. The smaller the slot disturbance, the higher the priority of the 5G uplink channel to be selected; the traffic monitoring video frame data packets with the above divided priorities are transmitted to the 5G base station through the 5G uplink channels with decreasing priority in turn, assisting relevant personnel or departments to timely understand the traffic conditions information, and then quickly respond to decisions, laying a foundation for large-scale monitoring of intelligent transportation.

[0046] The schematic diagram of the acquisition process of the occupancy time delay is as Figure 2 shown.

[0047] Based on the same inventive concept as the above method, an embodiment of the present application further provides a smart traffic monitoring video transmission system based on 5G technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned smart traffic monitoring video transmission methods based on 5G technology are implemented.

[0048] In summary, the embodiment of the present application provides a smart traffic monitoring video transmission method based on 5G technology. Through the synchronous analysis of the traffic conditions and traffic congestion conditions of the smart traffic monitoring video frames, the content key coefficients of each image block are calculated, and the transmission priority of each image block in the same video frame is determined, realizing the criticality division of the content of the smart traffic monitoring video data to be transmitted on the 5G uplink, effectively improving the flexibility and accuracy of the monitoring video transmission of smart traffic; during the 5G uplink channel transmission process, the signal interference and channel occupancy rate overlap situation suffered by the traffic monitoring video data packets due to the cross-slot interference between different base stations are used to construct the slot interference susceptibility during the transmission process of each uplink channel, and further provide a slot priority allocation method during the 5G transmission of the smart traffic monitoring video. Based on the transmission priority and the slot interference susceptibility, the upload order of the video frame content in the smart traffic monitoring video is adjusted, effectively reducing the risk of exacerbation of delay jitter and packet loss rate during the 5G transmission of the traffic monitoring video, and ensuring the 5G transmission quality of the smart traffic monitoring video.

[0049] It should be noted that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of the present application have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0051] The above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A smart traffic monitoring image transmission method based on 5G technology, characterized in that: The method comprises the following steps: Acquire smart traffic monitoring images, and within the transmission section, collect SINR data, transmission delay data, and channel occupancy data of each uplink channel at each time during uplink transmission of the image, and construct SINR data sequences, transmission delay data sequences, and channel occupancy data sequences; Divide each video frame in the image into image blocks; calculate the vehicle congestion degree of each image block in each video frame based on the vehicle displacement change between each video frame and the corresponding image block in the previous video frame, calculate the content key coefficient of each image block in combination with the vehicle speed in the image block, and determine the transmission priority of each image block in the same video frame; Based on the sharp characteristics of each peak value in the fitting curve of the channel occupancy data sequence and the degree of data confusion in the transmission delay data sequence, the occupancy delay of each uplink channel during transmission is constructed; based on the difference between adjacent elements in the SINR data sequence, the signal quality interference degree of each uplink channel during transmission is constructed; based on the occupancy delay and the signal quality interference degree, the time slot interference of each uplink channel during transmission is constructed; The upload order of the video frame content in the intelligent traffic monitoring image is adjusted based on the transmission priority and the time slot interference.

2. The method for transmitting intelligent traffic monitoring images based on 5G technology as claimed in claim 1, characterized in that: The process of obtaining the vehicle congestion degree of each image block in each video frame is as follows: Cluster the grayscale values ​​of all pixels in the grayscale image of each image block to obtain the clusters and cluster centers of each cluster in each image block; obtain the number of common vehicles between any cluster in the a-th image block of the current video frame and any cluster in the a-th image block of the previous video frame through image recognition technology; If the number of identical vehicles is greater than or equal to a preset number threshold, the corresponding two clusters are grouped into a same-region cluster group; The metric distance between the cluster centers of two clusters in the same-region cluster group is calculated and recorded as the first distance. The sum of the first distances of all the same-region cluster groups between the a-th image block of the current video frame and the a-th image block of its previous video frame is taken as the vehicle congestion degree of the a-th image block of the current video frame.

3. The method for transmitting intelligent traffic monitoring images based on 5G technology as claimed in claim 1, characterized in that: The process of obtaining the content key coefficients of each image block is as follows: The difference between the speed of each vehicle in the current video frame and the lane speed limit is calculated and recorded as the first difference; the expression of the content key coefficient is: In the formula, is the content key coefficient of the ath image block in the ith video frame in the traffic monitoring video; is the sum of the first differences of the speeds of all vehicles in the a-th image block of the ith video frame in the traffic monitoring image video; is the vehicle congestion degree of the ath image block in the ith video frame in the traffic monitoring video; is an exponential function with the natural constant e as base; A preset very small positive number.

4. The method for transmitting intelligent traffic monitoring images based on 5G technology as claimed in claim 1, characterized in that: The process of obtaining the transmission priority of each image block in the same video frame is as follows: The transmission priority of all image blocks of each video frame is determined in descending order. The larger the content critical coefficient is, the higher the image block transmission priority is.

5. The method for transmitting intelligent traffic monitoring images based on 5G technology as claimed in claim 1, characterized in that: The process of acquiring the occupancy delay of each uplink channel during transmission is as follows: For the fitting curve of the channel occupancy data sequence of each uplink channel, calculate the curvature at each peak value of the fitting curve; Obtaining the peak period fluctuation coefficient of each peak based on the time interval between adjacent peaks; Calculate the sum of the curvatures at all the peaks on the fitting curve, and record it as the first sum; Calculate the sum of the peak period fluctuation coefficients of all the peaks on the fitting curve, and record it as the second sum; Calculate the Shannon entropy of all data in the transmission delay data sequence of each uplink channel; The product of the first sum value of each uplink channel and the Shannon entropy is divided by the second sum value, and the calculated result is used as the occupation delay of each uplink channel during the transmission process.

6. The method for transmitting intelligent traffic monitoring images based on 5G technology as claimed in claim 5, characterized in that: The process of obtaining the peak period fluctuation coefficient is as follows: The time interval between each peak in the fitting curve and the peak of the next peak is calculated and recorded as the first time interval; the absolute value of the difference between the mean of the first time intervals of all peaks and the first time interval of each peak is recorded as the peak period fluctuation coefficient of each peak.

7. The method for transmitting intelligent traffic monitoring images based on 5G technology as claimed in claim 1, characterized in that: The process of obtaining the signal quality interference degree during the transmission of each uplink channel is as follows: The difference between each data and the next data in the SINR data sequence of each uplink channel is calculated and recorded as a first difference, and the sum of the first differences of all data in the SINR data sequence is used as the signal quality interference degree in the transmission process of each uplink channel.

8. The method for transmitting intelligent traffic monitoring images based on 5G technology as claimed in claim 1, characterized in that: The time slot interference during the transmission of each uplink channel is: the product of the occupation delay of each uplink channel and the signal quality interference degree.

9. The method for transmitting intelligent traffic monitoring images based on 5G technology as claimed in claim 1, characterized in that: The adjustment of the upload order of video frame content in the smart traffic monitoring image is specifically as follows: When the time slot interference of the uplink channel where the smart traffic monitoring image video data packet is transmitted is greater than or equal to the preset time slot interference assessment threshold Q, all uplink channels are divided into priorities according to the time slot interference. The smaller the time slot interference, the higher the priority of the uplink channel to be selected; the data packets of each image block of the traffic monitoring image video frame are transmitted to the 5G base station according to the priority through the uplink channels with the highest priority to the lowest.

10. A smart traffic monitoring image transmission system based on 5G technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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