Highway Fog Visibility Monitoring and Fog Release Notification Management System

Abstract

This invention discloses a highway fog visibility monitoring and fog clearance notification control system, belonging to the field of highway fog monitoring technology. The invention includes a highway fog prediction module, a highway fog monitoring generation module, a highway fog monitoring module, a highway fog clearance control module, and a database. By predicting the fog level of a corresponding fog day on the highway, it extracts the parameter set of the corresponding fog day video monitoring, analyzes the factors affecting the jumps in the corresponding fog day video monitoring, and obtains the set of adjustment viewpoint directions for the corresponding fog day video monitoring, thereby supplementing the corresponding highway monitoring facilities to improve facility monitoring. It also analyzes the visibility of the corresponding fog day on the highway, and implements the corresponding fog day clearance control to ensure highway traffic safety and reduce the traffic risks caused by poor visibility in foggy weather.

Description

Technical Field

[0001] This invention relates to the field of highway fog monitoring technology, specifically to a highway fog video visibility monitoring and fog clearance notification control system. Background Technology

[0002] Highways are closely related to people's travel, and fog on highways is a very common phenomenon in life. When fog occurs on highways, it is necessary to collect clear and effective video footage of the foggy conditions. Based on the visibility of the collected video footage, the correct traffic control measures can be implemented on the highway to ensure safe passage during foggy weather.

[0003] Existing technology, such as the invention patent application CN112419272A, discloses a method and system for rapid estimation of visibility on a highway in foggy weather. The method includes: performing edge detection on a foggy highway scene captured by a camera based on the standard spacing between highway lanes and white lines; fitting a mapping relationship between depth of field and pixels in the foggy highway scene using a scene geometric model; determining the actual distance between the farthest point recognizable by the camera and the camera based on the mapping relationship; obtaining the transmittance of objects in the scene to the camera based on dark channel prior theory; optimizing the transmittance to obtain an optimized transmittance; determining an attenuation coefficient for the foggy highway scene based on the optimized transmittance and the actual distance; and determining the visibility value of the foggy highway based on the attenuation coefficient. This invention can accurately and quickly estimate the visibility of a highway in foggy weather.

[0004] The above-mentioned solutions have the following technical problems: The invention primarily relies on attenuation coefficients to determine visibility on highways in foggy weather. It does not consider collecting highway information during a preset time period to predict the fog level. Consequently, it cannot provide a specific and clear understanding of the current fog conditions on the highway, hindering in-depth analysis of the fog situation and preventing the extraction of parameter sets for highway video surveillance. Furthermore, it fails to obtain traffic data during the preset time period, cannot analyze the factors influencing changes in highway video surveillance during foggy weather, and cannot prevent additional situations from occurring, thus failing to ensure the effectiveness of video surveillance. It also fails to analyze the adjustment points for highway video surveillance based on the analyzed factors and generate the necessary deployment requirements. This leads to potential oversights in video surveillance, compromising monitoring quality and hindering the correct and efficient management of highway traffic based on visibility data, ultimately reducing the safety and efficiency of highway traffic in foggy weather. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a video visibility monitoring and fog clearance notification management system for highways.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a highway fog video visibility monitoring and fog release notification control system, including: a highway fog prediction module, used to collect highway data under the pre-full time limit of the highway, and then predict the fog level of the corresponding fog day of the highway.

[0007] The highway fog monitoring generation module is used to compare the predicted fog level of the highway with the various fog levels that have appeared on the highway in the database, extract the parameter set of the highway fog day video monitoring, obtain the traffic flow of the highway in the corresponding preset time period, and analyze the factors affecting the jump of the highway fog day video monitoring.

[0008] The highway fog monitoring module is used to analyze the abrupt changes in influencing factors, and then to analyze the set of adjustment parameter points for video surveillance in foggy weather on highways, and generate the required deployment of video surveillance in foggy weather on highways.

[0009] The highway fog clearance control module is used to perform corresponding fog video monitoring on the highway after the required deployment of video monitoring for foggy weather is completed. It acquires monitoring data, analyzes the visibility of the highway in foggy weather, and then analyzes the clearance control measures for the highway in foggy weather.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention provides a highway fog visibility monitoring and fog release notification control system. By predicting the fog level of the corresponding fog on the highway, the system extracts the parameter set of the corresponding fog video monitoring, analyzes the jump influencing factors of the corresponding fog video monitoring, and obtains the set of adjustment viewpoints for the corresponding fog video monitoring, thereby supplementing the monitoring facilities of the highway and improving the monitoring facilities. The system analyzes the visibility of the corresponding fog on the highway and executes the release control of the corresponding fog on the highway. It promptly understands the visibility situation of the corresponding fog on the highway so as to take intelligent and efficient release control measures, further ensuring the safety and efficiency of highway traffic, reducing the traffic risks caused by poor visibility in fog, and ensuring the personal safety of highway drivers. The system realizes highway fog visibility monitoring and fog release notification control.

[0011] By collecting highway data under the pre-full time limit, the fog level of the corresponding fog day can be predicted, thereby enabling a quick understanding of the fog level that will occur on the highway in the current fog day, providing a clear and effective analytical direction for subsequent fog monitoring and traffic control.

[0012] By analyzing the parameter set of video surveillance on highways in foggy weather and obtaining traffic data for the corresponding preset time periods, the analysis reveals the factors affecting the fluctuations of video surveillance on highways in foggy weather. This enables effective monitoring of video surveillance on highways, clarifies the monitoring reference points corresponding to the video surveillance, and ensures the acquisition of complete and high-quality monitoring video. At the same time, it helps to understand the fluctuations that foggy video may encounter in the monitoring room, allowing for timely remediation of these effects and preventing them from affecting the video surveillance footage.

[0013] Based on the analysis of the factors affecting the jump, the set of adjustment parameters for video surveillance on highways in foggy weather is obtained, and the required deployment of video surveillance on highways in foggy weather is generated. This enables efficient and timely adjustment of video surveillance, thereby ensuring the effectiveness of video surveillance, timely identification and correction of omissions, and reducing the risk of incomplete video surveillance.

[0014] 5. By analyzing the visibility of highways in foggy weather, and by analyzing the corresponding traffic control measures for highways in foggy weather, we can understand the visibility situation of highways in foggy weather in a timely manner, so as to take intelligent and efficient traffic control measures to effectively ensure the safety and efficiency of highway traffic. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0016] Figure 1 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown, the highway fog video visibility monitoring and fog release notification control system includes a highway fog prediction module, a highway fog monitoring generation module, a highway fog monitoring module, a highway fog release control module, and a database.

[0019] The highway fog prediction module is connected to the highway fog monitoring and generation module and the database, respectively. The highway fog monitoring and generation module is connected to the highway fog monitoring module and the database, respectively. The highway fog monitoring module is connected to the highway fog release and control module and the database, respectively. The highway fog release and control module is connected to the database.

[0020] The highway fog prediction module is used to collect highway data under the pre-full time limit and then predict the degree of fog on the corresponding fog day.

[0021] As an optional implementation, the prediction of the fog level of the highway on a foggy day is carried out in the following specific process: using various detection devices and setting a pre-full collection time limit, the highway data under the pre-full collection time limit is collected, including dew point temperature, relative humidity and wind speed.

[0022] First, the dew point temperature of the highway under the full pre-delay time limit is compared with the reference temperature range stored in the database. If the dew point temperature of the highway under the full pre-delay time limit exceeds the reference temperature range stored in the database, it is determined that there is a temperature inversion layer on the highway; otherwise, the highway is in a normal temperature layer.

[0023] Simultaneously, the relative humidity and wind speed under the full-time limit of the highway are compared with the reference relative humidity and reference wind speed stored in the database. If there is an inversion layer on the highway, and the relative humidity and wind speed under the full-time limit of the highway are both greater than the reference relative humidity and reference wind speed stored in the database, then the fog level of the corresponding fog day on the highway is determined to be strong fog. If there is an inversion layer on the highway, and the relative humidity and wind speed under the full-time limit of the highway are both less than or equal to the reference relative humidity and reference wind speed stored in the database, then the fog level of the corresponding fog day on the highway is determined to be light fog. In this way, the fog level of the corresponding fog day on the highway can be predicted.

[0024] By collecting highway data under the pre-full time limit, the fog level of the corresponding fog day can be predicted, thereby enabling a quick understanding of the fog level that will occur on the highway in the current fog day, providing a clear and effective analytical direction for subsequent fog monitoring and traffic control.

[0025] It should be noted that the various detection devices include a portable dew point meter, an intelligent temperature and humidity dew point recorder, and a wind speed and direction sensor; the portable dew point meter is used to collect the dew point temperature of the highway during a preset period; the intelligent temperature and humidity dew point recorder is used to collect the relative humidity of the highway during a preset period; and the wind speed and direction sensor is used to collect the wind speed of the highway during a preset period.

[0026] It should be noted that the pre-collection time limit refers to the number of days or hours that need to be met. This pre-collection time limit is set by professional managers. By setting the pre-collection time limit, the collected data can be more comprehensive and complete, thus laying an effective data benchmark for the prediction results and ensuring the accuracy of fog prediction for highways. The reference temperature range, reference relative humidity, and reference wind speed are set by professional managers. These are reference values ​​used to better predict the degree of fog that will appear on the corresponding day on the highway.

[0027] The highway fog monitoring generation module is used to compare the predicted fog level of the highway with the various fog levels that have appeared on the highway in the database, extract the parameter set of the highway fog day video monitoring, obtain the traffic flow of the highway in the corresponding preset time period, and analyze the factors affecting the jump of the highway fog day video monitoring.

[0028] As an optional implementation, the extraction of the parameter set for highway video surveillance in foggy weather is carried out as follows: When the fog level of the highway corresponding to the foggy weather is strong fog, the data value set of the highway data for strong foggy weather is compared with the highway data value range corresponding to each strong foggy weather stored in the database. If the data value set of the highway data for strong foggy weather is within the highway data value range corresponding to a certain strong foggy weather stored in the database, then the reference objects monitored when the highway surveillance video was taken on that strong foggy day are taken as the parameter set of the highway corresponding to the strong foggy day.

[0029] When the fog level on the highway is light fog, the data value set of the highway data for light fog days is compared with the data value range of highways for each light fog day stored in the database. If the data value set of the highway data for light fog days falls within the data value range of highways for a certain light fog day stored in the database, the highway monitoring video taken on that light fog day is obtained, and the clarity of each reference point is extracted from the video. The clarity of each reference point is then arranged in descending order, and a certain number of reference points are arbitrarily selected to form a reference point set. This is used to extract the parameter set of the highway video monitoring for fog days.

[0030] It should be noted that the highway data set refers to the specific values ​​of dew point temperature, relative humidity, and wind speed in highway data under conditions of strong fog or light fog. Based on the comparison of specific data values, more effective reference points for highway video surveillance can be obtained.

[0031] It should be noted that the data value range set by professional managers is a reference value used to compare with the fog conditions corresponding to the current highway, thereby selecting more intuitive and effective parameters for the monitoring video of the current highway.

[0032] As an optional implementation method, the analysis obtains the factors affecting the jump in video surveillance of highways in foggy weather. The specific analysis process is as follows: Based on the traffic flow of the highway during a preset time period, the traffic flow of the highway during the preset time period is averaged to obtain the average traffic flow of the highway. At the same time, based on the light brightness value generated by the vehicle's starting lights, the light brightness value of the average traffic flow of the highway is obtained. The light brightness value of the average traffic flow of the highway is compared with the reference light brightness value corresponding to the set highway foggy weather video surveillance video. If the light brightness value of the average traffic flow of the highway is greater than or equal to the reference light brightness value, it is determined that the parameter point of the highway in foggy weather will be affected by light dispersion. The factors affecting the jump in video surveillance of highways in foggy weather are thus analyzed.

[0033] By analyzing the parameter set of video surveillance on highways in foggy weather and obtaining traffic data for the corresponding preset time periods, the analysis reveals the factors affecting the fluctuations of video surveillance on highways in foggy weather. This enables effective monitoring of video surveillance on highways, clarifies the monitoring reference points corresponding to the video surveillance, and ensures the acquisition of complete and high-quality monitoring video. At the same time, it helps to understand the fluctuations that foggy video may encounter in the monitoring room, allowing for timely remediation of these effects and preventing them from affecting the video surveillance footage.

[0034] It should be noted that the reference light brightness value is set by professional managers. The reference light brightness value is a reference value used to more intuitively determine whether the allowable light brightness value of the parameter points is exceeded when collecting monitoring videos on highways in foggy weather. The light brightness value refers to the brightness of the vehicle lights.

[0035] The highway fog monitoring module is used to analyze the abrupt changes in influencing factors, and then to analyze the set of adjustment parameter points for video surveillance in foggy weather on highways, and generate the required deployment of video surveillance in foggy weather on highways.

[0036] As an optional implementation, the analysis obtains the set of adjustment parameter points for video surveillance of highways in foggy weather. The specific analysis process is as follows: Based on the light illumination value, a jump influencing factor of video surveillance of highways in foggy weather, the monitoring image corresponding to the light illumination value of the parameter point set is simulated using virtual component technology. The divergence of each parameter point in the parameter point set is extracted from the monitoring image. The divergence of each parameter point of video surveillance of highways in foggy weather is compared with a set reference divergence range. If the divergence of a certain parameter point of video surveillance of highways in foggy weather exceeds the set reference divergence range, the parameter point is recorded as a divergence point. Each divergence point is obtained by comparison.

[0037] The divergence value of each divergence point in the monitoring image is obtained, and bidirectional changes of angle increment or decrement are performed based on the shooting angle of the previous monitoring device. At the same time, the real-time monitoring image under the monitoring angle change is obtained. When a divergence point is focused in the real-time monitoring image, and the parameter points of the same monitoring device shared by the divergence point have not changed in divergence, the change angle at this time is taken as the monitoring adjustment angle of the divergence point. The adjustment angle of each divergence point is obtained by adjusting it in this way, and the set of adjustment parameter point directions is obtained by combining them. If there is a divergence point that does not change after the bidirectional angle change of increment or decrement, the divergence point is recorded as the target divergence point.

[0038] It should be noted that "focused" means that after the monitoring angle is changed, the corresponding divergence point of the highway is presented in the image as a whole, without any further blurring of the divergence point.

[0039] It should be noted that a reference divergence range is set by professional managers. The reference divergence range is used to judge whether the divergence of each parameter point meets the reference value of the allowable divergence of the monitoring point.

[0040] As an optional implementation, the specific generation process for generating the required deployment of video surveillance for highways in foggy weather is as follows: obtain the distance difference between each target divergence point and the nearest monitoring device, divide the distance difference into segments with average intervals, use the final segment interval of the segment corresponding to the distance difference as the upper limit of the clarity of each target divergence point monitoring in the video surveillance for highways in foggy weather, and so on to obtain the clarity of each segment under the average distance difference, and select the segment with the highest clarity as the device deployment location of each target divergence point.

[0041] It should be noted that using the final segment distance in the segmented data corresponding to the distance difference as the upper limit of the clarity for monitoring each target divergence point in the foggy video of the highway refers to dividing the distance difference into average segment distances and using the terminal segment distance corresponding to the segment distance as the farthest receiving distance of each target divergence point in the video. Based on the point clarity of each target divergence point in the foggy video of the highway at the farthest receiving distance, the upper limit of the clarity for monitoring each target divergence point in the foggy video of the highway is obtained.

[0042] Once the equipment deployment locations for each target scattering point are obtained, based on the current price of the monitoring equipment, additional monitoring equipment at the same price point is acquired. The image clarity value of each target scattering point monitored by the current monitoring equipment in the video is then compared to the image clarity value of each monitoring device for the same target scattering point. If a monitoring device has a higher image clarity value than the current monitoring equipment, it is selected as the new deployment device for each target scattering point. This process generates the required deployment of video surveillance for highways in foggy weather. It should be noted that image clarity value refers to the clarity of each target scattering point under the video surveillance of each monitoring device on the highway section.

[0043] It should be noted that the image clarity value refers to the clarity of each target divergence point under the video monitoring of each monitoring device when each monitoring device performs video monitoring of each target divergence point on a highway section.

[0044] Based on the analysis of the factors affecting the jump, the set of adjustment parameters for video surveillance on highways in foggy weather is obtained, and the required deployment of video surveillance on highways in foggy weather is generated. This enables efficient and timely adjustment of video surveillance, thereby ensuring the effectiveness of video surveillance, timely identification and correction of omissions, and reducing the risk of incomplete video surveillance.

[0045] It should be noted that professional managers are responsible for segmenting the distances between the different sections, thereby enabling more effective video surveillance of highways in foggy weather; the system's search function is used to obtain various monitoring devices at the same price point, and the image clarity value is obtained from the device's parameter settings.

[0046] The highway fog clearance control module is used to perform corresponding fog video monitoring on the highway after the required deployment of video monitoring for foggy weather is completed. It acquires monitoring data, analyzes the visibility of the highway in foggy weather, and then analyzes the clearance control measures for the highway in foggy weather.

[0047] As an optional implementation, the monitoring data includes mileage and color saturation.

[0048] As an optional implementation method, the specific analysis process for obtaining visibility on highways in foggy weather is as follows: If If the visibility on the highway is determined to be severely imperceptible due to fog, and the current fog is a widespread phenomenon, then highway traffic control measures must be implemented under the severe fog visibility conditions. Here, u' represents the preset reference mileage, and u' represents the set reference color saturation. For logical symbols and, denoted as mileage of the highway corresponding to foggy weather video surveillance, and u represents the color saturation of the highway corresponding to foggy weather video surveillance.

[0049] like If the visibility of the highway in the foggy weather is determined to be slightly imperceptible, and the current foggy weather is determined to be sparse and diffuse, then highway traffic control measures should be implemented under the condition of slightly imperceptible visibility in the foggy weather.

[0050] like If the visibility on the highway is determined to be normal, and the current fog is determined to be a slight diffuse phenomenon that does not affect the visibility or field of vision for passage, then the highway will continue to operate as normal.

[0051] It should be noted that the reference mileage is set by professional managers, and u' is the set reference color saturation, which is used to determine the visibility of highways in foggy weather.

[0052] As an optional implementation method, the analysis yields traffic control measures for highways in foggy weather. The specific analysis process is as follows: When the visibility is severely impaired in foggy weather: By sending a corresponding prohibition instruction to the toll station entrance, the highway entrance is closed. In conjunction with various navigation software and news release software, the current severe fog conditions on the highway are pushed to users in real time, and instructions to temporarily close the highway are issued. At the same time, real-time images of the highway at relevant locations are released to drivers who are already on the highway. By specifying a viewpoint in the image, drivers are instructed to drive slowly according to the viewpoint, thereby guiding them to the nearest service area.

[0053] When visibility is slightly imperceptible in foggy weather: By real-time statistics of traffic flow at the entrance and closed-circuit testing of temporarily closing the passage of large trucks, based on the length of fog and the real-time clarity of the video, interval passage is adopted. Through preset time, vehicles travel in a unified manner according to preset time and number, and the vehicles departing each time are monitored in real time to ensure that the distance between vehicles is at the reference distance under preset conditions.

[0054] It should be noted that the reference spacing is set by professional managers in order to better ensure the safety of vehicles driving in foggy weather.

[0055] By analyzing the visibility of highways in foggy weather, and then analyzing the corresponding traffic control measures, we can promptly understand the visibility situation on highways in foggy weather, so as to take intelligent and efficient traffic control measures to effectively ensure the safety and efficiency of highway traffic.

[0056] The database is used to store highway data, reference temperature range, reference relative humidity, reference wind speed, highway data value range, traffic flow, average traffic flow, light illumination value, divergence, clarity, and monitoring data.

[0057] This invention, in its embodiments, predicts the fog level on highways during foggy weather, extracts a set of parameter points for highway video surveillance during foggy weather, analyzes the factors influencing the changes in highway video surveillance during foggy weather, and obtains a set of adjustable viewing directions for highway video surveillance during foggy weather. This allows for the supplementation of highway monitoring facilities, thereby improving facility monitoring. Furthermore, it analyzes the visibility on highways during foggy weather, enabling the implementation of highway traffic control during foggy weather. This allows for timely understanding of visibility conditions on highways during foggy weather, facilitating intelligent and efficient traffic control, further ensuring highway traffic safety and efficiency, reducing the risks associated with poor visibility in foggy weather, and protecting the personal safety of highway drivers. This achieves highway foggy weather video visibility monitoring and foggy weather traffic control.

[0058] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A highway fog video visibility monitoring and fog release notification management and control system, characterized in that, The application relates to a highway mist prediction module for collecting highway data under a highway pre-full time limit to predict the mist degree of a highway corresponding to a foggy day. The highway mist monitoring generation module compares the mist degree of the highway corresponding to the foggy day with the mist degrees of the highway in the database, extracts a reference point set of the highway video monitoring corresponding to the foggy day, and obtains the traffic flow of the highway under a preset time period to analyze the jump influencing factor of the highway video monitoring corresponding to the foggy day. The analysis of the jump influencing factor of the highway video monitoring corresponding to the foggy day is as follows: Based on the traffic flow of the highway under the preset time period, the average traffic flow of the highway is obtained by averaging the traffic flow of the highway under the preset time period. The light value of the average traffic flow of the highway is obtained according to the light value generated by the starting light of the vehicle. The light value of the average traffic flow of the highway is compared with the reference light value corresponding to the shooting monitoring video of the highway in the foggy day. If the light value of the average traffic flow of the highway is greater than or equal to the reference light value, it is determined that the reference point of the highway corresponding to the foggy day is affected by the light dispersion. The jump influencing factor of the highway video monitoring corresponding to the foggy day is analyzed. The highway mist monitoring module analyzes the adjustment reference point direction set of the highway video monitoring corresponding to the foggy day according to the jump influencing factor, and generates the demand layout of the highway video monitoring corresponding to the foggy day. The highway mist release control module executes the corresponding foggy day video monitoring of the highway after the demand layout of the highway video monitoring corresponding to the foggy day is supplemented, obtains the monitoring data, analyzes the visibility of the highway corresponding to the foggy day, and analyzes the release control measures of the highway corresponding to the foggy day. The prediction of the mist degree of the highway corresponding to the foggy day is as follows:

2. The highway foggy day video visibility monitoring and foggy day release notification management and control system of claim 1, wherein, The detection equipment is used to collect the highway data under the highway pre-full time limit, including the dew point temperature, the relative humidity and the wind speed. First, the dew point temperature under the highway pre-full time limit is compared with the reference temperature interval stored in the database. If the dew point temperature under the highway pre-full time limit exceeds the reference temperature interval stored in the database, it is determined that the highway has an inversion layer. Otherwise, the highway is in a normal temperature layer. ​ Meanwhile, the relative humidity and the wind speed of the expressway under the pre-full time limit are compared with the reference relative humidity and the reference wind speed stored in the database. When the expressway has an inversion layer, and the relative humidity and the wind speed of the expressway under the pre-full time limit are both greater than the reference relative humidity and the reference wind speed stored in the database, it is determined that the fog degree of the foggy day corresponding to the expressway is heavy fog. When the expressway has an inversion layer, and the relative humidity and the wind speed of the expressway under the pre-full time limit are both less than or equal to the reference relative humidity and the reference wind speed stored in the database, it is determined that the fog degree of the foggy day corresponding to the expressway is light fog. Thus, the fog degree of the foggy day corresponding to the expressway is predicted.

3. The highway foggy day video visibility monitoring and foggy day release notification management and control system of claim 2, wherein, The reference point set of the video monitoring of the foggy day corresponding to the expressway is extracted, and the specific extraction process is as follows: When the fog degree of the foggy day corresponding to the expressway is heavy fog, the data value set of the expressway data corresponding to the heavy fog day is compared with the expressway data value interval corresponding to each heavy fog day stored in the database. When the data value set of the expressway data corresponding to the heavy fog day is within the expressway data value interval corresponding to a heavy fog day stored in the database, the reference objects monitored when the expressway monitoring video corresponding to the heavy fog day is shot are taken as the reference point set of the heavy fog day corresponding to the expressway. When the fog degree of the foggy day corresponding to the expressway is light fog, the data value set of the expressway data corresponding to the light fog day is compared with the expressway data value interval corresponding to each light fog day stored in the database. If the data value set of the expressway data corresponding to the light fog day is within the expressway data value interval corresponding to a light fog day stored in the database, the clarity corresponding to each reference point is obtained from the expressway monitoring video corresponding to the light fog day, and the clarity corresponding to each reference point is arranged in descending order, so as to randomly select a certain number of reference points to form the reference point set. Thus, the reference point set of the video monitoring of the foggy day corresponding to the expressway is extracted.

4. The highway foggy day video visibility monitoring and foggy day release notification management and control system of claim 1, wherein, The adjusted reference point direction set of the video monitoring of the foggy day corresponding to the expressway is analyzed, and the specific analysis process is as follows: Based on the jump influence factor light value of the video monitoring of the foggy day corresponding to the expressway, the monitoring image of the light value corresponding to the reference point set is simulated by using the virtual component technology, and the divergence of each reference point in the reference point set is extracted from the monitoring image. The divergence of each reference point of the video monitoring of the foggy day corresponding to the expressway is compared with the reference divergence interval. If the divergence of a reference point of the video monitoring of the foggy day corresponding to the expressway exceeds the reference divergence interval, the reference point is recorded as a divergence point. The divergence values of each divergence point in the monitoring image are obtained, and the angle is increased or decreased based on the shooting angle of the front monitoring device, and the real-time monitoring image under the monitoring angle change is obtained, when a divergence point is in a focused state in the real-time monitoring image, and the reference point of the same monitoring device shared by the divergence point does not change, the changed angle at this time is taken as the monitoring adjustment angle of the divergence point, and the adjustment angles of the divergence points are obtained by adjustment, and the adjusted reference point direction set is obtained, if a divergence point does not change after the angle is increased or decreased, the divergence point is recorded as a target divergence point.

5. The highway foggy day video visibility monitoring and foggy day release notification management and control system of claim 4, wherein, The demand layout of the foggy day video monitoring corresponding to the expressway is generated, and the specific generation process is as follows: The difference distance between each target divergence point and the nearest monitoring device is obtained, and the difference distance is divided into segments with an average segment distance, and the terminal segment distance in the corresponding segment of the difference distance is taken as the upper limit of the clarity of the monitoring of each target divergence point of the foggy day video of the expressway, and the clarity of each segment under the equal division of the difference distance is obtained in the same way, and the segment point position with the highest clarity is selected as the device layout position of each target divergence point; After obtaining the device layout position of each target divergence point, the monitoring devices of the same price are obtained based on the price of the current monitoring device, and the imaging clarity value of each target divergence point in the monitoring video is obtained by the current monitoring device, and the imaging clarity value of each target divergence point in the video is obtained by analogy, if there is a monitoring device with higher imaging clarity value than the current monitoring device, the monitoring device with the highest imaging clarity value is selected as the newly added layout device of each target divergence point, and the demand layout of the foggy day video monitoring corresponding to the expressway is generated.

6. The highway foggy day video visibility monitoring and foggy day release notification management and control system of claim 5, wherein, The monitoring data includes mileage and color saturation.

7. The highway foggy day video visibility monitoring and foggy day release notification management and control system of claim 6, wherein, The analysis process of the visibility of the foggy day corresponding to the expressway is as follows: If , it is determined that the visibility of the highway corresponding to the foggy day is seriously invisible, it is determined that the current foggy day is in a large-area diffusion phenomenon, and highway traffic control under the serious visibility of the highway corresponding to the foggy day needs to be performed, is a preset reference mileage, and u' is a set reference color saturation, is a logical symbol and, is a mileage of the highway corresponding to the foggy day video monitoring, and u is a color saturation of the highway corresponding to the foggy day video monitoring. If , it is determined that the visibility of the expressway corresponding to the foggy day is slightly invisible, it is determined that the current foggy day is in the phenomenon of sparse diffusion, and the expressway passing control under the visibility of the expressway corresponding to the foggy day is slightly visible needs to be carried out. If , it is determined that the visibility of the expressway corresponds to a foggy day, and the current foggy day is determined to be a micro-diffusion phenomenon, which does not affect the visibility or field of view of the traffic, and the expressway continues to operate the vehicle in accordance with the normal traffic.

8. The highway foggy day video visibility monitoring and foggy day release notification management and control system of claim 7, wherein, The analysis process of the release control measures of the foggy day corresponding to the expressway is as follows: When the visibility of the foggy day is serious: send the corresponding prohibition of passing instruction to the entrance of the toll station, close the entrance of the expressway, and jointly use the navigation software and news release software to push the instruction that the expressway is in a serious foggy state and is temporarily closed to traffic to the user, and real-time release of the relevant position of the expressway image to the driver who has entered the expressway, and guide the driver to drive slowly according to the specified point in the image, so as to enter the service area nearby; When the visibility of the foggy day is light: real-time statistics of the traffic flow at the entrance, and temporary closure of the closed circuit test of large trucks, based on the diffusion length of the fog and the real-time clarity of the video, and then interval passing, the vehicle is unified according to the preset time and quantity, and the distance between the vehicles is monitored in real time to ensure that the distance between the vehicles is in the preset reference distance.

9. The highway foggy day video visibility monitoring and foggy day release notification management and control system, as claimed in claim 1, wherein, Also included is a database for storing highway data, reference temperature intervals, reference relative humidity, reference wind speed, highway data value intervals, traffic flow, average vehicle flow, light value, divergence, definition, and monitoring data.

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