A meteorological environment forecast control system for expressways

Abstract

The present invention discloses a meteorological environment forecast control system for a highway, which relates to the technical field of highway meteorological forecast, and comprises a road section advance data import module, a meteorological forecast central processor, a meteorological environment forecast data acquisition and analysis module, a forecast control management cloud platform, and a cloud SQL terminal. The present invention can set a corresponding meteorological environment early warning touch threshold value for each highway section by analyzing the specific characteristics of each highway section, thereby improving the accuracy and timeliness of meteorological early warnings for highways, providing reliable guarantees for the response efficiency of traffic management departments and the accuracy of meteorological control, and realizing refined management at the same time, being able to provide early warning information for each highway section in a targeted manner, thereby facilitating relevant departments to more scientifically and effectively deal with traffic safety risks under adverse weather conditions, and providing strong support and guarantee for the coordinated and stable operation of highways.

Description

Technical Field

[0001] The invention relates to the technical field of highway weather forecasting, in particular to a highway weather environment forecasting control system. Background Art

[0002] Highways are important transportation arteries between cities and regions. Vehicles on highways usually travel at a high speed. Severe weather conditions will reduce visibility on highways or aggravate negative conditions such as slippery road surfaces, which can easily lead to traffic accidents. Traffic accidents on highways have serious consequences, which may not only cause casualties, but also cause traffic interruptions and long-term traffic jams, seriously affecting the traffic efficiency of the entire road network. In view of this, it is necessary to provide a highway meteorological environment forecast control system.

[0003] The prior art, such as the invention patent with announcement number CN117272840B, is a method and system for warning severe weather on highways. The method includes: constructing a hidden danger point data set based on historical meteorological data, determining a number of severe weather elements that affect the passage of the target highway based on the hidden danger point data set, and matching the meteorological element values ​​of the hidden danger points with the several severe weather elements according to a preset spatial interpolation algorithm, training a meteorological risk forecasting model based on the hidden danger point data set, the meteorological element values ​​of the hidden danger points and a preset deep learning network, and conducting a disaster-bearing body analysis and a disaster resistance analysis on the target highway respectively, inputting the first indicator data and the second indicator data into the meteorological risk forecasting model based on the hierarchical analysis method to generate a risk level forecasting model, and predicting the driving risk warning level of the target highway through the risk level forecasting model. The invention can accurately predict the risk level and improve the user's driving experience.

[0004] To sum up, there are still the following limitations in the current meteorological control of highways: the current meteorological control of highways focuses more on the weather itself, but relatively lacks attention to the characteristics of the highway itself. Different highways have different application environments and degrees of aging, and the requirements for weather forecasts and the tolerance for severe weather are different. The lack of consideration in this dimension will make it impossible to take into account the specific characteristics of each highway, resulting in inaccurate and timely warnings for some highways, which may affect the response efficiency of traffic management departments and the accuracy of meteorological control. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a highway weather environment forecast control system, which can effectively solve the problems involved in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a highway meteorological environment forecast control system, including: a section advance data import module, which is used to divide the highway into various highway sections, and collect and count the advance data of each highway section, and synchronously import it into the meteorological forecast central processor.

[0007] The weather forecast central processor is used to respond to and receive the advance data of each highway section, obtain the advance indicator factor of each highway section through convergence fitting processing, and transmit it to the forecast control management cloud platform.

[0008] The meteorological environment forecast data collection and analysis module is used to collect the meteorological environment forecast data of each expressway section, analyze the meteorological environment warning characteristic quantity of each expressway section, and transmit it synchronously to the forecast control management cloud platform.

[0009] The forecast control management cloud platform is used to analyze the meteorological environment warning touch threshold value of each highway section according to the leading indicator factors of each highway section, and verify it with the meteorological environment warning characteristic quantity of each highway section, so as to select the risky highway sections to implement meteorological environment warning prompt control.

[0010] As a further solution, the statistics of advance data of each expressway section specifically include: constructing a surveying and mapping model of each expressway section by scanning with a laser scanner built in an inspection drone.

[0011] According to the surveying and mapping model of each expressway section, the central axis of each expressway section is extracted, and a number of survey points are arranged at equal intervals to obtain each central axis survey point. The elevation values ​​of each central axis survey point of each expressway section are counted and marked as feature sub-element 1.

[0012] According to the preset contour intervals, the surveying and mapping models of each expressway section are laid out with contour lines, and the total area of ​​each expressway section within each contour interval is extracted and marked as feature sub-element 2.

[0013] The total length of the slope section, the average slope of the slope section, the total number of curve sections, the average curvature of the curve, the cumulative damaged area of ​​the road surface, and the total number of intersections of each highway section are statistically obtained from the surveying and mapping model of each highway section, and are combined as characteristic sub-elements 3.

[0014] The average daily traffic volume, average daily traffic density, total number of traffic incidents, and average vehicle speed of each highway section are collected from the highway edge server and jointly marked as feature sub-element 4.

[0015] The characteristic sub-elements 1, 2, 3, and 4 of each expressway section are counted and combined as the advance data of each expressway section.

[0016] As a further solution, the advance indicator factor of each expressway section is obtained by convergence fitting processing. The specific process is: according to the advance data of each expressway section, the road structure characteristic value of each expressway section and the traffic characteristic value of each expressway section are processed in turn, and the advance indicator factor of each expressway section is obtained by convergence fitting processing. The advance indicator factor of each expressway section is obtained by analyzing and processing the advance data of each expressway section, which is used to determine the potential risk level of traffic in each expressway section and serves as a numerical analysis basis for the meteorological environment warning touch threshold value of each expressway section.

[0017] As a further solution, the collection of meteorological environment forecast data for each expressway section specifically includes: deploying each expressway section with a set number to obtain each location point, taking the current time as the starting point for segmentation, and dividing the forecast period by a preset time period length, collecting and counting the meteorological environment forecast data for each expressway section, the meteorological environment forecast data is the meteorological data summarized for the forecast period at each location point, wherein the meteorological data summarized for the forecast period includes the estimated cumulative precipitation, the estimated cumulative duration of precipitation, the estimated average precipitation rate, the estimated temperature change curve, the estimated humidity change curve, the estimated visibility change curve and the estimated wind speed change curve.

[0018] As a further solution, the analysis obtains the meteorological environment warning characteristic quantities of each expressway section. The specific analysis process includes: based on the meteorological environment forecast data of each expressway section, the estimated cumulative precipitation, the estimated cumulative duration of precipitation, and the estimated average precipitation rate at each location are counted, and the first influencing factor of the meteorological environment of each expressway section is obtained through processing.

[0019] The estimated temperature change curve and the estimated humidity change curve of each expressway section at each location point are statistically calculated, and the estimated temperature change curve and the estimated humidity change curve are fitted through a preset curve fitting algorithm to obtain the estimated meteorological characteristic change curve of each expressway section at each location point, and the second influencing factor of the meteorological environment of each expressway section is obtained through processing.

[0020] The estimated visibility change curve and the estimated wind speed change curve at each location point of each expressway section are statistically compiled, and the estimated minimum visibility, the estimated average visibility change rate, the estimated maximum wind speed and the estimated maximum wind speed change rate are extracted from them. The third influencing factor of the meteorological environment of each expressway section is obtained through processing.

[0021] According to the first influencing factor of the meteorological environment, the second influencing factor of the meteorological environment and the third influencing factor of the meteorological environment of each expressway section, the meteorological environment early warning characteristic quantity of each expressway section is obtained.

[0022] As a further solution, the meteorological environment warning characteristic quantity of each expressway section is obtained by analyzing the meteorological environment forecast data of each expressway section, which is used to determine the degree of meteorological warning demand for each expressway section and serve as the data basis for screening risky expressway sections to implement meteorological environment warning prompt control.

[0023] As a further solution, the analysis obtains the meteorological environment warning touch threshold value of each expressway section. The specific process is: based on the leading indicator factors of each expressway section, the leading indicator factor reference value stored in the cloud SQL end and the meteorological environment warning touch preset threshold value are counted.

[0024] The leading indicator factor of each expressway section is verified with the leading indicator factor reference value. If the leading indicator factor of a certain expressway section is lower than or equal to the leading indicator factor reference value, the meteorological environment warning touch threshold value of the expressway section is defined as the meteorological environment warning touch preset threshold value.

[0025] If the advance indicator factor of a certain expressway section is higher than the advance indicator factor reference value, the expressway section is recorded as a forecast adjustment section, the difference between the advance indicator factor of the forecast adjustment section and the advance indicator factor reference value is extracted, marked as the touch threshold adjustment benchmark value, and the impairment factor corresponding to the touch threshold unit adjustment benchmark value stored on the cloud SQL side is counted, and the meteorological environment warning touch threshold adjustment value of the forecast adjustment section is obtained by processing, and the meteorological environment warning touch threshold value of the forecast adjustment section is defined as the meteorological environment warning touch threshold adjustment value.

[0026] The meteorological environment warning touch threshold values ​​of each expressway section are summarized.

[0027] As a further solution, the risky highway sections are screened to execute meteorological environment early warning prompt control. The specific process is: according to the meteorological environment early warning touch threshold value of each highway section, the meteorological environment early warning characteristic quantity of each highway section is counted. Through comparison, if the meteorological environment early warning characteristic quantity of a certain highway section is higher than or equal to the meteorological environment early warning touch threshold value of the highway section, then the highway section is marked as a risky highway section, thereby screening the risky highway sections to execute meteorological environment early warning prompt control, and transmit the meteorological environment early warning prompt information of the risky highway sections to the receiving end of the road control center to which the risky highway sections belong.

[0028] As a further solution, the specific execution conditions and constraints of the preceding indicator factors of each expressway section are as follows:

[0029]

[0030] In the formula, represents the leading indicator factor of expressway section i, σ 1→i represents the road structure characteristic value of highway section i, σ2→i It represents the traffic characteristic value of expressway section i, where i is the number of each expressway section, i=1,2,3,...,p, p is the number of expressway sections, a1 is the weight of the preset road structure characteristic value, a2 is the weight of the preset traffic characteristic value, and e is a natural constant.

[0031] As a further solution, the meteorological environment warning characteristic quantity of each expressway section is subject to the following specific execution conditions:

[0032]

[0033] In the formula, ω i The meteorological environment warning characteristic quantity of the expressway section i, b1 is the weight of the first influencing factor of the preset meteorological environment, b2 is the weight of the second influencing factor of the preset meteorological environment, b3 is the weight of the third influencing factor of the preset meteorological environment, ω 1→i represents the first influencing factor of the meteorological environment of highway section i, ω2→i represents the second influencing factor of the meteorological environment of highway section i, ω3→i represents the third influencing factor of the meteorological environment of highway section i, e is a natural constant, i is the number of each highway section, i=1,2,3,...,p, p is the number of highway sections.

[0034] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0035] (1) The present invention provides a meteorological environment forecast control system for expressways. According to the specific characteristics of each expressway section, the meteorological environment warning touch threshold value of each expressway section is obtained through analysis. This overcomes the drawback caused by the current lack of attention to the characteristics of the expressway itself. By analyzing the specific characteristics of each expressway section, a corresponding meteorological environment warning touch threshold value can be set for each expressway section, thereby improving the accuracy and timeliness of meteorological warnings on expressways and providing reliable guarantees for the response efficiency of traffic management departments and the accuracy of meteorological control.

[0036] (2) The present invention obtains the leading indicator factors of each expressway section through convergent fitting processing, and performs numerical fitting processing on the specific characteristic information of each expressway section, thereby providing a reliable data support basis for subsequent meteorological environment early warning and prompt control, and realizing refined management, and being able to provide targeted early warning information for each expressway section, thereby helping relevant departments to respond to traffic safety risks under severe weather conditions more scientifically and effectively.

[0037] (3) The present invention sets up a meteorological environment forecast data collection and analysis module to carry out targeted meteorological data analysis and processing on each expressway section and a number of location points of each expressway section, and finally obtains the meteorological environment warning characteristic quantity of each expressway section through analysis, thereby reducing the error that may be caused by data processing to a large extent, and can specifically and deeply reflect the meteorological forecast status of each expressway section, which is conducive to more accurate meteorological environment warning prompts, and also provides strong support and guarantee for the coordinated and stable operation of expressways.

[0038] (4) The present invention finally analyzes and obtains the meteorological environment warning touch threshold value of each expressway section, and verifies it with the meteorological environment warning characteristic quantity of each expressway section, thereby improving the accuracy of screening risky expressway sections, and can help to more effectively allocate monitoring equipment, manpower, materials and other resources, and improve the efficiency and response speed of expressways in dealing with severe weather conditions. It not only ensures the pertinence and effectiveness of warning prompt control, but also helps to improve the overall safety of the transportation system.

[0039] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the system module connection.

[0041] Figure 2 It is a schematic diagram of the central axis involved in the embodiment of the present invention.

[0042] Figure numerals: 1: central axis. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in 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.

[0044] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0045] See also Figure 1As shown, an embodiment of the present invention provides a technical solution: a highway meteorological environment forecast control system, including: a road section advance data import module, a meteorological forecast central processor, a meteorological environment forecast data collection and analysis module, a forecast control management cloud platform, and a cloud SQL terminal.

[0046] The section advance data import module is used to divide the expressway into various expressway sections, collect and count the advance data of each expressway section, and import them into the weather forecast central processor synchronously.

[0047] It should be noted that the expressway is divided into various expressway sections. The specific process is: a number of toll nodes on the expressway are counted, and the section between two adjacent toll nodes is recorded as an expressway section, thereby counting the various expressway sections.

[0048] In another embodiment, the expressway sections may be divided according to the length or interval of the section, or the expressway sections may be divided according to the function and purpose of the expressway, such as dividing into main lines, branch lines, etc.

[0049] Specifically, the statistics of the advance data of each expressway section specifically include: constructing a surveying and mapping model of each expressway section by scanning with a laser scanner built in an inspection drone. This embodiment collects characteristic sub-element data of each expressway section through a surveying and mapping model, which can not only more accurately understand the status of each expressway section, and promptly discover problems such as road cracks and damage, thereby helping to carry out targeted maintenance and repairs, but also provides a preliminary basis for the analysis and determination of the advance indicator factors of each expressway section in this embodiment.

[0050] According to the surveying and mapping model of each expressway section, the central axis of each expressway section is extracted, and a number of survey points are arranged at equal intervals to obtain each central axis survey point. The elevation values ​​of each central axis survey point of each expressway section are counted and marked as feature sub-element 1.

[0051] like Figure 2 As shown, the central axis (1) is distributed at the center axis position of the expressway section and is consistent with the extension direction of the expressway section.

[0052] According to the preset contour intervals, the surveying and mapping models of each expressway section are laid out with contour lines, and the total area of ​​each expressway section within each contour interval is extracted and marked as feature sub-element 2.

[0053] In this embodiment, the surveying and mapping models of each highway section are laid out with contour lines based on a division basis of 2m. In a specific embodiment, the division basis of the contour interval can be adaptively adjusted according to the width of the highway to make data processing more efficient and accurate.

[0054] The total length of the slope section, the average slope of the slope section, the total number of curve sections, the average curvature of the curve, the cumulative damaged area of ​​the road surface, and the total number of intersections of each highway section are statistically obtained from the surveying and mapping model of each highway section, and are combined as characteristic sub-elements 3.

[0055] The average daily traffic volume, average daily traffic density, total number of traffic incidents, and average vehicle speed of each highway section are collected from the highway edge server and jointly marked as feature sub-element 4.

[0056] It should be distinguished that the average daily total traffic volume and the average daily traffic density, among which the total traffic volume refers to the total number of vehicles passing through a certain section of road within a certain period of time, usually expressed in the number of vehicles or the number of vehicles per hour. The total traffic volume reflects the number of vehicles passing through a certain section of road per unit time, and is an indicator of traffic flow size. The traffic density refers to the number of vehicles that exist simultaneously in a unit length of a certain section of road, usually expressed in the number of vehicles per kilometer. The traffic density reflects the degree of congestion and density of vehicles on the highway section, and is an indicator of traffic flow density.

[0057] The highway edge server refers to a server device deployed on the side of the highway or near the side of the highway, which is used to process and store data and information related to highway traffic management and intelligent services. These servers are usually located in traffic monitoring centers, service areas, toll stations and other locations near the highway. They are used to receive real-time data from monitoring equipment, meteorological sensors, traffic flow detectors and other sensing devices on the highway, and can perform real-time processing and analysis to obtain real-time traffic status, road conditions information, etc.

[0058] The characteristic sub-elements 1, 2, 3, and 4 of each expressway section are counted and combined as the advance data of each expressway section.

[0059] The weather forecast central processor is used to respond to and receive the advance data of each expressway section, obtain the advance indicator factor of each expressway section through convergence fitting processing, and transmit it to the forecast control management cloud platform.

[0060] The weather forecast central processor refers to a central processing unit used to process and analyze highway meteorological data. Its specific functions include collecting, storing, processing, analyzing and publishing highway meteorological information, and providing real-time weather forecasts and traffic safety tips for highway traffic management departments, drivers and other relevant users.

[0061] In a specific embodiment of the present invention, the leading indicator factors of each expressway section are obtained through convergent fitting processing, and the specific characteristic information of each expressway section is numerically fitted, which provides a reliable data support basis for subsequent meteorological environment early warning prompt control, realizes refined management, and can provide early warning information of each expressway section in a targeted manner, thereby helping relevant departments to deal with traffic safety risks under severe weather conditions more scientifically and effectively.

[0062] Specifically, the advance indicator factors of each expressway section are obtained by convergent fitting processing, and the specific process is: according to the advance data of each expressway section, the road structure characteristic values ​​and traffic characteristic values ​​of each expressway section are obtained in sequence.

[0063] In an embodiment, the structural features of the highway section can be extracted by utilizing digital map data, spatial analysis and geographic information system (GIS) technology. For example, GIS software can be used to analyze the geometric shape, length, curvature, slope and other features of the road. Machine learning algorithms can also be used to learn road structural features from a large amount of highway section data, and supervised learning algorithms can be used to train the model to analyze the number of lanes, lane width, curvature and other features of the highway section, and finally the road structural feature values ​​of each highway section can be determined.

[0064] In an embodiment, a data mining algorithm can be used to analyze the historical traffic data of a highway section and extract the traffic characteristic values ​​of the highway section. Commonly used data mining algorithms include cluster analysis, association rule mining, classification algorithms, etc. Neural network algorithms in deep learning can also be used to analyze the traffic data of the highway section and extract the traffic characteristic values ​​of each highway section.

[0065] According to the road structure characteristic values ​​and traffic characteristic values ​​of each expressway section, the leading indicator factors of each expressway section are obtained through convergence and fitting processing. The leading indicator factors of each expressway section are obtained by analyzing and processing the advance data of each expressway section, and are used to determine the potential risk level of traffic on each expressway section, and serve as a basis for numerical analysis of the meteorological environment warning touch threshold value of each expressway section.

[0066] In an embodiment, spatiotemporal data analysis can be performed on traffic flow, vehicle speed and other data to extract leading indicator factors for each highway section. Traffic behavior on each highway section can also be analyzed using data collected by on-board sensors or roadside sensors, such as vehicle speed, vehicle density, etc., to extract corresponding characteristic values, and combined with statistical model processing and analysis to obtain leading indicator factors for each highway section.

[0067] Furthermore, in this embodiment, the specific execution conditions and constraints of the preceding indicator factors of each expressway section are as follows:

[0068]

[0069] In the formula, represents the leading indicator factor of expressway section i, σ 1→i represents the road structure characteristic value of highway section i, σ 2→i It represents the traffic characteristic value of expressway section i, where i is the number of each expressway section, i=1,2,3,...,p, p is the number of expressway sections, a1 is the weight of the preset road structure characteristic value, a2 is the weight of the preset traffic characteristic value, and e is a natural constant.

[0070] It should be understood that the road structure characteristic values ​​of each expressway section are obtained by analyzing and processing the characteristic sub-elements 1, 2, and 3 of each expressway section, and are used to comprehensively judge the potential risk level of the road structure of each expressway section, and serve as the analysis basis for the leading indicator factors of each expressway section.

[0071] Furthermore, in this embodiment, the road structure characteristic values ​​of each expressway section are specifically processed with the following constraints: σ 1→i represents the road structure characteristic value of highway section i, α i→1 represents the first eigenvalue of the road structure of highway section i, α i→2 represents the second characteristic value of the road structure of the expressway section i, ε1 represents the correction proportional coefficient of the preset first characteristic value of the road structure, and ε2 represents the correction proportional coefficient of the preset second characteristic value of the road structure.

[0072] According to characteristic element 1 and characteristic sub-element 2, the first characteristic value of the road structure of each expressway section is analyzed and obtained. The first characteristic value of the road structure of each expressway section is used to determine the potential risk degree of the structure of each expressway section and provide data support for the analysis of the leading indicator factor of each expressway section. The specific constraints are:

[0073]

[0074] Among them, α i→1 represents the first eigenvalue of the road structure of highway section i, H ic represents the elevation value of the cth center axis survey point of highway section i, S ij It represents the total area of ​​expressway section i in the jth contour interval range, i is the number of each expressway section, i=1,2,3,...,p, p is the number of expressway sections, c is the number of each central axis survey point, c=1,2,3,...,c0, c0 is the number of central axis survey points, j is the number of each contour interval range, j=1,2,3,...,j0, j0 is the number of contour interval ranges.

[0075] According to the characteristic sub-element 3, the second characteristic value of the road structure of each expressway section is processed and obtained. The second characteristic value of the road structure of each expressway section is used to determine the potential risk degree of the road structure of each expressway section and provide data support for the analysis of the leading indicator factor of each expressway section. The specific constraints are:

[0076]

[0077] Among them, α i→2 represents the second eigenvalue of the road structure of highway section i, L i ,θ i , Z i , Q i It is represented by the total length of the slope section, the average slope of the slope section, the total number of curve sections, and the average curvature of the curve section i. ΔL, Δθ, ΔZ, and ΔQ are represented by the total length of the slope section, the average slope of the slope section, the total number of curve sections, and the average curvature of the curve section stored in the cloud SQL end. e is a natural constant, and S i→1 , J i They are the cumulative damaged area of ​​the road surface and the total number of intersections of highway section i, respectively. S0 represents the characteristic evaluation factor per unit area of ​​the cumulative damaged area of ​​the road surface stored on the cloud SQL side. J0 represents the characteristic evaluation factor corresponding to a single intersection stored on the cloud SQL side.

[0078] Furthermore, in this embodiment, according to the characteristic sub-element 4, the traffic characteristic value of each expressway section is processed and obtained, and the traffic characteristic value of each expressway section is used to determine the potential risk degree of traffic operation of each expressway section, and serves as the analysis basis of the leading indicator factor of each expressway section. The specific constraints are:

[0079]

[0080] Among them, σ 2→i represents the traffic characteristic value of highway section i, E i , i , T i 、V i They represent the average daily traffic volume, average daily traffic density, total number of traffic incidents, and average vehicle speed of expressway section i respectively.

[0081] The meteorological environment forecast data collection and analysis module is used to collect the meteorological environment forecast data of each expressway section, and analyze the meteorological environment warning characteristic quantity of each expressway section, and synchronously transmit it to the forecast control management cloud platform.

[0082] In a specific embodiment of the present invention, a meteorological environment forecast data collection and analysis module is set up to carry out targeted meteorological data analysis and processing on each expressway section and a number of location points of each expressway section, and finally the meteorological environment warning characteristic quantities of each expressway section are obtained through analysis, which greatly reduces the errors that may be caused by data processing, and can specifically and deeply reflect the meteorological forecast status of each expressway section, which is conducive to more accurate meteorological environment warning prompts, and also provides strong support and guarantee for the coordinated and stable operation of expressways.

[0083] Specifically, the collection of meteorological environment forecast data for each expressway section includes: deploying each expressway section in a set number to obtain each location point, taking the current time as the starting point for segmentation, and dividing the forecast period by a preset time period length, collecting and counting the meteorological environment forecast data for each expressway section, the meteorological environment forecast data is the meteorological data summarized for the forecast period at each location point, wherein the meteorological data summarized for the forecast period includes the estimated cumulative precipitation, the estimated cumulative duration of precipitation, the estimated average precipitation rate, the estimated temperature change curve, the estimated humidity change curve, the estimated visibility change curve and the estimated wind speed change curve.

[0084] It should be understood that, in the present embodiment, the meteorological environment forecast data for each expressway section is specifically retrieved from the meteorological bureau and meteorological station in the region to which it belongs, and the data is simultaneously retrieved in conjunction with the meteorological monitoring stations and meteorological sensing devices installed on the expressway. In another embodiment, the meteorological forecast data over a wide area provided by meteorological satellites and remote sensing technology can also be retrieved and analyzed.

[0085] The forecast period summary meteorological data refers to the summary meteorological data in the divided forecast period, that is, the estimated cumulative precipitation, estimated cumulative precipitation duration, estimated average precipitation rate, estimated temperature change curve, estimated humidity change curve, estimated visibility change curve and estimated wind speed change curve, all of which are extracted based on the forecast period as the time scale.

[0086] In this embodiment, the forecast period is obtained by dividing the period into 3 hours. In a specific application process, the period length can be adaptively adjusted and is not limited to the 3-hour limit described in this embodiment.

[0087] Specifically, the analysis obtains the meteorological environment warning characteristic quantities of each expressway section. The specific analysis process includes: based on the meteorological environment forecast data of each expressway section, the estimated cumulative precipitation, the estimated cumulative duration of precipitation, and the estimated average precipitation rate at each location point are counted, and the first influencing factor of the meteorological environment of each expressway section is obtained through processing.

[0088] In an embodiment, multivariate statistical analysis can be used to obtain the first influencing factor of the meteorological environment of each highway section. Multivariate statistical analysis is a statistical analysis method that comprehensively utilizes multiple variables. It can simultaneously consider the relationship between multiple meteorological characteristic data in each highway section, usually including principal component analysis and factor analysis, etc. These methods can help find the correlation between the main meteorological characteristics and variables, and finally analyze and obtain the first influencing factor of the meteorological environment of each highway section.

[0089] It should be understood that the first influencing factor of the meteorological environment of each expressway section is obtained by analyzing and processing the estimated cumulative precipitation, estimated cumulative duration of precipitation, and estimated average precipitation rate at each location point on each expressway section. The numerical results are used to analyze the degree of meteorological environmental impact of each expressway section, and are used as the basis for analyzing the meteorological environment warning characteristic quantities of each expressway section.

[0090] In this embodiment, the first influencing factor of the meteorological environment of each expressway section is obtained by the following analysis method:

[0091]

[0092] Among them, ω 1→i represents the first influencing factor of the meteorological environment of highway section i, A ig ,t ig 、w ig represents the estimated cumulative precipitation, estimated cumulative duration of precipitation, and estimated average precipitation rate of highway section i at the gth position point, respectively. represents the overall estimated precipitation amount, the overall estimated precipitation duration, and the overall estimated precipitation rate of highway section i, respectively. ΔA, Δt, and Δw represent the reference estimated cumulative precipitation, reference estimated cumulative duration of precipitation, and reference estimated average precipitation rate stored on the cloud SQL end, respectively. g is the number of each location point, g = 1, 2, 3, ..., g0, and g0 represents the number of location points.

[0093] The estimated temperature change curve and the estimated humidity change curve of each expressway section at each location point are statistically calculated, and the estimated temperature change curve and the estimated humidity change curve are fitted through a preset curve fitting algorithm to obtain the estimated meteorological characteristic change curve of each expressway section at each location point, and the second influencing factor of the meteorological environment of each expressway section is obtained through processing.

[0094] The curve fitting algorithm includes but is not limited to the least squares method, a polynomial fitting algorithm, a spline interpolation algorithm and a Gaussian process regression algorithm.

[0095] In an embodiment, cluster analysis can be performed on the temperature and humidity data in the estimated temperature change curve and the estimated humidity change curve of each highway section at each location point to mine different meteorological characteristic data, and the data can be divided into different groups by combining methods such as K-means clustering or hierarchical clustering to identify different meteorological characteristics. Finally, the second influencing factor of the meteorological environment of each highway section can be analyzed in combination with cloud computing technology.

[0096] It should be understood that, in the present embodiment, the second influencing factor of the meteorological environment of each expressway section is obtained by analyzing and processing the estimated temperature change curve and the estimated humidity change curve of each expressway section at each location point, thereby obtaining a numerical result for judging the degree of meteorological environmental impact of each expressway section, and is used as a basis for analyzing the meteorological environment warning characteristic quantity of each expressway section.

[0097] In this embodiment, the first influencing factor of the meteorological environment of each expressway section is obtained by the following analysis method: the estimated meteorological characteristic change curve of each expressway section at each location point is compared with the meteorological characteristic reference change curve stored in the cloud SQL end, and the total deviation curve length between the estimated meteorological characteristic change curve of each expressway section at each location point and the meteorological characteristic reference change curve is obtained, which is recorded as L i→g , and substitute it into the following formula:

[0098]

[0099] Among them, ω 2→i represents the second influencing factor of the meteorological environment of the expressway section i, and θ represents the meteorological environment impact assessment value per unit length of the preset deviation curve.

[0100] The estimated visibility change curve and the estimated wind speed change curve at each location point of each expressway section are statistically compiled, and the estimated minimum visibility, the estimated average visibility change rate, the estimated maximum wind speed and the estimated maximum wind speed change rate are extracted from them. The third influencing factor of the meteorological environment of each expressway section is obtained through processing.

[0101] In an embodiment, a machine learning model or a time series prediction model can be used to identify the pattern between the estimated visibility change curve and the estimated wind speed change curve of each highway section at each location point, and predict and analyze the future weather conditions of each highway section. It can also be combined with linear regression, decision tree, random forest, neural network and other algorithms for analysis to ultimately determine the third influencing factor of the meteorological environment of each highway section.

[0102] It should be understood that, in the present embodiment, the third influencing factor of the meteorological environment of each expressway section is a numerical result obtained by analyzing and processing the estimated visibility change curve and the estimated wind speed change curve of each expressway section at each location point, which is used to judge the degree of meteorological environmental impact of each expressway section, and is used as a basis for analyzing the meteorological environment warning characteristic quantities of each expressway section.

[0103] In this embodiment, the third influencing factor of the meteorological environment of each expressway section is obtained by the following analysis method:

[0104]

[0105] Among them, ω 3→i represents the third influencing factor of the meteorological environment of highway section i, N ig 、V ig They represent the estimated minimum visibility and the estimated average change rate of visibility at the g-th position of highway section i, respectively. ig 、f ig They respectively represent the estimated maximum wind speed and the estimated maximum wind speed change rate of the expressway section i at the g-th position point. ΔN, ΔV, ΔF, and Δf respectively represent the estimated minimum visibility threshold, the estimated average visibility change rate threshold, the estimated maximum wind speed threshold, and the estimated maximum wind speed change rate threshold stored on the cloud SQL side. R1 is the correction proportional coefficient of the preset visibility, and R2 is the correction proportional coefficient of the preset wind speed.

[0106] According to the first influencing factor of the meteorological environment, the second influencing factor of the meteorological environment and the third influencing factor of the meteorological environment of each expressway section, the meteorological environment early warning characteristic quantity of each expressway section is obtained.

[0107] Furthermore, the meteorological environment warning characteristic quantity of each expressway section is obtained by analyzing the meteorological environment forecast data of each expressway section, which is used to determine the degree of meteorological warning demand of each expressway section and serves as the data basis for screening risky expressway sections to implement meteorological environment warning prompt control.

[0108] In an embodiment, a warning model can be established to predict the future meteorological conditions of each highway section. At the same time, time series analysis, regression analysis and other methods are used to combine the predicted weather data and traffic data of each highway section to analyze the meteorological conditions that may lead to traffic accidents, and finally determine the meteorological environment warning characteristic quantities of each highway section.

[0109] Specifically, in this embodiment, the meteorological environment warning characteristic quantity of each expressway section is subject to the following specific execution conditions:

[0110]

[0111] In the formula, ω iThe meteorological environment warning characteristic quantity of the expressway section i, b1 is the weight of the first influencing factor of the preset meteorological environment, b2 is the weight of the second influencing factor of the preset meteorological environment, b3 is the weight of the third influencing factor of the preset meteorological environment, ω 1→i represents the first influencing factor of the meteorological environment of highway section i, ω 2→i represents the second influencing factor of the meteorological environment of highway section i, ω 3→i It represents the third influencing factor of the meteorological environment of the expressway section i, e is a natural constant, i is the number of each expressway section, i=1,2,3,...,p, and p is the number of expressway sections.

[0112] The forecast control management cloud platform is used to analyze the meteorological environment warning touch threshold value of each highway section according to the leading indicator factors of each highway section, and verify it with the meteorological environment warning characteristic quantity of each highway section, thereby selecting risky highway sections to implement meteorological environment warning prompt control.

[0113] The forecast control management cloud platform is a management platform based on cloud computing technology, which aims to integrate and process various meteorological data, highway traffic data and other related data, and provide meteorological forecasts, traffic forecasts, traffic control and other services to highway traffic management departments and related highway control agencies. According to the results of meteorological forecast and traffic forecast analysis, the forecast control management cloud platform can formulate traffic control measures and scheduling plans, including road condition prompts, speed limit measures, traffic signal optimization, etc., to respond to sudden weather and traffic incidents.

[0114] In an embodiment, risky highway sections are screened to perform meteorological environment early warning prompt control, specifically including: transmitting the meteorological environment early warning prompt information of the risky highway section to a receiving end of a road control center to which the risky highway section belongs, and also transmitting the early warning prompt information to a receiving end of a mobile vehicle that communicates with the highway section through the Internet of Vehicles. Currently, vehicles are equipped with on-board communication equipment, such as on-board communication modules or on-board communication terminals, which are used to communicate with the communication infrastructure of highways. Transmitting the early warning prompt information to the receiving end of the mobile vehicle can help the driver to prepare in advance, take corresponding driving measures, and further reduce the risk of traffic accidents caused by bad weather.

[0115] In a specific embodiment of the present invention, the meteorological environment warning touch threshold value of each expressway section is analyzed and verified with the meteorological environment warning characteristic quantity of each expressway section, thereby improving the accuracy of screening risky expressway sections, and can help to more effectively configure monitoring equipment, manpower, materials and other resources, improve the efficiency and response speed of expressways to cope with severe weather conditions, which not only ensures the pertinence and effectiveness of early warning control, but also helps to improve the overall safety of the transportation system.

[0116] Specifically, the analysis obtains the meteorological environment warning touch threshold value of each expressway section, and the specific process is: according to the leading indicator factor of each expressway section, the leading indicator factor reference value and the meteorological environment warning touch preset threshold value stored in the cloud SQL end are counted.

[0117] The leading indicator factor of each expressway section is verified with the leading indicator factor reference value. If the leading indicator factor of a certain expressway section is lower than or equal to the leading indicator factor reference value, the meteorological environment warning touch threshold value of the expressway section is defined as the meteorological environment warning touch preset threshold value.

[0118] If the advance indicator factor of a certain expressway section is higher than the advance indicator factor reference value, the expressway section is recorded as a forecast adjustment section, the difference between the advance indicator factor of the forecast adjustment section and the advance indicator factor reference value is extracted, marked as the touch threshold adjustment benchmark value, and the impairment factor corresponding to the touch threshold unit adjustment benchmark value stored on the cloud SQL side is counted, and the meteorological environment warning touch threshold adjustment value of the forecast adjustment section is obtained by processing, and the meteorological environment warning touch threshold value of the forecast adjustment section is defined as the meteorological environment warning touch threshold adjustment value.

[0119] Specifically, the meteorological environment warning touch threshold adjustment value of the forecast adjustment section is specifically processed as follows: τ=Y-(K*k0), wherein τ represents the meteorological environment warning touch threshold adjustment value of the forecast adjustment section, Y represents the meteorological environment warning touch preset threshold value, K represents the touch threshold adjustment reference value, and k0 represents the impairment factor corresponding to the touch threshold unit adjustment reference value.

[0120] In another embodiment, the meteorological environment warning touch threshold adjustment value of the forecast and adjustment section can not only be obtained by analyzing the above-mentioned numerical expression, but also by utilizing the relevant historical meteorological data and traffic accident data of each expressway section, and adopting data mining techniques, such as cluster analysis, association rule mining, etc., to analyze the occurrence of traffic accidents on expressway sections under different meteorological conditions, so as to determine the meteorological warning trigger thresholds of different expressway sections. It can also comprehensively consider different factors, such as meteorological conditions, traffic flow, section characteristics, etc., and determine the meteorological warning trigger thresholds of different expressway sections through expert evaluation or multi-index decision-making methods. Finally, the meteorological environment warning touch threshold adjustment values ​​of different expressway sections are obtained by comprehensive analysis, including the meteorological environment warning touch threshold adjustment values ​​of the forecast and adjustment sections involved in this embodiment.

[0121] The meteorological environment warning touch threshold values ​​of all expressway sections are counted and summarized to obtain the meteorological environment warning touch threshold value of each expressway section.

[0122] Specifically, the screening of risky highway sections to execute meteorological environment warning prompt control has the following specific process: according to the meteorological environment warning touch threshold value of each highway section, and statistics of the meteorological environment warning characteristic quantity of each highway section, through comparison, if the meteorological environment warning characteristic quantity of a certain highway section is higher than or equal to the meteorological environment warning touch threshold value of the highway section, then the highway section is marked as a risky highway section, thereby screening the risky highway sections to execute meteorological environment warning prompt control, and transmit the meteorological environment warning prompt information of the risky highway sections to the receiving end of the road control center to which the risky highway sections belong.

[0123] In this implementation, a cloud SQL terminal is also set up to store reference indicator data. The reference indicator data is collected and verified by relevant personnel during the development and testing phase, and the data is complete and highly reliable after review.

[0124] Specifically, the reference indicator data include: the total length of the slope section, the average slope of the slope section, the total number of curve sections, the average curvature of the curve, the characteristic evaluation factor per unit area of ​​the cumulative damaged area of ​​the road surface, and the characteristic evaluation factor corresponding to a single intersection. It also includes: reference to the estimated cumulative precipitation, reference to the estimated cumulative duration of precipitation, reference to the estimated average precipitation rate, reference change curve of meteorological characteristics, estimated minimum visibility threshold, estimated average visibility change rate threshold, estimated maximum wind speed threshold, estimated wind speed maximum change rate threshold, reference value of leading indicator factor, meteorological environment warning touch preset threshold value and damage reduction factor corresponding to the touch threshold unit adjustment benchmark value.

[0125] In a specific embodiment of the present invention, by providing a meteorological environment forecast control system for a highway, according to the specific characteristics of each highway section, and analyzing the meteorological environment warning touch threshold value of each highway section, the disadvantages caused by the current lack of attention to the characteristics of the highway itself are overcome. By analyzing the specific characteristics of each highway section, the corresponding meteorological environment warning touch threshold value can be set for each highway section, thereby improving the accuracy and timeliness of meteorological warnings for highways, and providing reliable guarantees for the response efficiency of traffic management departments and the accuracy of meteorological control.

[0126] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0127] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A highway meteorological environment forecast control system, It is characterized in that include: The section advance data import module is used to divide the expressway into various expressway sections, collect and count the advance data of each expressway section, and import it into the weather forecast central processor synchronously; The weather forecast central processor is used to respond to and receive the advance data of each highway section, obtain the advance indicator factors of each highway section through convergence fitting processing, and transmit them to the forecast control management cloud platform; The meteorological environment forecast data collection and analysis module is used to collect the meteorological environment forecast data of each expressway section, analyze and obtain the meteorological environment warning characteristic quantity of each expressway section, and synchronously transmit it to the forecast control management cloud platform; The forecast control management cloud platform is used to analyze the meteorological environment warning touch threshold value of each highway section according to the leading indicator factors of each highway section, and verify it with the meteorological environment warning characteristic quantity of each highway section, thereby selecting risky highway sections to implement meteorological environment warning prompt control; The preceding indicator factors of each expressway section are obtained by convergent fitting processing, and the specific process is as follows: According to the advance data of each expressway section, the road structure characteristic value and the traffic characteristic value of each expressway section are processed in sequence, and the advance indicator factor of each expressway section is obtained by convergence fitting processing. The advance indicator factor of each expressway section is obtained by analyzing and processing the advance data of each expressway section, and is used to determine the potential risk level of traffic in each expressway section, and serves as a numerical analysis basis for the meteorological environment warning touch threshold value of each expressway section; The specific execution conditions and constraints of the preceding indicator factors of each expressway section are as follows: In the formula, represents the leading indicator factor of expressway section i, σ1→i represents the road structure characteristic value of expressway section i, σ2→i represents the traffic characteristic value of expressway section i, i is the number of each expressway section, i=1,2,3,...,p, p is the number of expressway sections, a1 is the weight of the preset road structure characteristic value, a2 is the weight of the preset traffic characteristic value, and e is a natural constant.

2. A highway weather environment forecast control system according to claim 1, Features: The statistics of advance data of each expressway section specifically include: The laser scanner built into the inspection drone is used to scan and construct the surveying and mapping model of each highway section; According to the surveying and mapping model of each expressway section, the central axis of each expressway section is extracted, and a number of survey points are arranged in an evenly spaced manner to obtain each central axis survey point, and the elevation value of each central axis survey point of each expressway section is counted and marked as feature sub-element 1; According to the preset contour intervals, the surveying and mapping models of each expressway section are laid out with contour lines, and the total area occupied by each expressway section within each contour interval is extracted and marked as feature sub-element 2; The total length of the slope section, the average slope of the slope section, the total number of curve sections, the average curvature of the curve, the cumulative damaged area of ​​the road surface, and the total number of intersections of each highway section are statistically obtained from the surveying and mapping model of each highway section, and are combined as characteristic sub-elements 3; The average daily traffic volume, average daily traffic density, total number of traffic incidents, and average vehicle speed of each highway section are collected from the highway edge server and jointly marked as feature sub-element 4; The characteristic sub-elements 1, 2, 3, and 4 of each expressway section are counted and combined as the advance data of each expressway section.

3. A highway weather environment forecast control system according to claim 1, Features: The meteorological environment forecast data collected for each expressway section specifically includes: Each expressway section is deployed with a set number to obtain each location point, the current time is used as the starting point for segmentation, and the forecast period is obtained by dividing it with a preset time period length, and the meteorological environment forecast data of each expressway section is collected and counted. The meteorological environment forecast data is the meteorological data summarized during the forecast period at each location point, wherein the meteorological data summarized during the forecast period includes the estimated cumulative precipitation, the estimated cumulative duration of precipitation, the estimated average precipitation rate, the estimated temperature change curve, the estimated humidity change curve, the estimated visibility change curve and the estimated wind speed change curve.

4. A highway weather environment forecast control system according to claim 3, Features: The analysis obtains the meteorological environment warning characteristic quantity of each expressway section, and the specific analysis process includes: According to the meteorological environment forecast data of each expressway section, the estimated cumulative precipitation, estimated cumulative duration of precipitation, and estimated average precipitation rate at each location are counted, and the first influencing factor of the meteorological environment of each expressway section is obtained by processing; The estimated temperature change curve and the estimated humidity change curve of each expressway section at each location point are statistically calculated, and the estimated temperature change curve and the estimated humidity change curve are fitted by a preset curve fitting algorithm to obtain the estimated meteorological characteristic change curve of each expressway section at each location point, and the second influencing factor of the meteorological environment of each expressway section is obtained by processing; The estimated visibility change curve and the estimated wind speed change curve at each location point of each expressway section are statistically analyzed, and the estimated minimum visibility, the estimated average visibility change rate, the estimated maximum wind speed and the estimated maximum wind speed change rate are extracted from them, and the third influencing factor of the meteorological environment of each expressway section is obtained by processing; According to the first influencing factor of the meteorological environment, the second influencing factor of the meteorological environment and the third influencing factor of the meteorological environment of each expressway section, the meteorological environment early warning characteristic quantity of each expressway section is obtained.

5. A highway weather environment forecast control system according to claim 1, Features: The meteorological environment warning characteristic quantities of each expressway section are obtained by analyzing the meteorological environment forecast data of each expressway section, and are used to determine the degree of meteorological warning demand for each expressway section, and serve as the data basis for screening risky expressway sections to implement meteorological environment warning prompt control.

6. A highway weather environment forecast control system according to claim 1, Features: The analysis obtains the meteorological environment warning touch threshold value of each expressway section, and the specific process is as follows: According to the leading indicator factors of each expressway section, the reference values ​​of leading indicator factors and the preset threshold values ​​of meteorological environment warning touch screen stored in the cloud SQL are counted; The leading indicator factor of each expressway section is verified with the leading indicator factor reference value. If the leading indicator factor of a certain expressway section is lower than or equal to the leading indicator factor reference value, the meteorological environment warning touch control threshold value of the expressway section is defined as the meteorological environment warning touch control preset threshold value; If the leading indicator factor of a certain expressway section is higher than the leading indicator factor reference value, the expressway section is recorded as a forecast adjustment section, the difference between the leading indicator factor of the forecast adjustment section and the leading indicator factor reference value is extracted, marked as the touch threshold adjustment reference value, and the impairment factor corresponding to the touch threshold unit adjustment reference value stored in the cloud SQL end is counted, and the meteorological environment warning touch threshold adjustment value of the forecast adjustment section is obtained by processing, and the meteorological environment warning touch threshold adjustment value of the forecast adjustment section is defined as the meteorological environment warning touch threshold adjustment value; The meteorological environment warning touch threshold values ​​of each expressway section are summarized.

7. A highway weather environment forecast control system according to claim 1, Features: The screening risk expressway sections are implemented with meteorological environment early warning prompt control, and the specific process is as follows: According to the meteorological environment warning touch threshold value of each expressway section, and the meteorological environment warning characteristic quantity of each expressway section is counted, through comparison, if the meteorological environment warning characteristic quantity of a certain expressway section is higher than or equal to the meteorological environment warning touch threshold value of the expressway section, then the expressway section will be marked as a risky expressway section, thereby selecting the risky expressway sections to implement meteorological environment warning prompt control, and transmit the meteorological environment warning prompt information of the risky expressway sections to the receiving end of the road control center to which the risky expressway sections belong.

8. A highway weather environment forecast control system according to claim 4, Features: The specific execution conditions and constraints of the meteorological environment warning characteristic quantities of each expressway section are as follows: Where ωi is the meteorological environment warning characteristic quantity of highway section i, b1 is the weight of the preset first influencing factor of the meteorological environment, b2 is the weight of the preset second influencing factor of the meteorological environment, b3 is the weight of the preset third influencing factor of the meteorological environment, and ω1→i represents the highway section i The first influencing factor of meteorological environment, ω2→i represents the high-speed section i The second influencing factor of meteorological environment, ω3→i represents the high-speed section i The third influencing factor of the meteorological environment, e is a natural constant, i is the number of each expressway section, i=1,2,3,...,p, p is the number of expressway sections.

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