Method for detecting agglomerate fog on expressway

Abstract

The invention belongs to the technical field of traffic meteorology, and provides a highway agglomerate fog detection method, which comprises the following steps: step 1, detecting layout including a first electrode, a second electrode, an environment conductivity sensor and a background server; 2, electrode signal time and conductivity information of the first electrode and the second electrode and conductivity information of the environment conductivity sensor are collected, and data are uploaded to a background server; step 3, acquiring a continuous change conductivity data curve through conductivity information acquired by the first electrode, the second electrode and the environment conductivity sensor; 4, judging and extracting agglomerate fog inducement conductivity change data through the conductivity change duration, and performing agglomerate fog prediction; and 5, uploading the agglomerate fog prediction information to the fog disaster risk zoning model, and starting agglomerate fog early warning. Accuracy and timeliness of agglomerate fog prediction are improved, timely and effective information is provided for related departments and drivers, and traffic accidents are reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of traffic meteorology and relates to a method for detecting fog on a highway. Background Art

[0002] The goal of modern highway transportation system is "high speed, high efficiency, safety and comfort", but with the increase of transportation volume and the frequent occurrence of extreme weather events, the safe operation of highways is seriously affected. Heavy fog, precipitation, strong winds, sandstorms, extreme temperatures, lightning, snow accumulation and road icing, as well as geological disasters such as mudslides and landslides induced by heavy rain are the main meteorological factors affecting safe driving.

[0003] Due to the large volume of traffic on expressways day and night, a large amount of hydrocarbons, fine carbon smoke and a large amount of dust raised by cars contained in automobile exhaust can all serve as condensation nuclei. Water vapor, cooling and condensation nuclei are the basic elements for the formation of large-scale dense fog, so expressways are conducive to the formation of dense fog. Among the weather phenomena that cause serious traffic accidents on most expressways in my country, heavy fog is the main cause of traffic accidents on expressways, especially dense fog and "cluster fog" that cause ultra-low visibility of less than 50 m, which are more harmful to traffic safety and smoothness.

[0004] This is because foggy weather can easily cause visibility obstruction, especially "fog clusters", which are most likely to cause serious traffic accidents. In foggy weather, water vapor is prone to condensation on the inside of the car window, affecting vision, especially under suitable temperature and humidity conditions, a thin layer of frost will form on the road surface, accompanied by the mixture of fog droplets and accumulated oil and mud on the road, which seriously reduces the adhesion coefficient between the tire and the road surface, resulting in extended braking distance, driving slippage, braking deviation and other phenomena.

[0005] Fog clusters are characterized by suddenness, volatility, locality, and hysteresis. Their formation and development are also seasonal and closely related to the special topography along the highway. Under the same weather background, areas with more mountains, hills, rivers, and lakes are prone to form repetitive and jumping fog clusters. The influence of the local microclimate environment, or the increase of tiny particles of pollutants in the air near the highway (such as autumn straw burning, industrial dust pollution, automobile exhaust emissions, etc.), are conducive to the formation of fog clusters. In the local area of ​​tens to hundreds of meters in the fog cluster, the fog is thicker and the visibility is lower. Usually, the visibility outside the fog cluster is good, but the inside is hazy, and it is particularly difficult to forecast.

[0006] At present, my country's forecast of large-scale advection fog is quite accurate, but the forecast accuracy of small-scale fog with sudden drop in visibility, which is extremely harmful to road safety, is still low. The main problem is that the density of traffic meteorological monitoring stations set up in my country is low, and it is impossible to accurately obtain information on local sudden fog. The best way to achieve accurate density of facilities on highways is to rely on highway traffic facilities. Therefore, a method of accurately predicting fog based on highway traffic facilities and transmitting fog information to national traffic meteorological monitoring stations is an important measure to achieve high-density meteorological monitoring. Summary of the invention

[0007] The present invention provides a method for detecting fog clusters on highways, aiming to solve the problem that the prior art cannot accurately predict and warn the occurrence of fog clusters.

[0008] The technical solution of the present invention is achieved in this way: A method for detecting fog on a highway, comprising: Step 1: Detection layout, including a first electrode, a second electrode, an environmental conductivity sensor and a background server.

[0009] Furthermore, step one includes, S11: First electrode arrangement: installed on a guardrail column on one side of a highway, the first electrode and the second electrode are electrically connected.

[0010] S12: Arrangement of the second electrode: installed on the guardrail post on the other side of the highway, each of the first electrodes is paired with n second electrodes, denoted as {M1, M2, ...Mn}, and each of the second electrodes is placed at a different height, angle and position on the guardrail post on the other side, to achieve spatial layout of the conductivity signals of the first electrode and the second electrode.

[0011] Furthermore, the relationship between the installation height h of the first electrode and the layout of the second electrode: the installation height h of the first electrode is crucial to the spatial coverage of the conductivity signal.

[0012] The relationship between the excitation angle θ of the first electrode, the excitation coverage diameter D, and the distance l from the first electrode to the second electrode is: tanθ=D / l.

[0013] The relationship between the inclination angle α from the excitation range of the first electrode to the ground, the installation height h of the first electrode, and the distance l from the first electrode to the second electrode is: tanα=h / l.

[0014] S13: Arrangement of environmental conductivity sensors: Install one or more environmental conductivity sensors, which should be arranged in the area to be monitored so as to accurately reflect changes in environmental conductivity.

[0015] Step 2: Collect the electrode signal time and conductivity information of the first electrode, the electrode signal time and conductivity information of the second electrode, and the conductivity information of the environmental conductivity sensor, and upload the data to the background server.

[0016] Furthermore, step 2 includes, S21: Record the electrode signal time and conductivity information of each of the first electrodes, and upload the data to the backend server.

[0017] S22: Record the electrode signal time and conductivity information of each of the second electrodes, and upload the data to the backend server.

[0018] S23: The environmental conductivity sensor collects environmental conductivity information in real time and uploads the data to a backend server.

[0019] Step three: obtaining a continuously changing conductivity data curve through the conductivity information obtained by the first electrode, the second electrode and the environmental conductivity sensor.

[0020] Furthermore, step three includes S31: For each group of the first electrodes and the corresponding n second electrodes, the conductivity data set at the same time is recorded as I={I1,I2,...,I n}, I i ∈I represents the conductivity value of the i-th second electrode received at a given moment.

[0021] S32: Calculate the maximum conductivity value of n second electrodes corresponding to each first electrode , for each second electrode i (i=1,2,…,n), calculate the conductivity difference ΔIi=Imax−Ii, and define the threshold difference ΔKi: let ΔKi=ΔIi; where, Imax represents the maximum conductivity calculated according to the first electrode and the corresponding second electrode layout, Ii represents the measured actual conductivity of the i-th second electrode, ΔIi represents the conductivity difference of the i-th second electrode, and ΔKi represents the threshold difference of the i-th second electrode, which will be an element in the threshold difference set.

[0022] S33: For the dynamic threshold of the i-th second electrode, Γi = K - ΔKi = K - ΔIi. Set the dynamic threshold set Γ: Γ = {Γi | i = 1, 2, …, n} = {K - ΔKi | i = 1, 2, …, n}, where K is the dynamic original threshold.

[0023] Further, step S33 includes S331: Continuously monitor the change of the ambient conductivity data and record the current ambient conductivity value L_current.

[0024] S332: Calculate the average value of the ambient conductivity. Average the collected ambient conductivity values L_current to obtain the average value L_avg of the ambient conductivity.

[0025] S333: Initialize an initial threshold K_init and a threshold ratio k_init. According to the current ambient conductivity value and the threshold ratio, calculate the dynamic original threshold K = K_init + k_init * (L_current - L_avg), where L_avg is the average value of the ambient conductivity.

[0026] S34: Denote the dataset of conductivity with respect to time as {Io(i, j) | i = 1, 2,......n; j = 1, 2,......n}, where Io(i, j) represents the conductivity value of the i-th second electrode at the j-th moment.

[0027] S35: For each second electrode, fit the conductivity data to form a conductivity curve fi(t), where i represents the conductivity value received by the i-th second electrode.

[0028] Step Four: Judge and extract the conductivity change data of the fog cluster inducement through the duration of the conductivity change, and conduct fog cluster prediction.

[0029] Further, step Four includes S41: Obtain the threshold T0 for the duration of the conductivity change. The duration of the conductivity change Ti is expressed as satisfying dfi(t) / dt ≠ 0 in the continuous time period from t0 to t1, denoted as the duration of the conductivity change Ti = t1 - t0. When the duration of the conductivity change Ti ≤ T0, and there is no continuous conductivity value of the second electrode less than the average value of the ambient conductivity, it is considered as signal abnormal data, and the background server does not process the data.

[0030] S42: When the duration of the conductivity change Ti > T0, and at the same time, there are continuous M second electrodes whose conductivity values are less than the average value of the ambient conductivity, where M is close to the actual number of second electrodes arranged; it is denoted as rain and snow weather data.

[0031] S43: When the conductivity change duration Ti>T0, there are at least m second electrodes adjacent to the i-th second electrode and the conductivity value of the second electrode is less than the average value of the ambient conductivity, wherein m>3, m is much smaller than the number of second electrodes actually deployed; it is recorded as the conductivity change data of the fog cluster inducing, according to the fog cluster prediction formula P =a * (dfi(t) / dt) * T, wherein P is the possibility of fog cluster generation, a is a constant (which can be adjusted according to actual conditions), (dI / dt) is the rate of change of conductivity, indicating the degree of change of conductivity per unit time, and T is the duration of conductivity change.

[0032] Step 5: Upload the cluster fog forecast information to the fog disaster risk zoning model, and activate the cluster fog warning.

[0033] Furthermore, step five includes analyzing the cluster fog prediction data to infer the concentration and distribution range of fog, combining meteorological data with the fog disaster risk zoning model , predict the specific location and development trend of fog clusters; where H is the hazard index of disaster-causing factors , when i=1,2,3, Ai represents the annual average number of heavy fog days, annual average number of dense fog days and annual average number of heavy fog days at a toll station on the expressway; Vs is the spatial vulnerability index of the carrier , when i=1,2,3, Bi represents whether a toll station on the expressway is located in a foggy section, the road network density at the location, and the daily average one-way maximum traffic volume; V is the vulnerability index of the carrier , when i=1,2,3, Ci represents the annual average number of road closures caused by fog at a toll station on the expressway, the annual cumulative road closure duration, and the annual average number of traffic accidents.

[0034] Once confirmed as fog, the system will immediately issue an early warning, notifying relevant departments and drivers to take corresponding measures to ensure road safety.

[0035] The beneficial effects of the present invention are: 1. By using existing traffic facilities to carefully design the electrode layout, comprehensively monitoring the conductivity changes on the highway, and predicting the location of fog clusters in real time, the formation of fog clusters can be discovered in time, thereby warning drivers and relevant departments in advance, reducing traffic accidents caused by fog clusters, and ensuring road safety. At the same time, the paired arrangement of the first electrode and the second electrode and the settings of different heights and angles can provide more precise spatial coverage, thereby improving the accuracy and reliability of fog cluster detection.

[0036] 2. Use duration and conductivity values ​​to determine the possibility of fog. Unlike simple threshold judgment, this method takes into account the temporal continuity and spatial consistency of changes, significantly improving the scientificity and accuracy of the prediction. The ability to distinguish between rain, snow and fog avoids false warnings, reduces interference to drivers, and improves the credibility of the system.

[0037] 3. Integrating fog forecast information into the fog disaster risk zoning model can provide scientific decision-making support for decision makers. Timely warnings can save lives, reduce property losses, and maintain traffic order. The real-time and accuracy of this method enable relevant departments to effectively allocate resources, optimize response measures, and improve the overall efficiency of responding to sudden weather events.

[0038] 4. Real-time collection of conductivity information allows the system to quickly respond to environmental changes and update data in a timely manner. This real-time monitoring capability is crucial for dynamically changing weather conditions, helping to capture key signals that may cause fog, improve the highway management department's ability to respond to fog disasters, and develop more reasonable emergency plans. Timely fog warnings can reduce highway closures due to low visibility and reduce economic losses caused by traffic delays.

[0039] 5. This method does not involve the use of harmful chemicals and is an environmentally friendly monitoring method that helps protect the ecological environment along the highway. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0041] Figure 1 The overall flow chart of a method for detecting fog on a highway is shown in FIG. Figure 2 It is a schematic diagram of the overall functions of a method for detecting fog on a highway; Figure 3 A schematic diagram of electrode layout for a method for detecting fog on a highway; Figure 4 A schematic diagram of a method for detecting fog clusters on a highway and the effect of detecting fog clusters; Figure 5 A schematic diagram of a method for detecting fog on a highway to detect rain and snow effects; Description of the numbers in the figure: The first electrode; 2. The second electrode; 3. The environmental conductivity sensor; 4. The backend server; 201. The second electrode numbered 201; 202. The second electrode numbered 202; 203. The second electrode numbered 203; 204. The second electrode numbered 204; 205. The second electrode numbered 205; 206. The second electrode numbered 206; 207. The second electrode numbered 207; 208. The second electrode numbered 208; 209. The second electrode numbered 209; 210. The second electrode numbered 210; 211. The second electrode numbered 211; 212. The second electrode numbered 212; DETAILED DESCRIPTION

[0042] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Combination Figure 1 , Figure 2 As shown, a method for detecting fog on a highway includes: Step 1: Detection layout, including a first electrode 1, a second electrode 2, an environmental conductivity sensor 3 and a background server 4.

[0044] Furthermore, step one includes, S11: Arrangement of the first electrode 1: Installed on a guardrail column on one side of the highway, the first electrode 1 and the second electrode 2 are electrically connected.

[0045] S12: Arrangement of the second electrode 2: The second electrode 2 is installed on the guardrail post on the other side of the highway. Each first electrode 1 is paired with n second electrodes 2, denoted as {M1, M2, ...Mn}. Each second electrode 2 is placed at a different height, angle and position on the guardrail post on the other side to achieve a spatial layout of the conductivity signals of the first electrode 1 and the second electrode.

[0046] Relationship between the installation height h of the first electrode 1 and the layout of the second electrode 2: The installation height h of the first electrode 1 is crucial for three-dimensional coverage.

[0047] The relationship between the excitation angle θ of the first electrode 1 , the excitation coverage diameter D, and the distance l from the first electrode 1 to the second electrode 2 is: tanθ=D / l.

[0048] The relationship between the inclination angle α between the excitation range of the first electrode 1 and the ground, the installation height h of the first electrode 1 and the distance l from the first electrode 1 to the second electrode 2 is: tanα=h / l.

[0049] S13: Arrangement of environmental conductivity sensor 3: Install one or more environmental conductivity sensors 3. These environmental conductivity sensors 3 should be arranged in the area to be monitored so as to accurately reflect the change in environmental conductivity.

[0050] Step 2: Collect the electrode signal time and conductivity information of the first electrode 1 , the electrode signal time and conductivity information of the second electrode 2 , and the conductivity information of the environmental conductivity sensor 3 , and upload the data to the background server 4 .

[0051] Furthermore, step 2 includes, S21: Record the electrode signal time and conductivity information of each first electrode 1 , and upload the data to the backend server 4 .

[0052] S22: Record the electrode signal time and conductivity information of each second electrode 2 and upload the data to the backend server 4.

[0053] S23 : the environmental conductivity sensor 3 collects the environmental conductivity information in real time and uploads the data to the backend server 4 .

[0054] Step three, obtaining a continuously changing conductivity data curve through the conductivity information obtained by the first electrode 1, the second electrode 2 and the environmental conductivity sensor 3.

[0055] Furthermore, step three includes, S31: For each group of first electrodes 1 and corresponding n second electrodes 2, the data set of conductivity at the same time is recorded as I={I1,I2,...,I n}, I i ∈I represents the conductivity value of the i-th second electrode received at a given moment.

[0056] S32: Calculate the maximum conductivity value received by n second electrodes 2 corresponding to each first electrode 1 , for each second electrode 2i (i=1,2,…,n), calculate the conductivity difference ΔIi=Imax−Ii, and define the threshold difference ΔKi: let ΔKi=ΔIi; where, Imax represents the maximum conductivity calculated according to the layout of the first electrode 1 and the corresponding second electrode 2, Ii represents the actual conductivity received by the i-th second electrode 2 measured, ΔIi represents the conductivity difference of the i-th second electrode 2, and ΔKi represents the threshold difference of the i-th second electrode 2, which will be an element in the threshold difference set.

[0057] Further, such as Figure 3 As shown, a schematic diagram of the layout effect of two first electrodes 1 and twelve second electrodes 2 is provided, and each first electrode 1 corresponds to six second electrodes 2 .

[0058] S33: For the dynamic threshold of the i-th second electrode 2, Γi = K - ΔKi = K - ΔIi. Set the dynamic threshold set Γ: Γ = {Γi | i = 1, 2, …, n} = {K - ΔKi | i = 1, 2, …, n}, where K is the dynamic original threshold.

[0059] Further, step S33 includes S331: Continuously monitor the change of the ambient conductivity data and record the current ambient conductivity value L_current.

[0060] S332: Calculate the average value of the ambient conductivity. Average the collected ambient conductivity values L_current to obtain the average value L_avg of the ambient conductivity.

[0061] The average value L_avg of the ambient conductivity can be achieved by, but not limited to, calculating the average value of the ambient conductivity data over a period of time.

[0062] S333: Initialize an initial threshold K_init and a threshold ratio k_init. According to the current ambient conductivity value and the threshold ratio, calculate the dynamic original threshold K = K_init + k_init * (L_current - L_avg), where L_avg is the average value of the ambient conductivity.

[0063] For the first electrode 1, a dynamic original threshold K relative to the average value of the ambient conductivity can be set. An embodiment provided in this application is to set the dynamic original threshold K to a certain multiple k of the average value of the ambient conductivity to ensure that the light intensity of the first electrode 1 is sufficiently higher than the background noise of the ambient conductivity, so that the signal change can be more easily detected at the receiving end. According to the change of the ambient conductivity, the threshold ratio k is dynamically adjusted. The initial threshold K_init and the threshold ratio k_init can be set according to actual needs, or can be obtained through experiments or experience.

[0064] S34: Denote the dataset of the conductivity with respect to time as {Io(i, j) | i = 1, 2,......n; j = 1, 2,......n}, where Io(i, j) represents the conductivity value received by the i-th second electrode 2 at the j-th moment.

[0065] S35: For each second electrode, fit the conductivity data to form a conductivity curve fi(t), where i represents the conductivity value received by the i-th second electrode 2.

[0066] Step Four: Judge and extract the light intensity change data of the fog cluster inducement through the duration of the conductivity change, and perform fog cluster prediction.

[0067] Furthermore, step four includes, S41: Obtain the conductivity change duration threshold value T0, the conductivity change duration Ti is expressed as satisfying dfi(t) / dt≠0 in the continuous time period t0~t1, recorded as conductivity change duration Ti=t1-t0, when the conductivity change duration Ti≤T0, and there is no continuous conductivity value of the second electrode 2 that is less than the average value of the environmental conductivity, it is considered to be signal abnormal data, and the background server 4 does not process the data.

[0068] S42: When the conductivity change duration Ti>T0, it is satisfied that there are M=12 consecutive second electrodes 2 whose conductivity values ​​are less than the average value of the environmental conductivity, wherein the value of M provided in this embodiment is equal to 12, the number of the actually arranged second electrodes 2; Figure 5 As shown, a schematic diagram of the detection effect of rain and snow is provided.

[0069] S43: When the conductivity change duration Ti>T0, there are at least m=5 second electrodes 2 adjacent to the i-th second electrode 2 and the conductivity value of the second electrode 2 is less than the average value of the ambient conductivity, wherein m=5, and m is much smaller than the number of second electrodes 12 actually deployed; according to the fog cluster prediction formula P =a * (dfi(t) / dt) * T, wherein P is the possibility of fog cluster generation, a is a constant (which can be adjusted according to actual conditions), (dI / dt) is the rate of change of conductivity, indicating the degree of change of conductivity per unit time, and T is the duration of conductivity change.

[0072] Step 5: Upload the cluster fog forecast information to the fog disaster risk zoning model, and activate the cluster fog warning.

[0073] Furthermore, step five includes inferring the concentration and distribution range of fog by analyzing the cluster fog prediction data, combining the meteorological data of the Meteorological Bureau and the fog disaster risk zoning model , predict the specific location and development trend of fog clusters; where H is the hazard index of disaster-causing factors , when i=1,2,3, Ai represents the annual average number of heavy fog days, annual average number of dense fog days and annual average number of heavy fog days at a toll station on the expressway; Vs is the spatial vulnerability index of the carrier , when i=1,2,3, Bi represents whether a toll station on the expressway is located in a foggy section, the road network density at the location, and the daily average one-way maximum traffic volume; V is the vulnerability index of the carrier , when i=1,2,3, Ci represents the annual average number of road closures caused by fog at a toll station on the expressway, the annual cumulative road closure duration, and the annual average number of traffic accidents.

[0074] Once confirmed as fog, the system will immediately issue an early warning, notifying relevant departments and drivers to take corresponding measures to ensure road safety.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made according to design requirements and other factors. Although the present invention has been described in detail with reference to the aforementioned embodiments, those of ordinary skill in the art should understand that it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

[0076] It should be understood that the size of the sequence number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. It is worth noting that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this disclosure can be achieved, and this document does not limit it here.

[0077] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

Claims

1. A method for detecting fog on a highway, characterized in that: Including Step 1, detecting the layout, including the first electrode, the second electrode, the environmental conductivity sensor and the background server; Step 2, collecting the electrode signal time and conductivity information of the first electrode, the electrode signal time and conductivity information of the second electrode, and the conductivity information of the environmental conductivity sensor, and uploading the data to the background server; Step 3, obtaining a continuously varying conductivity data curve based on the conductivity information obtained by the first electrode, the second electrode and the environmental conductivity sensor; Step 4, judging and extracting the conductivity change data of the cause of group fog by the duration of conductivity change for group fog prediction; Step 5, uploading the group fog prediction information to the fog disaster risk zoning model to start the group fog warning.

2. A method for detecting highway fog according to claim 1, characterized in that: Step 1 includes S11: Arrangement of the first electrode: Installed on the guardrail column on one side of the highway, and the first electrode and the second electrode are electrically connected; S12: Arrangement of the second electrode: Installed on the guardrail column on the other side of the highway. Each first electrode is paired with n second electrodes, denoted as {M1, M2,......Mn}. Each second electrode is placed at different heights, angles and positions on the guardrail column on the other side to realize the spatial layout of the conductivity signals of the first electrode and the second electrode; S13: Arrangement of the environmental conductivity sensor: Install one or more environmental conductivity sensors, and the environmental conductivity sensors should be arranged in the area to be monitored so as to accurately reflect the conductivity change of the environment.

3. A method for detecting highway fog according to claim 1, characterized in that: Step 2 includes S21: Recording the electrode signal time and conductivity information of each first electrode and uploading the data to the background server; S22: Recording the electrode signal time and conductivity information of each second electrode and uploading the data to the background server; S23: The environmental conductivity sensor collects the conductivity information of the environment in real time and uploads the data to the background server.

4. The method for detecting highway fog according to claim 1, characterized in that: Step 3 includes S31: For each group of the first electrodes and the corresponding n second electrodes, the conductivity data set at the same time is recorded as I={I1,I2,...,I n }, I i ∈I represents the conductivity value of the i-th second electrode received at a given moment; S32: Calculate the maximum conductivity value of n second electrodes corresponding to each first electrode , for each second electrode i (i=1,2,…,n), calculate the conductivity difference ΔIi=Imax−Ii, and define the threshold difference ΔKi: Let ΔKi=ΔIi; where Imax represents the maximum conductivity calculated according to the first electrode and the corresponding second electrode layout, Ii represents the measured actual conductivity of the i-th second electrode, ΔIi represents the conductivity difference of the i-th second electrode, and ΔKi represents the threshold difference of the i-th second electrode, which will be an element in the threshold difference set; S33: For the dynamic threshold of the i-th second electrode, Γi = K - ΔKi = K - ΔIi, set the dynamic threshold set Γ: Γ = {Γi∣i = 1, 2,…, n} = {K - ΔKi∣i = 1, 2,…, n}, where K is the dynamic original threshold; S34: Denote the dataset of conductivity with respect to time as {Io(i, j)丨i = 1, 2,......n; j = 1, 2,......n}, where Io(i, j) represents the conductivity value of the i-th second electrode at the j-th moment; S35: For each second electrode, fit the conductivity data to form a conductivity curve fi(t), where i represents the conductivity value received by the i-th second electrode.

5. The method for detecting highway fog according to claim 1, characterized in that: Step 4 includes S41: Obtaining a conductivity change duration threshold T0, where the conductivity change duration Ti is expressed as satisfying dfi(t) / dt≠0 in a continuous time period of t0 to t1, recorded as conductivity change duration Ti=t1-t0. When the conductivity change duration Ti≤T0, and there is no continuous conductivity value of the second electrode that is less than the average value of the environmental conductivity, it is considered to be signal abnormal data, and the background server does not process the data; S42: When the conductivity change duration Ti>T0, it is satisfied that there are M consecutive second electrodes whose conductivity values ​​are less than the average value of the environmental conductivity, where M is close to the number of second electrodes actually deployed; recorded as rainy and snowy weather data; S43: When the conductivity change duration Ti>T0, there are at least m second electrodes adjacent to the i-th second electrode and the conductivity value of the second electrode is less than the average value of the ambient conductivity, wherein m>3, m is much smaller than the number of second electrodes actually deployed; it is recorded as the conductivity change data of the fog cluster inducing, according to the fog cluster prediction formula P =a * (dfi(t) / dt) * T, wherein P is the possibility of fog cluster generation, a is a constant (which can be adjusted according to actual conditions), (dI / dt) is the rate of change of conductivity, indicating the degree of change of conductivity per unit time, and T is the duration of conductivity change.

6. The method for detecting highway fog according to claim 1, characterized in that: Step five includes, By analyzing the cluster fog prediction data, the concentration and distribution range of fog can be inferred, and combined with meteorological data and fog disaster risk zoning model , predict the specific location and development trend of fog clusters; where H is the hazard index of disaster-causing factors , when i=1,2,3, Ai represents the annual average number of heavy fog days, annual average number of dense fog days and annual average number of heavy fog days at a toll station on the expressway; Vs is the spatial vulnerability index of the carrier , when i=1,2,3, Bi represents whether a toll station on the expressway is located in a foggy section, the road network density at the location, and the daily average one-way maximum traffic volume; V is the vulnerability index of the carrier , when i=1,2,3, Ci represents the annual average number of road closures caused by fog at a toll station on a highway, the annual cumulative road closure duration, and the annual average number of traffic accidents; Once confirmed as fog, the system will immediately issue an early warning, notifying relevant departments and drivers to take corresponding measures to ensure road safety.

7. A method for detecting highway fog according to claim 4, characterized in that: Step S33 includes S331: continuously monitor the change of environmental conductivity data and record the current environmental conductivity value L_current; S332: Calculate the average value of the environmental conductivity, average the collected environmental conductivity values ​​L_current, and obtain the average value L_avg of the environmental conductivity; S333: Initialize an initial threshold value K_init and a threshold ratio k_init, and calculate a dynamic original threshold value K= K_init + k_init * (L_current - L_avg) according to the current environmental conductivity value and the threshold ratio, where L_avg is the average value of the environmental conductivity.

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